be-5638 : assist : automatic system for surface inspection

SYNTHESIS REPORT
FOR PUBLICATION
CONTRACT No:
BRE2-CT9241260
PROJECT N“:
TITLE:
ASSIST
(Automatic System for Surface Inspection and Sorting of Tiles)
PROJECT
20-ORDINATOR:
Bull HN Italia
PARTNERS:
I-T One Tile
Thomson B
University of Surrey - DEEE
University di Geneva - DIBE
Thomson TCS [Associated to Thomson B)
STARTING DATE:
PROJECT FUNDED BY THE EUROPEAN
COMMUNITY UNDER THE BRITEIEURAM
PROGRAMME
DURATION: 43 months
ASSIST: Automatic System for Surface Inspection
and Sorting of Tiles
Report Coordinators: VSSP
Department of Electronic and Electrical Engineering
University of Surrey
Guildford GU2 5XH, United Kingdom
1. H’UTRODUCTION
The ceramic tiles industrial sector has taken significant advantage of the advances in the world of automation in recent
years. Ail production phases have been addressed through various technical innovations, with the exception of the final
stage of the manufacturing process, namely the product hspecfion. This is still performed manually and is concerned
with the sorting of tiles into distinct categories or the rejection of the tiles found with defects and pattern faults.
The research effort expended upon the problem of objectively inspecting, analysing and characterizing ceramic tiles is
easily justified by the commercial and safety benefits to the industry:
I
■ automation of a currently obsolete and subjective manual inspection procedure
8 100% product inspection
■ more robust and less costly inspetilon
m higher homogeneity within sorted classes of products
■ increased processing stability and improved overall production performances through the removal of a
major bottleneck
continuation and consolidation of the leadership currently enjoyed by the European Community in this area
[n this paper we describe the integrated system developed under the ASSIST project, for the detection of defects on
colour tiles and for the colour grading of defect-free tiles. In Section 2 an overview of the sensor developed specifically
for this project is presented. In Section 3 the algorithms developed for the detection of the defects will be described,
while the coiour grading framework is discussed in Sectkm 4. In Section 5 the various types of defects are quantified
geometrically and statistically. In Section 6 the system integration is presented, and finally in Section 7 the performance
evaluation of the system is discussed.
2. THE SYSTEM SENSORS
To meet the ASSIST demands, a multi-camera vision approach has been developed in order to cover many different
kinds of defects. The acquisition system which has been designed for the ASSIST project, is mainly made up of three
CCD-based cameras.
2.1 Colour CCD image sensor
The first camera designed and developed for the project is a colour camera (TH7821 C), based on a high resolution line
array device sensor. This is a trilinear array with up to 3 x 8640 pixets, featuring very high performance in terms of
linearity, dynamic range, and low noise response. It uses high quality on-chip colour filters to obtain full colour
information from a single chip. The following topics highlight the electro-optical performances that make this sensor the
ideal device to satisfy the ASSkST demands.
Signal to noise ratio
The TH7821 C image sensor was especially designed to provide very high saturation output voltage (3 V) in spite of its
small pixel size (7x7 micron). By measuring temporal noise in darkness based on correlated double sampling, an
analogue signal processing technique employed allows the suppression of the reset noise and the reduction of low
frequency noise contributions. The F\MS value corresponds to 32 electrons, mainly contributed by the sensor output
amplifier. The high saturation voltage and very low temporal noise make the TH7821 C a fri-CCIY colour image sensor
with the smallest pixel size and hig’best dynamic range in the world, featuring 15000 (83 dB) signal to noise ratio
(relative to RMS noise in darkness).
Charge transfer efficiency
The typicai value of the total charge transfer efficiency is about 0.98 and the charge transfer inefficiency deduced for
one CCD output register stage is equal to 5x1 C@. These results indicate a very good charge transfer for 6 cm iength
(XD shift registers.
Lag
This important parameter corresponds to the charge transfer efficiency from the pixel storage region to the CCD shift
register. For a colour image sensor, lag effects induce error in colour detection, together with reduced resolution in the
scanning direction. To obtain a good colour restitution and therefore to avoid false colour phenomena, a new pixel
technology was introduced in the TH”7821 C image sensor to minimise the lag effect. The lag effect was measured for an
average signal from 50 mV to 600 lmV and the results obtained for this tricolour CCD image sensor show excellent
transfer efficiency {cO.3%) as compared with a conventional linear CCD image sensor (>5%).
Responsivity
A linear image sensor with photodiodes and 100% pixel aperture features high quantum efficiency providing a maximum
sensitivity with an improved responsivity at 400 nm (blue band). [n order to further improve this sensor, new colour filters
with high transmission and low crosstalk have been developed. The primary ccdour filter transmission curves were
optimised for colour restitution and the crosstalk between Blue/Green and Green/Red were reduced to suppress coiour
mistakes due to colour mixing.
Linearity
Linearity is mainly dictated by the output amplifier characteristics. For a dynamic range of 20mV to 2500mV the linearity
deviation is less than 2%. This value is better than 0.5% from 20mV to 500mV which fully corresponds to the ASSIST
needs. These very good [inearities are required for RGB channels to ensure perfect colour restitution, allowing then
good defect recognition in order to perform reliable colour shade grading of ceramic tiles.
2.2 CoIour line-scan camera,
.
?
~
#
a
!
s
The colour line-scan camera is used for the high precision colour grading of the tiles. It is fitted with the integrated
3 X 8640 element-per-line, colour (2CD image sensor described in Section 2.1. It provides three -bit digitised” colour
outputs, one for each of the RGB channels. The circuitry within the camera comprises logic and driver controls of the
CCD image sensor, the sensor itsellf, video preprocessing and processing blocks, the analog to digital converters and
the line memories. An infra-red reject optical filter and a lens mounting adaptor are included in the enclosure.
To achieve fast scanning it includes a ve~ high resolution trilinear architecture array sensor and functionalities for the
remote control of the operational parameters such as gain and black level adjustments of each RGB video channel.
These parameters will be tuned by the computer platform depending on the light conditions detected and the colour of
the tiles. Since each colour has different sensitivity and responsivity characteristics, it is necessa~ to tune each colour
in the camera for the maximum dynamic range of all colours for a given light source or coloured tile.
The trilinear CCD image sensor ow’ttains 3 coloured pixel arrays (red, green, and blue) which are implemented in
parallel. Each line array is separated from the next by a space equal to 42 lines (294 micron). This separation between
RGB lines shows that at a given time, each pixel array does not capture the same image. A digital system mainly made
up of FIFO memories and EPLD devices to controt read and write FIFO operations has been designed for the automatic
restoration of the image geometry starting from 3 image lines acquired in different spatial positions. Three FIFO banks
are acthg as digital delay lines to store respectively 15 red lines, 8 green lines and only 1 blue line, assuming that the
red line is captured first. Once these lines’ have been stored, the 3 memory banks corresponding to each colour are
simultaneously read, providing 3 superposed images.
8
‘it
I
2.3 BNV cameras
There are two Black and White cameras used in the system and they are both fitted with linear CCD arrays. Three
boards are currently used in these cameras: a sensor driver board, a timing generator board, and a digital video
3
processing board. Each key component such as the sampling circuit to improve the CCD signal to noise ratio, the low
noise video amplifier, the high speed drivers and the fiexible programmable logic devices have been carefidly tested
and hence selected to improve and to exploit CCD performances.
A major concern is the pairing effect which is caused by signal difference (gain and DC level misalignment) between
odd and even CCD outputs. This produces a kind of fixed pattern noise at half the pixel frequency. In order to cancel
this effect, the two CCD channels have been carefully matched from dark to white level so that the pairing effect is less
than 1%.
In order to minimise transfer inefficiency and a dark signal component that increases with increasing temperature, the
drivers and the trilinear colour CCD need to be thermally coupled to the camera front end. For each 80C step, the dark
current doubles, reducing the signal to noise ratio. High performance silicon grease ensures an ef[cient thermal
exchange between CCD as well as CCD drivers and the camera front end fitted with the lens mounting system.
3. TILE DEFECT DETECTION
The expected result of a defect detection module is the identification of defective regions (defect localisation) and the
quantification of defect parameters (shape, extension, etc.) to be used for further classification purposes, Depending on
the number of defects and their dimensions, the tiles are grouped into:
m First Class (none or very few acceptable defects)
z Second Class (few but still acceptable defects)
M Waste (unacceptable defects)
I
u
1“
Some of the most common and anti-aesthetic defects found on both dain and textured tiles can be cateaorised as
cracks, bumps, depressions, pin-holes, dirt, drops, undulations, and colour and texture defects. These are pr&ented in
more detail in Table 1.
: :%Efissuresandwts tenthsofmm osomecms :
E!==== ‘ ’
depressions
holes
dirt
drops
water drops
undulations
‘
F;y :fiousm o
~
‘ ’
’
reliefs of the glazed surface with few tenths to some mms
respect to the planar surface of tile
general depression of glazed surface radius usually <5mm
with a circular shape
pin-tips, small bubbles, holes, craters
minimum 0.25mm
dust particles or dried glaze residuals few tenths to some mms
drops during glaze app[i@ion
few tenths to very low mms
irregular shapes (condensation drops) few mms to cms
longitudinal bars, rills and imperfect widths and lengths of few cms
’ u
rs
blobs discemib[e from regular texture
few tenths of mm to cms
few tenths of mm to cms
Table 1: Typical Ceramic Tile Defects
.,
We now provide an overview of the most innovative approaches developed by the consortium for detecting different
types of defects in tile images.
#
3.1 Inspection of the edges of the tile
The border area of a tile is a critical area for physical defects since it is the most fragile. The inspection of tile borders,
however, is particularly difficult because, due to the geometry of both material and acquisition systems, the image of the
tile border suffers from the presence of shadows, reflectance, and discontinuities.
4
The problem was tacklecf in two ways; initially, by adopting appropriate sensing and lighting techniques to reduce as
much as possible the acquisition problems (shading, reflections and non-uniform illumination), and subsequently by preprocessing the border area to produce a set of data compatible with the techniques used for the inner zone. This last
operation was performed by using an algorithm made up of three steps:
1. Identification of the extericr border of the tile
2. Compensation of the luminance variation in the border area
3. Extension of the image by a pre-defined number of image lines (mirroring).
3.2 Crack detection in uniformiy-coloured tiles
Cracks are linear structures contrasting with the rest of the tile. Our method [1] consists of two 1 D convolutions, in the
horizontal and vertical directions respectively with line detection filters [2]. Local maxima in the output indicate the
possible presence of a line, and trigger the hypothesis that a line is present. The shape of the output signal around a
local maximum is compared with the expected shape if a line were present in order to confirm or reject the hypothesis.
3.3 Spot-like defect detection in uniformly-co[oured tiles
On light-coloured plain tiles, small, :;pot-like faults are of reasonably high contrast against the background. However,
due to various sources of noise, e.g. non-uniform illumination, a simple threshold will not serve as an adequate solution
to their detetilon. Thus, an adaptaticm of the line filter method from Section 3.2 was developed for spot-like defects [1].
The only difference is that the tile image is convolved with only one filter which is optimised for spot profiles. The spot
peaks thus enhanced are extracted by thresholding. Figure 1 shows a plain and a lightly-textured tile with detected
defects.
●
8
1
Figure 1: A plain white tile and a lightly-textured tile with small crack and spot defects
3.4 Crack detection on textured tiles: A quick solution
Morphological techniques were used for the identification of cracks [3,4]. In particular, we used the difference between a
Closing and an Opening for the identification of thin structures. On the basis of these operations, a criterion called
Anomaly Presence Degree (APD) has been proposed for the identification of the defective parts of the image. During
the anaiysis phase the image is subdivided in blocks of dimension L x L, and for each block the APD parameter is
5
evaluated and compared with a threshold: if the threshold is exceeded, the block is classified as anomalous, otherwise it
is considered regular. The goal in an adaptation algorithm used, if applied to a normal image used for training, is to
minimise the maximum APD value present in the image.
3.5 Crack detection on textured tiles: A more sophisticated approach
We use the cojoint spatial and spatial frequency representation of the Wigner Distribution [1,5,6] to enhance pattern
separability as the crack and texture patterns have disjoint support regions in the cojoirrt representation. According to
this method, at each pixel position (x,y) we calculate the Fourier transform of a non-linear combination of pixel values
within a window of size N x N centred at pixel (x,y).
I e
The Wigner distribution is a real function and its components constitute the feature vector at each pixel position. All local
Wtgner spectral components are normalised by their corresponding dc component, so that only the general shape
characteristics of each spectrum are captured. These components form the features that describe the texture pattern.
The statistical distribution of these features is computed from the defect-free image during an off-line training phase. In
the testing stage, the Mahaianobis distance of the feature vector of each pixel from this distribution is calculated. The
values of this distance are used to form a residual map image. This image is subsequently processed by the optima!
linear filter mentioned in Section 3.2 to detect the cracks. Figure 2 shows the app]i~~on of our approach to !&@ner
distribution for the detection of (synfheticaliy produced) cracks_on highly textured tiles
—
)
Figure 2: Rossini textured tile with short crack defect and Donizetti textured tile with long crack defect
3.6 Blob-detection: a quick approach
The image is subdivided into square blocks which are classified on the basis of their rank functions or morphological
properties. The generalised rank function provides exactiy the same information as the histogram but has the
advantage of making it possible to define an efficient distance measure that complies with the three following
requirements: {~ to be zero if and oniy if the two histograms are exactly equal; (ii) to be propotilonal to the distotilon
both for the concentration and for the values; (iio to weight the point differences in relation to their distances from the
mean value.
If a uniform texture is partitioned into sufficiently large blocks, each block is representative of the whole texture: the
problem is then to define a prototype of the texture from the analysis of the block histograms. By using the rank
distances, our strategy is to define as a prototype the rank funtilon computed on the block, and when testing an
unknown texture to evaluate the distance between the rank function of each block and the prototype ~6,7,8J.
6
3.7 Blob-detection: a more sophisticated approach
Using a perfect tile during the training stage, the various colour categories present in the defect-free tile can be
identified with the aid of K-means clustering in RGB space. The number of these clusters is chosen to be high so that
over-segmentation into chromatic classes is obtained, thus minimizing (and eliminating) the under-segregation error.
Next, these clusters are transformed into CIE-LUV unifoml colour space for perceptual merging, i.e. merging of small
clusters into super-clusters. Thus, the image is segregated into chromatic categories which are perceptually uniform.
The image can then be split into a stack of binary images one for each chromatic category. We perform morphological
smoothing on each binary image to remove noise before characterizing the structure of the left-over blobs. For each
blob we compute as structural features its area, perimeter fractality, elongatedness, and some spatial information about
the distribution of other blobs around it, and model the distribution of these attributes.
During testing, the image pixels are classified into the chromatic categories defined during the training stage using the
nearest neighbour rule. Any unclassified pixels are rejected and considered as colour defects. The accepted pixels are
used to form the stack of binary blob images again. The structural features of each resulting blob are then computed
and any blob-like texture defects are identified by means of the Mahalanobis distance function using the structure
statistics saved in the training phase. Figure 3 shows the application of our technique to a real defect in a Rlain tile and a
simulated defect on a highly textured tile.
e
P
Figure 3: Plain white tile with large water drop defect and Puccini textured tile with blob defect
3.8 Regular Pattern Fault Detection
The research activity carried out in the field of regular tile pattern analysis was mainly concerned with two problems:
first, the registration of the acquired data (i.e., the digitised image of the tile to be analysed) with a template (target
pattern); second, the identification of faults (relevant differences between actual and target patterns). The analysis has
to be performed in full colour, due to the possibility of errors in colour layer superposition.
Due to the very high computational requirements of such analysis it was decided to reduce the resolution of the sensor
used for performing the acquisition of these tiles: an RGB coiour camera at 24 bpp with resolution 1024xI 024 pixeis has
been judged suficient to achieve a good quaiity evaluation for tiles up to the maximum standard format.
As it was verified that non-negligible shifts and rotations can happen among different layers of the same inspected tiie
(generated by successive applications of siik-screen coiour drawings) it resuited that a reliabie registration of inspected
tiies with the prototype (tempiate) can oniy be obtained if the matching is performed for each iayer separately. Such
iocai shifts can in fact produce apparent errors (imperfect superposition between current and reference tile) which have
7
not to be detected as real flaws. Therefore, a layer splitthg strategy was studied (based on K-Means clustering
approach) and further analysis was performed only at the layer level.
The registration algorithm developed takes into account two primary objectives: to maintain the distance between
template and inspected pattern within one pixel accuracy and to minimise the computational requirements. The solution
adopted consists of the use of a fast roto-translation procedure, that allows a fast optimisation of the angle and shift
parameters.
The mean absolute difference between corresponding pixels was selected as a measure of correct pattern reproduction,
because it showed the best trade-off between speed and reliability.
Testing results showed that defects with total dimension as low as 3 pixel (at a resolution of 1024x 1024 pixel per tile)
can be successfully detected by applying the proposed strategy.
4, COLOUR SHADE GRADING
The scientific advances of the method developed consist of:
■ the development of an automatic scheme that can cope with spatial and temporal variability of illumination
■ the development of methodology that allows the replacement of the spectral blurring of a colour, caused by
the electronic sensor, by the spectral blurring caused by the human colour sensory system.
-
The developed methodology allows the identification of coiour grades that correspond to the threshold of human colour
perception which are discriminated by human inspectors wcrldng at the peak of their performance. To achieve this, our
methodology had to be able to measure colour differences at least one order of magnitude smaller than the various
types of noise involved in the process of colour recording.
I
‘8
4.1 Spatial and temporal illumination correction
The spatial variation of the illumination manifests itself as a low frequency variation over the profile of the tile. Assuming
for the moment that the inspected tile is of uniform colour, we fit the grey level function of the tile in a single colour band
with a low order polynomial using the least squares error method. This polynomial fitting is equivalent to identifying the
low frequency component of the function. At each location then, the actual value of the grey level function is divided by
the corresponding value of the fitted polynomial and multiplied with some reference value. This way, the low frequency
variation due to variable illumination is removed and all values are referred to the reference illumination value chosen.
This process is repeated for all colour bands separately. Thus, the multiplicative noise due to the spatial variation of the
illumination is taken care of.
n
To take care of the temporal variation of the illumination, we have to image each tile with the same reference surface,
another tile which plays the role of a pallet. When the system is operatio~al, the difference in the observed variation of
grey value that can be attributed to the temporal illumination variation is removed with the hehI of the reference surface
and only the difference that remains is re”corded as genuine variation. In other words, all ‘the ~ecorded signals are
referred to the same reference signai recorded at a specific time [9,1 O].
After the above corrections take place, the tiles are graded by comparing their colour histograms with a reference
histogram and using the correlation coefficient as a measure of similarity [1 1,12,13].
4.2 Using the results of Psychophysics research
All the above described methodology was applied to several series of ceramic tiles which had been colour graded by
human inspectors. In most of the cases the tiles could be clustered in clusters identical to the cfusters the human
experts had clustered them into. In some cases, however, there was confusion and disagreement with the human
classification. That disagreement was removed when the difference in the electronic and the human sensor response
was taken into consideration in the way described below.
The visible part of the e[ectro-magnetic spectrum was discretised and represented by the values at 31 equidistant
wavelengths. The sensitivity of each colour sensor and of the cones of the human retina were thus expressed by
vectors of length 31. The spectrum of the illuminating source was also a similar known vector. The intrinsic reflectance
8
of the inspected sutface was expressed by an unknown vector of the same length. As we only know the recordings of
the three colour sensors, we have an under-determined system of equations which cannot be solved for the intrinsic
reflectance values, as we have 28 degrees of freedom. We make the following assumption: The transformation between
the three recorded intensities by the electronic sensors and the three intensities the human sensors would have had
recorded, is affine. We created a large number of corresponding triplets of recordings using a Monte Carlo approach.
We then solved in the least square error sense to identify the e[ements of the affine transformation between the two sets
of recordings. This transformation can be used to predict what the human sensor would have recorded, given what the
electronic sensors have recorded. Then we transform the data into L.UV coordinates, where we know that the Euclidean
distance between any two points in this space is proportional to the perceived colour difference behveen the two colours
represented by these two points.
When the above process was applied to several series of tiles graded by human observers, almost total agreement
behiveen the clusters created by the human observem and the computer was achieved [14,15].
When one deals with finely textured surfaces, however, the dominant effect is not so much the difference in spectral
blurring between the eye and the sensor, as the dlferenf pok?f spread function of the electronic sensor and the eye. The
latest Psychophysical experiments have shown that humans perceive texture in a different way for different colours and
there are blurring masks that have been derived that quantify the way we biur colour textures viewed from different
distances. To take this effect into account when grading crdour textures, we first measured the spatiai blurring of our
sensor for each of the three RGB channels, and performed a Wiener filtering method to restore each chancel. We then
converted the coiour image into a set of new colours, called opponent colours where we applied the spatial blurring
performed by the human eye at the distance of 40cm. The corrected tiles were then graded by comparing their colour
histograms again. This time, however, the separation of ‘the grades the humans had defined was much better, and
where some clusters were overlapping before, we had no overlapping grades this time at all [16, 1~.
5. DEFECT QUANTIFICATION
Defect quantification concerns the extraction of numerical (quantitative) information from the defetifve reaion identified.
in order’ to achieve a better knowledge of the defect cttaracteristi~’. The output data are used both” for statistica~
purposes and for allowing a feed-back on the production process.
8
The computation of numerical parameters on the defects required the definition and algorithmic implementation of a
number of measures on the geometry and content of the flaws. A number of image processing algorithms were selected
taking into account the strong real-time requirements.
A list of the different defect types was defined, discriminating them on the basis of their visual characteristics.
Consequently, a [ist of the more significant parameters was formulated, by taking into account the requirements of the
following defect classification subsystem. Two main classes have been defined: defects representing a thin structure
and defects representing a circular structure.
For each of these categories, several geometrical and statistical measures have been proposed, listed in Table 2.
Defect
Thin Structures
Other defects
Geometry
total length
number of branches
centroid
diretilon
[ineatity
area
centroid
perimeter length
minimum bounding rect.
shape
eccentricity
aspect ratio
moments
Statistks
mean value
standard deviation
variance of the length of branches
mean value
variance
light variations
optical centroid
Table 2: Geometrical and Statistical measures for defects
I
Specific algorithms have been designed and implemented in software to compute the great part of the above mentioned
measures, working on hi-level patterns simulating real defects after segmentation. The eticiency and the computational
complexity of these algorithms have been tested and some improvements carried out in order to achieve peak
performances. The calculation of such parameters required the development and optimisation of several computer
graphics algorithms like: filling, thinning, skeletonization, oontour following, finite-state automatas, and so on.
6. SYSTEM INTEGRATION
The integrated vision system is schematically presented in Figure 4. The tiles are first inspected for bumps and
depressions using a laminated light source at an angle for maximum sensitivity to surface imperfections. This means
that each line across the tile reeeives more or less light of the same possible intensity. Nexl, the tile is inspected for
other structural faults like cracks and holes, using diffuse lighting. Finally, the tile is inspected for colour defects and
colour grading using the tri-linear colour scanner and again, diffuse lighting.
~
I
........
1
Figure 4: CMeralt ASSIST system structure
The system architecture (Figure 5) has been designed according to a modular approach. Two main functional modules
have been identified: the Host !vlodule and the Data Acquisition and Processing Module (DAPM).
Selection control
color ~
camera
~
b~w 1024~
camera
blw3100
camera
Host Module
J
Data
AcquMtion
Ramote
Host Module
~
Local
and
Processing
Module
Host
Module
~
4
/
1
$
5
VME Bus
●
m
Ethernet LAN
Figure 5: Overall ASSIST system architecture
The DAPM Module has been designed to contro[ image grabbing, to process the image, to classify tiles and to drive the
proper 1/0 module for tile sorting. The Host Module is in charge of the interface with the human operator, output data
collection and defect diagnosis. This module is split into a Local l-lost Module dedicated to provide 110 funtilonality (file
access, screen and keyboard functions, etc.) to the image acquisition and processing sub-system and into a Remote
Host Modu}e providing the graphical man-machine interface (MMI) and high-level data processing functionality (storing,
statistics, and diagnosis).
The DAPM Module and the Local Host Module are mapped onto the same machine, a rugged VME-bus machine with
embedded PC able to operate in industrial environments, while the Remote Host Module is implemented on a standard
PC. The DAPM and Local Host communication is based on VME bus while a IAN interface (Ethernet &pe) is chosen for
inter-machine communications.
This architecture, while allowing the use of rugged and costly machine only where needed, has the great advantage of
being easily expandable : the Remote Host could control and collect data coming from more than one Local machine.
6,1 Hardware ”Architecture
The system has been realised using two machines, a “local” one placed near the sorting line and a “remote” one that
controls operations and collects outputs. While a standard PC is used for the remote machine, both standard and
special hardware are used for the local one, A VME cabinet has been chosen to host both the Local Host, a standard
PC, and the DAPM module, implemented using both off-the-shelf and specially designed boards.
The DAPM module can be divided into three wmponents: Camera Control Modules, Camera Adapter Modules, and the
DSP Network. Camera Control has been designed to receive synchronisation signals coming from an enmder and
photocells and to generate line scar! camera timing (exposure time, pixel c?ock, etc.) white the Camera Adapter is
needed to directly interface camera outputs to 13SP ports.
Due to the high input data rate and to the high processing power required, the processing sub-module uses a network of
high performance Digital Signal Processors based on the Texas TM S320C40 family.
6.2 Software Architecture
A multi-tasking operating system has been chosen both for the Local Host and for the Remote one: Microsoft Windows
for Workgroup has the features needed for multiprocessing and IAN communication requirements.
An i10 Server daemcm has been implemented into the Locai Host in order to provide the DAPM moduie with ito
functionality. The Remote Host is made up of the foilowing moduies: Supervisor Module, Output Data Collector Module,
Statistics Module, Diagnostic Moduie. The DAPM Module is based on a DSP network ; a quick parallel kemei was
chosen in order to satisfy real-time requirements. This kernel was then extended by developing its routing and
distributed fiie system functionality. Message Router is in charge of routing message packets among the processors of
the network while Link File System provides a mechanism for data exchange in the form of standard fiie operations
(open, read, write, etc.) regardless of processes allocation.
s
7. PERFORMANCE EVALUATION
The integrated system was extensively evaluated on the factory floor, mainiy with respect to defect detection. in
particular, the quick methods used for defect detection were extensively tested with respect to two major kinds of tiles:
uniformiy coloured tiies and textured tiles.
Performance is quite iike that of human operators in the case of uniformiy coloured tiles, as Figure 6(a) shows. It must
be noted that the main difference in the resuits between the prototype and human classifications is due to defects
beyond the ASSIST specifications (i.e. smalier size fauits than originally specified by the manufacturer), or due to minor
problems (illumination or thresholds settings) which were iater solved.
tom
tm
!ELl
(a)
r
-!
i
a
i -—
(b)
Figure 6: ASSIST vs. Human Accuracy in (a) Helsinki tiles (a white plain tile) and (b) Ecate tiles (a grey textured tile)
t
Textured tiles include a great variety of products, with different technologies and aspects . The main kinds are flamed
tiles, randomly textured tiles (i.e. dropped tiles), and pseud~randomly textured tiles (i.e. marble like tiles). While flamed
tiles (tiles with shadings caused by flame exposure) and dropped tiles [tiles coloured by drops, granite like) have been
successfully tested, marble like tiles were beycmd the limits of effectiveness of the quick approaches. More preciseiy,
defects caused by problems in glazing by means of silk-screen rolls, proved very difficult to be detected. Figure 6(b)
shows the comparison between automatic and human classification in the case of a marble-like textured product.
As a final evaluation, we can say that the quick methods developed are quite effective in defect detection in uniformly
ccdoured tiles and in some kinds of textured tiles whereas they are less reliable in pseudo-random textures.
8. SUMMARY Abll) CONCLUSIONS
We described here an integrated system for the inspection and colour grading of ceramic tiles. Although not all of the
methods proposed are feasibie for real-time implementation with the existing technology, it is beiieved that perhaps in 5
years time real-time implementation wiii be possible.
it is hoped that the considerable advance achieved in overali production through the automation of ceramic tiie
inspection wiii eliminate an estimated 70-80%customer complaint rate regarding product quaiity. Furthermore, the spinoffs of the findings of this project can have an impact in other industrial fields presenting simiiar problems; for instance in
the textde industry for defect detection, [oose threads detection, and colour shading classification on fabrics, the agro-
12
food industry for visual analysis of crops such as apples/orangeslpears/etc, the wood industry for texture and colour
classification, and in a number of other industries.
Acknowledgments
Assist consortium is grateful to the European Community for the economic and trws.tful support.
References
il]
[2]
J. KHtler, R. Marik, M. Mirmehdi, M. Petrou, and J. Song.
Detection of defects in colour texture surfaces.
/APR Pmt. of Machine Vision Appficatiorrs 94, pages 558-567, December 1994.
M. Petrou.
Optimal convolution filters and an algorithm for the detection of linear feature.
fEE Proceedings-1, 140(5):331-339, 1993.
[3]
R. Fioravanii, S. Fioravanti, P. Giusfo, and Vemazza G.
Real-time texture defect detection.
[n Proceedings of Phare-accord Workshop on Eii?cienf Texfure Analysis, volume 1, pages 57-66, 1994.
[4]
D. Corso, R. Fioravanti, and S. Fioravanti.
Morphofogicd analysis of textured images for identification of thin structures.
In Proceedings of LEEE kf. Conf
OR Acoustic,
Speech, and Signal Processing, 1995.
[5]
J. Kittler, R. Marik, M. Mirmehdi, M. Petrou, and K.Y. Song.
The detection of local abnormalities in random macro textures.
Technical report, University of &mey b’s5p-T??-4@5, fgg~. .
[6]
S. Fioravanti, R. Fioravanti, F.G. DeNataie, R. hfarik, M. Mkrnehdi, J. Kittler, and M. Petrou.
Spectral and rank order approaches to texture analysis.
Eurvpean Transactions on Telecommunications, 6(3):287-300, 1995.
go
9
E
9
&
m
R. Fioravanti and S. Fioravanti.
Texture description through rank-order functions,
in Proceedings of JEEE Workshop on Nonlinear Signs/ and Image Processing, 1995.
[8]
F. G. B. DeNatale.
Rank-order functions for image texture discrimination.
h’rternational Journal of Pattern Recognition and Artificial Intelligence, To appear, 1996.
[9]
C. Boukouvalas, J. Kittler, R. Marik, and M. Petrou.
Automatic grading of ceramic tiles using machine vision.
in iEEE M Symposium on Industrial Electronics, pages 13-18, 1994.
[1 o]
C. Boukouvalas, J. Kittler, R. Marik, and M. Petrou.
Automatic coiour grading of ceramic tiles using machine vision.
IEEE Trans. Industrial Electronics, To appear February 1997.
[11]
C. Boukouvalas, J. Kittler, R. Marik, and M. Petrou,
Automatic grading of textured ceramic tiles.
In Proceedings of the SPIE, volume 2423, pages 248-266, 1994.
[12]
C. Boukouvalas, J. Kittler, R. Marik, and M. Petrou.
Automatic colour grading of randomly textured ceramic tiles using machine vision.
In IASTED M. Conf on Computer Applications in k?dusfry, pages 121 –1 23, 1995.
[13]
C. Boukouvatas, J. Kittler, R. Marik, and M. Petrou.
Colour grading of randomly textured ceramic tiles using colour histograms.
IEEE Trans. Industrial Electronics, Submitted, 1996.
11
,
[14]
M. Petrou and C. Boukouvalas.
Model based cofour grading.
In IEEE and LSTIMDSP Workshop, pages 110-111, 1996,
[15]
C. Boukouvaias and M. Petrou.
Perceptual correction for co[ow grading using sensor transformations and metameric data.
IEEE Trans. hdustry Applications, Submitted, 1996.
[16]
M. Petrou and C. Boukouvalas.
Colour quantification for industrial inspection.
In [EEE [WSIP, pages 6f 1-614, 1996.
[4 q
C. Boukouvalas and M. PeWou.
Perceptual correction for colour grading of random textures.
IEE Vision, Image and Signal Processing, Submitted, 1996.
8
I
t
9
I
,
~
9
B
‘9
IS
II
14
.
EXPLOITATION REPORT
CONFIDENTIAL
CONTRACT N*:
BRE2-CT92-0260
PROJECT N*:
BE5638
TILE:
ASSIST
(Automatic System for Surface Inspection and Sorting of Tiles)
COORDINATOR: Bull HN Italia
PARTNERS:
I-T One Tile
Thomson B
University of Surrey - DEEE
Universit5 di Geneva - DIBE
Thomson TCS [Associated to Thomson CSF)
REFERENCE PERIOD: from ‘1 March,1993 to 30 September, 1996
STARTING DATE: March 1993
DURATION: 43 months
PROJECT FUNDED BY THE EUROPEAN
COMMUNITY UNDER THE BRITEIEURAM
PROGRAMME
pag 1 oflf)
1.
Description of results
A prototype system able to select ceramic tiles by means of image grabbing, storing and
processing has been developed and tested with good results. The system, is currently
able to operate at reduced speed (less than final system requirements) cm plain and
textured tiles. Tiles are classified in up to four classes depending upon relevance of
physical defects detected, in addition color shade classification can be performed.
Recently, prototype of systems addressing the same problem have been presented to the
market, but none of them seems to be completely adequate to the purpose. Some of them
have been also installed in production plants, but not yet in Italy where the majority of the
european tile production is located and where quality requirements for tiles are more
stringent.
We think that ASSIST may be well considered representative of the state of the art in
vision technology applied to ceramic tiles inspection.
Besides addressing its main goal, the development of this precomtpetitive system has
brought to the availability of many building blocks and tools reusable in similar areas of
application. The main reusable objects that can be identified are the following:
1. Advanced linear cameras, blacklwhite and color. The color camera is based on new
2.
3.
4.
5.
6.
high resolution three linear sensors.
A special controller for the direct correction of linear cameras to DSP modules, based
on an innovative architecture which has drastically simplified the image acquisition
channel. There is no need for a specific frame grabber module, but the data are feed
directly into the DSP module.
A mu[ti DSP software platform that can be use on systems with one to twenty 13SP
modules, and can be easily expanded to support even more complex architectures.
A sophisticated DSP algorithm library for image processing and acquisition
An image analysis workbench integrated into a software development environment
which simplifies the evaluation and development of new Visual Inspection solutions.
A Methodology for visual inspection problems approach, from economical to technical
evaluation of-new opportunities.
Some of these have already been successfully tested by Bull in V[sual Inspection
concerning different areas of application.
Related Subject Descriptors
The following codes applies to the scientific field of this project
El 5 inspection, testing
Technology for contact less, high speed, real time products inspection
E37 Quality assurance
System for acquisition and recording of quality data on 100% of production
E32 Process control
pag20f10
Real time products inspection allowing for immediate feedback on production process.
Furthermore collected data are extremely useful for evaluation of Overall Equipment
Efficiency (OEE)
E04 Ceramic, glasses
The main objective of the project is in the area of quality control and sorting for ceramic
tile production
D32 Signal processing
Visual Inspection technology is based on fast signal detection, via linear or arrays
cameras, and processing based on paralle[ DSP architecture
2.
Industrial applications
To date there is a growing interest in the manufacturing sector for automatic quality
measuring systems strictly integrated with the production process. Visual inspection, by
means of computer vision, is probably one of the most growing technology in the area of
quality control. The value of this solutions is not only in the final sotiing process of good
and defective products, where they are directly operating. But also in their capability of
collecting real time data about the overall production process, the basis for successful
plant maintenance and effectiveness improvement.
Besides its final objective, which will require further development to move from the results
of this precompetitive “research to the development of an industrial product, ASSIST has
brought to the deve~opment of many applications taking advantage of the know how and
building blocks of this project.
identified areas of application, some of them already under exploitation, are the following:
m
1. Automatic quaiity control of TV monitors
2. Inspection of silk-screen prints on electronic components
3. Inspection of packaging for alimentary products
4. Quality control of security printings
5. Quality control of wooden panels, raw or decorated
6. Quality control of other kind of plastic laminated materials
7. Inspection of plastic molded objects (2 D)
Related market sectors
The following codes apply to the market area of application of ASSIST results
E32 wood products
inspection of wooden panels produced in large mills, some of them using recycled
materials like pallets or wooden boxes. The production system is fully automated and
integrated. Quality information is of high importance to altow immediate feedback in case
of abnormally defective products, or to identify possible problems cause.
pag30f 10
E19 packaging
Automatic inspection of packaging, mainly for the pharmaceutical and alimentary industry,
petformed both at the packaging line or at the package production plant, is of paramount
importance for product integrity and human safety.
These production processes are so fast (10-20 parts per second) that 100% human
inspection is absolutely impossible, while computer vision technology can be successfully
used.
El 5 manufacturing control systems
In general a computer vision system can be directly connected to a plant information
system providing real time data concerning 100 % product quality and plant performance.
E23 printing, publishing
The printing process is to date extremely fast (up to 6 m /see) and is sometimes used to
print important. information (i.e. serial number on a check or a note). Human control of this
process is impossible, while on time detection of errors may save the production of
useless documents.
E03 ceramic products
The main target of ASSIST is the tile production industry that cannot implement a
continuous production process only due to the need for manually inspecting each single
tile produced.
A significant economic impact is expected by the automation of this process.
From the previous list if industrial sectors that can make use of computer vision
technology we can see that many benefits might derive from it in two different but related
areas: 100% product quality control and real time equipment efficiency monitoring.
The vision technology is expected to spread in the manufacturing industry starting from
those sectors for which one of this two aspects is more important (which means
convenience to invest at to date cost of vision technology, ). With the expected cost
reduction of these systems in the coming years more companies and even other sectors
wili find convenient their usage.
From a point of view of companies involved in bringing vision solution to the market, we
think (and have also experienced) that many companies wii[ be involved in this process:
●
Modules suppliers
1. Companies specializing in optics and illumination techniques
2. Companies producing CCD sensors and DSP processors
3. Companies producing cameras
●
Research institution
developing innovative algorithms
●
System Integration Companies
providing the integrated vision system (or better “vision engine”)
☛
Factory automation companies
pag40f10
capable of integrating vision systems with the specific automation required for each
target sectors (tiles, wood panels, plastic, print) having a good level of competence in
that market
. End users (production plants) in different sectors
All these kind of companies will be involved in the process of bringing eticiently to the
market vision systems.
3=
Market analysis
The tile production in 1995 had a positive performance of plus fl 0 ‘A in quantity over
1994. This represents a significant increase in line with predictions. A more detailed
ana!ysjs indicates a different trend for the various kind of products:
Type of tile
on tot.
prod.
62.60
16.68
14.71
6.01
0/0
single-firing
double firing
vitrified stoneware (gres)
other
trend
stable
new
kilns
16
notes
-
-1.5Y0
+12,62%
~?
14
constant increase
Production distribution in 1995 (Italy)
,
year
?989
1990
1991
? 992
A 993
fi 994
~ 995
quantity
million mq
434
446
432
434
458
510
562
variation
+72.47%
+2.92%
-3.19?40
+0,51$4
+5.53!40
+1 1%
+10%
Italian tile production from 1989
Even the forecast for the next year production is positive and is estimated in the range of
O
+lO /O.
pag50f~0
Capital investments
One further important aspect to consider is the amount of capital investments which the
tile industry is typically affording. The kalian tile industry has invested 400 million ECU’S in
1995, with an increase of 44% over 1994. These investments are mainly in the area of
technological innovation and improvement of products and services. For 1996 the amount
of capital investments is expected to be significantly lower, due to less favorable fiscal
condition.
I
Prod uct target cost
Tile production costs have again analyzed in order to understand the value of an ASSIST
system, i.e. the price that the industry may be willing to pay.
Tile production cost per unit (square meter) may be considered equally divided among
three areas: raw materials, energy and man power. ASSIST addresses the last element
which results to be the one were significant changes and improvements are still possible.
A production line may be considered as build around a kiln, which is the unit defining the
production speed. H has been calculated that for a typical modern production line the
saving, due to automation of the selection process, will be in the range of 120,000 ECU
per year.
Assuming an amortizing period of two years it seems reasonable to consider that an
investment of 240,000 ECU may be industrially acceptable to fully automate one
production line (1 kiln). Beside the absolute vaiue, it has been also calculated that
ASSIST, with full functionality, will reduce production costs of apx. 4-5Y0.
AS a resuit of all previous consideration we may assume a target ASSIST price of 120,000
ECU per system. H has-to be noted that this price cc)nsideration does not take into account
any other beneficial effect that ASSIST may provide like: better quality, objective control,
continuous production process, equipment efficiency monitoring. These will be considered
as plusses that the system will provide beside the direct economic benefit.
Potential market
Concerning the potential market, we have estimated that there are 1500 selection lines
currently operating in Europe and America which may be interested in. a system like this.
For a rough business evaluation we may assume that one third of them may integrate
ASSIST in 5 years since its introduction and that they are going to be mainly concentrated
in Italy where high quality products are produced. This results in 100 systems per year,
with possible revenues of 12 million ECU’s per year.
This analysis brings to the conclusion that: the direct business associated with ASSIST
sales is interesting and that the economic effort required to the tile industry is absolutely in
line with the typical trend of investments.
As far as ASSIST industrialization, production, distribution and maintenance channels are
concerned it results more convenient to develop an agreement with one of the various
automation companies already specialized in the tile industry. Contacts with some of them
have already been established.
** production data in this section are from 16th Halian statistical analysis - Assopiastrelle
pag60f 10
4.
industrial and intellectual property riahts
Up to date it was decided not to apply for patents or other protections of inteiiectuai
property rights, even though some of the ideas on which software and hardware moduies
are based may be suitabie for protection. The reason for this choice is that algorithms and
other software soiutions, for the time being, are well protected due to the hardware
piatform on which they run.
The issue of protection wiii be reconsidered during the exploitation and industria[izat,ion
phase, and at that moment, before deiivery of first product to the market, proper
protections will be implemented.
5.
Exploitation and marketing p [an
The exploitation pian of ASSiST is based on three main assumptions:
●
●
●
Some of the obtained test resuits are positive, even though more tests and further
development in some areas is thought necessary.
We have build up an important know how on tiie inspection.
An industrial partner, from the tiie automation industry, must be invoived in the
exploitation phase, for many reasons iike:
- know how on automation aspects (product industriaiization)
- deep knowiedge of market
- customer base
- product lunch strategy
MODULES
SUPPLJERS
V.1. m’s
NTEGRATOR
AUTOMAllON
suPPilERs
SPEC(FIC
ViSION TECHNOLOGY EXPLOITATION CHANNELS
pag70f10
Based on above assumptions, next steps of the exploitation process are:
1. presentation of current results to “potential partners”
from fl 2-96 to 3-97
2. demo of ASSIST to selected interested partners
3. definition of preliminary agreement, MOU or statement of interest
4. joint Product Functional Specification definition
5. verify at which extent current results are satisfactory for a first level product
5.1. or identify R&D effort still required to obtain a satisfactory product
5.2. or identify classes of products that can be addressed by actual system
6. definition of a joint development plan with the objective of bringing a first product to the
market, for a beta testing phase
6.1. Expected delivery of a beta level system: end of 1997
6.2. Expected delivery of product: mid 1998
Step 5.2 is extremely important because we think that it can be possible to start delivery of
a product addressing a relatively simpler class of products, while at the same time further
research might take place.
e.g. it may be noticed that in 1995, 17 new kilns were installed just for GRES products,
which present a relatively simpler texture. And this kind of product has been constantly
growing in the past 10 years,
1997
G F M A M J J A S O N D G F
M A
M J J
A S O
PRESENTATION TO SELECTED PARTNERS
DEFINITION OF PRELIMINARY AGREEMENT - MOU
PFS ANALYSIS
MARKET ANALYSIS - IDENTIFY FIRST TRAGST SEGMENT
m
DEVELOPMENT OF BETA PRODUCT
BETA TEST PHASE
PRODUCTION AND DISTRIBUTION
ADDITIONAL RESEARCH
EXPLOITATION IN OTHER TARGET SEGMENTS (TILES)
Exploitation time-table
pag80f10
Objective and interests of each partner in the direct exploitation program are:
For Bull Italia
To supply the vision engine, constituted by processing system hardware and software, to
the company responsible for the delivery of a complete product to the end user.
For Thomson
To suppiy cameras thru T(X, or Jicence to build cameras, to be integrated in the image
acquisition system. See annex 2 for a better understanding of Thomson position.
For IT
One Tile, and the Marazzi group to which it beiongs, to be among the first users of the
system with all the possible returns that this may imply (economic and commercial
advantages).
i
i
For DEEE and CNBE
Further involvement in research activities necessary for completion and/or evolution of the
product. See annex 1 concerning CIEEE exploitation strategy
Besides these objectives strictly related with the direct exploitation of ASSIST, each
partner will exploit in other areas the know how and the building blocks made available
during this program.
6.
w
Communication strategy
As stated in previous sections, we think that the best strategy for delivery of vision
solutions to the various markets is through cooperation with automation companies well
known in each market segment.
This strategy directly aff~cts the communication channels used to access, not the end
users, but the companies who can successfully bring a fully integrated product to their
customer base.
We have already setup direct contacts with some of the major companies operating in the
tile automation industry. Furthermore Bull has identified companies specialized in each
industrial sector that it intends to target and has aiready established good bases for the
definition of exploitation agreements.
After the definition of agreement with these companies, most likely it will happen that the
developed products will be presented in cooperation with them at specialized fairs of each
sectors.
e.g. Bull is analyzing the possibility of presenting a vision solution for plastic molded
objects at the PLAST fair next may in Italy, in cooperation with a medium company of this
sector.
pag90f 10
7.
Annexes
1- Exploitation strategy from University of Surrey
2- Exploitation plan from Thomson
‘B
2- Abstract from 16th Italian statistical analysis - Assopiastrelle
I
I
e
9
pag 10of10
ANNEX 1
Exploitation: UNIVERSITY OF SURREY
Exploitation of’ Results
The algorithms for colour shade grading of plain, two colou.r and textured tiles and the crack
detection and chrornatos~cturzd inspection algorithms developed as part of the project are
applicable tocolour texture surface inspection and grading in other sectors. These algorithms
advance the state of the art in stiace inspection and grading which, up to now, was largely limited
to plain surfaces. The possibility of exploiting these techniques for visual inspection in textile
industries and in particular in porcelain and earthenware industries+vill be explored. This currently
involves an evaluation of the techniques on inspection problems in these areas in conjunction with
existing research projects. These applications have considerable economic potential in terms of
automating the inspection process and therefore aiding in the enhancement of the product quality. In
addition, the inspection process can be used to control the parameters of the production process
itself to reduce waste.
Exploitation Plan
im
The University of Surrey will expIoit the results of research and development in a number of
different ways.
1. The main role of the university is to provide education and to offer the best quality professional
and vocational training for its students. The research results (inspection methodology) obtained
as a result of project ASSIST will be incorporated into the syllabus of its courses, in particular at
the MSC level, which wiH aid the European industry by providing graduate engineers with up to
date knowledge in surface inspection and related areas. This wilI contribute to the
competitiveness of the European industry and its ability to continue to innovate industrial
technology and bring new products involving machine vision on the market,
L .
A second, and equally important role of the University is to carry out research and advance the
frontier of science and engineering. The research results will be exploited in the mu-rent and
i%ture research projects and through consuha.ncy. A particular effort will be made to attract
projects horn industry. With this objective the University has appointed in the Centre for Vision,
Speech and Signal Processing, a Research Support Off~cer with the main responsibility for
technology transfer. This appointment represents a considerable investment to facilitate research
result exploitation.
3. The University assumes that the main drive for industrial exploitation of the results of the
project will be applied through the two industrial partners of the project. In this context the
University will provide all the necessary help within its means to bring an inspection and colour
texture grading system based on the research accomplished under the ASSIST project on to the
market. One specific aspect of the exploitation plan which WH be addressed by the Universi& is
a further evaluation of the inspection algorithms developed. This will be camied out as part of a
natiomdl y fimded project entitled “Visual Inspection of Colour Texture Surfaces: Operational
Integration Perfomumce Evaluation”. As part of the project a large number of tiles will be
inspected to establish statistical] y significant petiormance characteristic of the machine
inspection algorithms.
4. The University will also seek to exploit the research results directly in other industrial sectors
via other industrial companies subject to any cosntraints imposed by the existing collaboration
Agreement,
‘c
9
.
t
●
.
e
.
,
~
O
N
lNlRi3DLlCTK3N
Since the beginning of the ASSIST project in 1993, many changes have uccured in
the organisation and goak$ uf the THOMSON Research Gentre located in Rennes
(France) and this is the main justification for certain variations in the intentions of
THOMSON with regard tu the industrial exploitation of ASSIST results.
1
~
1 v’
It is not worth coming back again in details here on ali these changes, but it should
on[y be pohted out that, by now, the ASSIST camera arid the associated team are
part of THOMSON rrwitimedia Corporate inrmvdkm and Research (W@, whose
main goai is to make advanced oorpfxate studie$ and development for paving the
way tithe future consumer digital video products af THOMSON.
This mainly means that:
a as ~l&R has n~ Me objective, nor the organisation to seil ~roducts by itself (as it
couId have been the case with the ‘LB% structure’ at the beginning of the project), no
‘direct’ industrial ‘external’ exploitation of A&31ST resu!ts can now be planned;
E ~in TJ-K)MSON, but ~ufside THOMSON multimedia Cl&R, exploitation Of the
ASSiST oamera could be ernkaged, especially in T(X (THOMSON COMPOSANT$
SPECIFIQUES) which was our Associated Partner in the framework of the ASSET
project.
JNTEM3ED EXPLCW$TIC2N OF THE ASSIST CAMERA WITHi~ THOMSOt$
1. Contaots have been iritiaiised with TCS tbr trarwfening a limnce of the camera to
them, but, up to now, discus~on$ have not reached a final end, mainly because a
patent problem has not been ck?ar’ed out.
2. Anyway, C}&R is open to discussions to seiling nun-exclusive Iicences of the
ASSET oamera to any third-parties which could be interested in this equipment,
provided that this would not create any conflict with basic industrial interests of the
whole THOMSON group.
ANNEx 3
9
‘8
,D
part of
16° INDAGINE
STATISTICA NAZIONALE
I
I s
$ . .*/
‘$
ASSOPIASTRELLE
—
J3ndaginc Statistic Nticwde & realizzata attraverw il censirnento di tutm k
;C
m“ende itakuw produtici di piastrelle di
I
eeramica e eostittdsce cmmai imo strumfxto
conuscitiw cmsolidato per 1a Winizicme dei
l –
dati strutturaIi de~ settcre.
La 16aedizione ddl’hkg{ne si & svolta nei
, 8
prfrni mesi de! 1996 con riferimento N’intem
arm 1995. Intewistatwi oppcxtunarnente
istruiti hanno visitato tutte k swiet& operami
I S
in ItaIia, ottenendo la compilazitme di w queshwio appositamepte predisposto.
‘ Eorgantiatione gtheral~, Wabcmzicme
I
<
dei dati e la stesura del eommento smm stati
curati direttamente dal Cen&u Stud AssopiaE
.1
strek <
.
n
:SQ
-
NOTE METODOLO GKHE
~
11
@
Per una eorretta analisi e iriterpretazkme
dei risultati presentati DWQ tabelk alIegatc, &
utik Elustrare il metodo di rlhwazicme dei
dati c ia twmincdogia adottata.
1) p e r k
pmtkibne:
a) quelIa diretta dde aziendc, retdizzata
con proprio rnarchio;
b) queNa per eonto terz~ vale a dire per
canto di altra m“enda produttrke @o societi
-comma%de, con marchio dell’uienda ~m.
mittente.
La smmw (u + b)
fomkce
fl qwmtifmivo
male di>rodttrwne del setture.
21 per i% wmiiiie:
a) que!le di attiviti?i diretta (Italia ed
export), relative ckk alla produzione effettuaia con marchio proprio;
13) quek relativeak qrantit~prodotte per
conta Wzi (Italia ed export), cio$ alla produzicme commissionata (eon proprkj marchio)
da akra aziendaproduti~ e/o datma societ3
commercial;
c) la cmnmercial-one deIie azitmde
produttrici (Ralia ed export), ciok Ie vendite
di un’azienda,di materiali fatti produrre (eon
il pmprio marehio) da un’altra azierida produitric-e.
pruduzhm delk aziende censite &stata
‘a *Lasuddivisa
in aJ&itA diretta e attkitiii canto
1s
r
&rzJ.
Per quanta riguarda lewndite, tstata evidcn-
ziata la “Cf3mrnercia ~zione”
‘
di materiali
prodotti da teti Quests eonfigmxzkme ha
permewm di icientificarix
9
1
It
1
-
, .
..
.
9
B
.
U LEAZIJHVD E E I DIPEN DE NTI
o
AI1a data disvolgimento dell’indriginesono
state registrate in ltalia 340 a.ziende produttrici di piastrelle di ceramiw. RisgMtto
aikmna precedence, smm risukate 5 um”ti in
meno, come saldo ti chiusure di aziend~
fusioni e/o incorpm-aziuni e ingresw nel settore di pkcde unitk ““~ micq costituziorie o
,..
non ctmsite in precedem, produdici principrdmente di pezzi spec@i e caedi.
I dipendenti, al 31/3241995, sono ns~ki
32386 (+5,22%) conferm~do Mnversionedi
* tendenza inizka riel 1994, dopo tie anni di
>.
~ntimm cdo degli o&upati,
1
D
s
—
@
1=
.
.
.
:.
in iinea con l’andarnenta delki produzione di
tali comparti, came piti avanti descritto.
Le variazioni def fomi relative al)e akre
tipdogie productive trovano ~&’meri~ntrO negli
andamenti delle rispettive
pruduzion.i.
12utiltio dells capaciti produttiva k stata delJ’85,46% contro 1785,X% deI 1994. Da
se.gnalare che [’ineremento del numem dci
ford k je”riibtie teenck@c hanno mmpo~m
1a creaiiofietii tma rrmggiore capacit& produttiva the, kfatti, nel 1995 & aumentata de~ 10%
circa rispett~ al 1994.
M LA Pltmuzmm
12 1 FORM E L’UTILIZ ZO DELLA
9
C!APACIT~ PRC)DUTTNA
??el 1995 la piaduzicme comple~iva ha
confermato ii &end di crescita che aveva
earatterbto in”partiedwe Mh-ncI tritmnio;
arrivando ad un nuovo record determinate
soprattutto dal positivo andamento deU’export. WI 1995 sono stati pmdotti 562,2
mdioni di metri quadrati, pari ad un inereniento del 10,19% rkpetto ali’armo precedence.
Al kmnine del k$)$ il mmxxo dei forni
..
kstalkti & risultato di 396 uni~a .,,
Rispetto alla precedence rilevazione si 5
registrato m awnentu ,del nurnero dei forni
pad a 47 unit~ dovutci principaIm=te aWineremento degIi impianti per gres porcell%ato
(+13, per mwmeottti chkra (+16) e ‘per
corredi (battiscopa e altri pwzi speciaii + 14)
i
t
8
a
@
sow
SO.(M
k
\
\
,
8 1 8 2
\
\
\
1
,
U 8 4 W M m 8 E 8 9
. . ,.
---- q m u b u i
*
S U 91 1 19 2 i W W 9 15
=k?ulC
“
..
4ooq. ”
1
d
350
300
-
“
1
-
20(1
/
h
50-
-/
-
/
\
’
“
—
* . * — - - - m ~ d - = = * a m,a -*----.* .
1
,
.
w
e
9
a
0
*
.
-
.
gres pmdlanEm
s
,
*
x
&
—
-
alcri
prodoti
●✍
- .
-
&
*
”
*
I
i
80 al 82 m: M 8586.” 8?:”88 89 90-91 92 93 94 95
La ripartiz!onedellaprodtioneper tipo di
prodotto non ha wbito sostanziali rnorii&@:
stabile la morwottw cm il 62,60YQ” dell~
produtirme eompkssiva (era il 62$1% nel
1994’); la bicottura ha visto cake la propria
quora std totqle daI 18,10% del, 1994. d
16,68% registiato nel 1995.
11 gres porcekrtato ha cmtinuato rid inerernentare i suoi wkmi &to a rappresefttare d
14,71% (era i] 10,81% nel 1992, ii X?Jl% nei
1993 e il 12,62% nel W34).
11 cotto rustieo, do@ d cah eorisisteote del
1993, ha ripreso,a erescere ma ha. p~po quota
M totale il ~159Z eontto ii 2J9Ye dell’ann~
precedence,
.b produtione dei””corredi (battkmpa cd
akri pezzi Speciali) 6 stata di 12~ milioni di
metri quadrati, 1 mikme in piii rispetta al
1994, pari ad w incrernento del 9,12%; rappresentarm, come neI 1994, i1~20% della produzicme cornplessiva.
Tale andamerlto conferrna la crescente teridenza delle aziende a eompletare la prapria
gamma di pradotti per offrim setvizi sempre
piii rispondenti alle richieste di una clientela
sdisticata ed esigente,
1. aflivif.d direfta e altivitd conto
teni
Nel 199S le aziende che harm prodotto
per conto terzi, vale a dire con rnarchio di
un’aitra azknda pmduttrice e[a socktk com
rnerciale, hanno realizzato tm volume di pr
dwkme pari a 90,8 milioni di metri quach
(il 16,15% dells produzkne eomplessiva d
1995).
Urmremento suli’armo precechte, dete
rninato da 16,1 rnilioni rli eonto terzi Prorh
in piii riqmtto al 1994,5 stato de] 21~57&
❑
w
m-ckzi
a!tdfrcmi
Questo conferma, da Un latr+ Ja crwxnt
i.ntegrazione tra aziende di produzione
dall$altro il rualo sernpre piii impormte su
mercato di soeit%i cornmerciali can tnarchi
proprio, laeui attivk~ k ingrado di influenzu
e rendere axmora piti eompkssa la struttuq
dells distrjbitzjone sia in Italia &e Wester
come. wdrema nci eapitoli sueeessivi.
c) Ilnpa.$!!o per terzi
2 zone ifi kwdkmzwneprndut tiva
Nel 1995 Ie provincie di Modena e Reggio
Exr@a harmu pmdotto i~ 78,70% del totaie
nazkmale (in particdare harmo pmdotto
1’80,73% & attivith:~etta ed,iI 68,14% di qttiVit& c/terzi). II tikrzi rappresenta in quests
zona il 14% circa “deka produzjcme cornpksSim
II resto Emilia Romagna detiene 1’8,57% del
totale nazknale (la stessa pereentttale
riguarda sia ajl’attivitil diretta che iI clterzi),
Nel rests dWIta.Iia ? stata pradatto il residuo
1~74%: piii in specifico ii 10,69% di attiyit~
diretta e il X$34% di attVlt& e&xk Da scdtolineare la partiwlarith che nel resto Itaiia ia
produzione c/terzi ,,mppmsenta quasi il.30%
‘ deIla produzione @.rqplessiva deIla zgrqi eun‘ tro iI 14!% CM Cornkensario, mme wpm
riportato. Questo giustifiea d ado in terrnini
I t
di rappreseritativiti pwxentuak ck ii ComI
prensoria ha registrato rispctto allo scorsc)
a.mml passmdo da~ 79,21% al ?8,70%.
10
-
‘9*
1“
r
f
.-
La produzione di impasto per terzi, pari a
1,6 milioni di trmnel]ate, si ~ portata su valori
piil equiIibratl rkpetto agli armi pissati,
Le vendite pari a 1,4 milioni di tonnellate
sorm aumentatc deI 27,1296 (8&17~ deila produzione). In particcdare sono wmentate deI ~
34,66% k vendite di ~ irnpasto atomizzato
chiaro, in iinea con l’incrmrmto delhi mono--eottura chiara.
1.4 LEVENIMTE
AIwhe ,Ie vendite totali hanno fatto registrar krcmenti positivi nel eorso de! 1995,
d~ti escIusivamente Wandmnento delI’expart. Complcssivarnente sono stati wmduti ~
5424 xnilioni di metri quadrati, pari ad un
aumento del 7,58% rispetto al 1994,
L atfiviiti direffi.z f? cfxrmzercia&z.azbe
Le vmdite cmispondenti all’attivith
S37%
12.74%
E
l
❑ rcsto Entilia R
❑ rcstolka[ia I
I
Ii) Suppati per ti~i
Nel 1995 la produzione di supporto per
terzi ha manterwto i Iivelli CM 1994 (?1
miiioni di metri quadrati).
Le vendite di supportcI in ftrdi~ pari a 60,2
milkmi di metri quadmri, cmrispondono
sostanzialmente, eselusi @i searti di kworaziorle, ai volumi di produzione delk smaltcrk
(59,4 milioni di rnetri’quadrati).
direttri pari a 452# milkmi di metri quadrati
(+5,41% wl 1994), eostitukemm H13~O%
ckk verkdik corrqdessive (erano 1’85,22%
he! 1994).
Le veridite di nmteriale commerciaIizzata,
cioi+ Ie quantitii ck Ie stesse aziende hanrm
fatto produrre con marchio proprio ad ahre
aziende, sono state 42 rniiioni di rnetri quadrati, pari al 7,75% deHe vendite tutali.
Mine, circa 47$ rrdioni di rnetri quadrati
so no stati destinati a societ?s commerciaIi sia
italiane che estere che operano SUI rmxcata
con marchio proprio.
Qu=t~ Ultimo fmmxmo ha registmto
negli ultimi anni forti ritmi di creseita: dai 13
mfiioni del 1992 si A passati agli altre 4’7,5
attuali (+264%).
2. njxx!ogii dipmdotto
.
Dal punto di vista delle tipcdogie di prodotto, la monocottura rappresenta iI 62,72%
delle vendite tota~i, q 340,2 tioni di metri
quadrati venduti n&l*95 [+7,62%).
Labi@tu~ con 90,1 milioni di metri quadrati, k passata dal Y&44%4” 16,62%,&Ile
,..
vendite (- 3,03%). ,: ‘.”+ ~~.
~n bucm hxrerncnto di ve~diti e,~ peso
percentuaie SUI to@gdelJe &sse & stita registrato dal gresporcellanato, che passa dai 62,3
rnilioni dirnetriquadrati del 1994 ai 78 del ’95
(+25,17%). Ora iI gres porcelhmato rappresenta D 14,3$92 delle vendite cornpkssive deI
settcw, ccmfermando una eostante ascesainiziata eon 1a second;~eth deg{~ &mi ’80 (e& 3
Z?% neI 1984, quasiiI.6%nei 1981i,I’8J?% qql
1990, 0 10,8% d 1992 e il Q4 nel 1994].
Al gres porcdanato seguono, in ordine di
iiripartanza per vohirni vendu& i cotti rustici
che eon 11,6 milio@ di mctri qua&i h,tinb
mgistrato un inerenk?nto del 6,93%.
In calo il kk.ker cm@3 mil.k~ dimetri quadmti (-&18) e iI gr~ rosso e COkatO Cbe,
eon qwui 4,3 milioni di tietri quadrati
(-3,S% sul 1994), rappresenta ma lotl,’79%
del totak vendutg..~ ~::,
,.
:
Intmssante l’incre~e.nto iieIIe vt@ite di
“akri prodotti” da 11,9 a 12# mi.bni di rnetri
q u a d M i ( + 8 $ S % Sld 1 9 9 4 ) .
Quests mtegmia include quasi mciusiwrmmte tipcdogie di eorredo (list~w grad&i~
bat?kqwq corrirnaqp: e=) non xientranti
nelle altre ckssificazioti. Hncrernento di
vendite di questi prodotti & cormesso allo” sforzo della quasi totalit~ dek aziende di
cornpletare la propria gamma con tm vasto
corredo di prudotti estetieamnte e/G fu.nzionahnente compkmentari alle piastreile di
fondct.
3. deminazbn.e ven.dte Italti e Yendlk kapoti
b a.ziende produttrici harum indicato di aver venduto 2123 mikmi di metri quadrati
in, Italiq (-(),1270 rispetto al ’94) cd cOmpoi
St :
a) .@L66/$37Q ~q, vendite di attkiti diretta
b) 17.7?5.676 ‘req. commereializzxdmm
c) “11.863.206 mq. vendite di soueti commer–
Ciay. ~n Inarchio proprio
(k&fmma dib+c cmiqfmdedle wnfiifediaffii.fti
Clrt?my
M’esters ~cmo stati destinati, sempre
semndo le indicazbni deJle aziende di produziwm,. 330,1 milioni di rnetri quadrati
{+13,20%) eosl eompcxti
a) 27&264.lfj8 rnq. vemiite di attivith direb
b) 24.2+2.733 ~. cormnereiahzzazione
c) 35589.6.15 mq; vendkc &c societi comrnmeirdi con marchio propriQ
(lascmma dib+c umis-omfe&vena&dia uivitii
dw!tzi)
-
Isil
IB!
o
M & k @ w ~ N a z i m d e `.""" ="'"' ``.. ".". ".`..'"
Vcndkc kdia
Ventm upon
V&ndk t4t&li
``. " . . . . . . . . . . . . . . . . . . . . . . ..i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .pagi?
s
$“’,i
W 199S I’expoti” complessivb dell’iffriustria ceramiea -.Iie”gistrato
‘”
@a:una specifim
indagine condott~ &@wopiastrel]e - i ~tato
n
di 361,4 milicmi di r &.ri qu&~ti (~11,24%
.
rispetto al 1994).
Quest: ciati meritano akune cansiderazioni.
Ckxxxre rk.ordare cheia prexmte Indagine
fornisee dati secondo le indicazioni dck
irnprese produ~ici,
La fonte aziendale rileva pertarm come
“’vendite Ealia” anck quek i-iferite’ a- piastrelk cedute a suci@&comm”eddi o ad a.ltri
cIieriti chc a loro vojt’d” effettiianb” operazid
di
espm-tazlone.
“
~
‘
CZuesto confernm, seconcio quanto dieevamo primi+ iI ruolo’~empre rnaggiom ch+ k
societ~ commerchdi “svcdgoxm rispetto aIla
rete distributive. Tali’ societh infatti oltm a
distribuire i quantitative faxi produrre cm
rnarchio proprio, krnettono sxd mercato italiano ed estero anche prodotti delk &iende
di produzione:
:
- lI”data expo~ fiiiki ‘kl 1992 di fonte Istat e
successivarnente di ‘“kotite diretta Assc@astrelk, rileva invece tutte b quantitii che passano attrawxscs Je fiwntiere e quindi anche le
quantiti vendute all’&&o dagli intermediary
commerciaii italiani.” ~
Pertanto, i dati ripotiati nella prestmte
indagine sono vaKdi dal puntm di vista delle
aziende di produzion~ da] r?twto di Vi?S#a “clef
BUUOIQ gli sfessi *gm essere r-icomposti
can k rikvazimd ‘iIi.,.fonte dketta Assopiastrelk. E dwtque$ terkndo conto ~he k vendite cmnplessive sono pari a 542,4 miiioni cii
metri quadrati, e IG ,vendite export 361,4
itki[ioni dL metri quadrati (oltre i! 6670 delk
vendite Maii), il niereato ink-no ha assorbito, p e r d i f f e r e n m , ~ d i m e t r i ~
dH&(~Qn w parziale recupero di circa m
punto percfmtuale sul 94).
....
.;..”,
:
- ~.:..~
J&’ W@l &e&&fi JLJ &@
’
,.. .
....
$ostanzialmente stabile 6 rirnasta Ia ripartizicme ddk vendite MIia per aree geografiche convenziwxd.k il Sud e klsOIerappresentarm iI 32@% delle vtmdite, seguiti dal
Nerd-Est eon H 25,23%, dal Ncxd-Ovest
eon il 22,33%, cd Mine ckd Centru con 11
20,06%.
Le ;endite “Alla produzimw wmto terz.i si
comentrarm, nOII a cam, I@ Nwd-Est (eke
il 95% dkl tbtaie); paich~ proprici in quests
area M sede la maggior park dek imprese di
produzkme e di ccmmrerciakzazkme dek
piastrelle di cerarnka.
L5 IX GLM2ENZE DI MAG4ZZlN0
Le gkxnze di magazzhm allafine de] 1995
““B.w$&&T****:l
Www%w%-yiy$ V,q
+4+w+i%.
~
. L k W. . . ..+w.xcw. . . . . . ,..
.J
Soo
ii
,
v
“
%’
“g
,
50
z
m 90
,.19a4a6
,
Smm rimdtate pari a quad 1~1 mtiioni di metri
quadrati, 20 miiiani in piii rispetta aI1’anno
precedence, e rappresentano B 26,86% dells
produziane dell’anno 1995 contro i! 25,69%
deil’armo precedence.
L6 IL FATTURATO
?1 fatturato relativo al prodotto finito &
amrncmtato nel 1995 a 8.471 rniliardi di !ke
,’,.$
(con UKI increinento del 16,16% WI 1994) cd
ripartiti 2334 rniIiardi di Ike iI fattumto ItaIia
(+ 11,48% d 1994) e S887 rnilkrdi di Iire d
fatturato export (+18S4% SUI19M). La cifra
d%ffari raggiunta eorriplessimente d.ti.settore, quasi 8.500 mhrdi, rapp~esenta d
nuovo piwo stori~, come giii rilevato per ia
p~dticm e per le wmdite.
-
Annows
‘ p -
~ ~ i ~
Lh?hrJq.
{ Mer=to
nazionde
Export
7Male
., ‘..:
,. !
viz %
9S/94
14.276 “ +m,43
16.2/37 . + 6 , 3 8
15.616
+7,97
p==i speciali (un miiione in piil nel 199.5
rispettci al 1994), a riportare i prezzi me.diri
valori piti ccmgrui e piti equilibrate rispetto
alh.ndamento cmscente dei costi produttiti.
Uncreme~to dei prezzi medi registrato sui
mereati esteri, tenendo eonto deIIa debok~
dells Era nei confronti delk principdi valute
straniere, non 8 invece da ritenersi deI tuttb
.mddisfacente: a tionte infatti di maggiori
vohmli esportati non si scum eonsegtiti corn: spondenti e cangrui increment di prezzo,
..-
H fatkato compiessivo del supporto per
terzi & ammontato nel 1995 a circa 198
rniikrdi dl I~e e queUQ deI1’impasto pez terzi a
154,7 rnik~di lire: da@ qwst~ da smmrmre
d fatt~rato relativo al prodotto ilnito gw tma
piil puntuale .vahizione
del giro di affari
..
aggregato deI setiore.
1.7 GLI INVESTTMEN’’I’I
I
4
fronte di un increment del 758% .ciei
volwni verlduti, i prbi medi smm saliti iel
1995 del 7,w?% rispetto dhrmo precedence.
In particolare i preizi scm aumentati de]
10,43% in Italia E del 638% rdl’estero.
h Italia le azkmde somy riuscite, grazie
all’hmakamento del mix venduto dovuto’
prkcipahnente alI’awnentato vohune . dei
A
8
R
9
i
*
WI 1995 I’industria eeramica ha investita
okre 814 miiiardi di he in beni capitali
(-+44,!3% rispetto d’annoprccedente) c482
m~i~di pr,evede hi irwestirne nel 1996.
~~~z@~~ivalofi degji ~~~mentidel 95
Sorlo .*Q .destinat prineipalmente all’incrementa del nurnero degii impkm~ al rinnovo
tbologico, al rnigIiorarnento deI1’a.mbiente
i
.-$!
Izl
IllB
El
Skui f&
Emu.@Q R&l
famamto
CzpQa
famfrato ttmdc
ed estemo, al rnigliormento del.l’effl=nza COmpkssiva attraverso I’uiterkn-e qua~cazione dei pr[dc$@ c d~i servi.zi. offe~i.
Contemporae&tite sono .eontinyati j pro,’.: J. ;
@ ~ irkte~on~ aziendak (a~uisizion.i
dipaecheti azionar~ fusioni, immrporazioni).
In particuke & possibile oggi affermare che
grazie a questi i.nvestimenti I’apparato teamIogico e Ia qualit~ delle aziende nel ko complesso ha.tma raggiunto liveili & asxduta
eCcellenza.
b prevision di investfiento relative al 96
Cbrotano un atteggiamento piii cohtenuto e
prudenziale che risp.ecchia sia Ie mutate mndizicini di rnercatmehe ii venir meno.di agevokzkmi fkcali Iegate”’aI reinvestimento degli
inttxrm
?l~ in azienda.
‘t-
1.8 LE PREvIsION PERIL 1996 . .
I-e previ.sioni formulate dd.le azknde
hanno indicato un incrernento del 10,21%
dells pmduzione COtnplessiv% come evidtmte
prdezione dei~andamento del 1995.
T;li indkaziuti non harm perh tenure conto
dell’invenione di tendem deIIa doma@~
ski in ItaIia che all’estero, che si e’ man.ifestata
con evidenza nei primi mesi de[ 1996.
Tidi mutate condizionl richiedono infatti
un :kihnerki&bento dei programti, giil
visibile M cxmtrcdlo operato sullaprodwi~ne
at~avexxo i] ricorso aUa CaS.sa Integrazione e
aIle fene anticipate da parte di un certo
mimero di aziende per contenere i liveI!i dei
magazzini.
Infatti & pib che mai neeessatio che il liwdlo
cWa capacit& produttin e la pmduzione
siano oggetto di approfondite rifiessioni da
parte ddle aziendeche dwranno esercitare k
massima attenzione aH’equiIlktia del rapporto ha doma.@a e offerta del mercato.
.
..
. ,