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Industrial Processes

Surface inspection goes on line

The cost of poor quality in the steel industry is continuously increasing. To address one aspect of this problem in the manufacture of flat products, an Italian research centre has devised a prototype system for the real-time inspection of stainless steel strip. Poor quality in this industry can lead to costly in-plant repairing, product downgrading and the risk of claims, particularly in light of the present laws concerning quality assurance, which comes back to the supplier. A digital image of the strip obtained on the production line is analysed for defects which are then classified into at least six types by one of three image recognition procedures. Applications are foreseen in a wide range of processes involving the manufacture of continuous material of uniform surface appearance, such as in the sheet metal, papermaking and plastics industries.

Many manufacturing processes produce continuous strips of material - steel and paper making are examples - in which the product would be spoiled, and even rendered worthless, by surface blemishes occurring during manufacture. Centro Sviluppo Materiali (CSM), a materials research centre based in Rome, has long been investigating methods of automating the surface inspection of steel strip so that faults can be identified swiftly and efficiently.
In an earlier steel research project, CSM collaborated with steel maker AST to devise an automated method of examining stainless steel strip emerging from the annealing and pickling line at the Terni steelworks in Umbria. That pilot project, completed in February 1994, was so successful that CSM was awarded further funding to develop a prototype suitable for commercial application.
The prototype can inspect and analyse a complete steel strip as fast as it can be manufactured. It consists of two main parts, the Remote Acquisition Module and the Surface Inspection Module.

Defect detection

The Remote Acquisition Module is mounted close to the production line. It contains a special camera which scans a line of 2,048 points across the width of the strip producing an image in 256 shades of grey. As the strip passes the camera, successive lines are added to build up a complete image. The resolution of the image depends on the size and speed of the steel strip, but is typically half a millimetre to one millimetre.
If required, a second camera may be used to view the strip from a different angle, as this usually makes it easier to detect the complete range of defects.
The image is corrected for non-uniform illumination within the module and then the digitised data is sent by a fibre-optic link to the second major component, the Surface Inspection Module. Because the amount of image processing required here is huge, much of the computation in the module is carried out on hard-wired boards rather than by a program held in memory. This is the only way the surface inspection can keep pace with the speed of strip production.
As the manufactured surface is designed to be of uniform appearance, defective areas generally show up where points in the image ('pixels') differ in brightness from their surroundings. The first stage of processing is a map of the strip showing individual defective pixels.
Next, neighbouring defective pixels are grouped into objects whose 'features' which are defined by 14 parameters including their geometric properties (dimensions, area and perimeter) and their optical characteristics (such as the maximum and minimum brightness). As many as 1,024 defects can be handled at once. The parameters of each feature are then passed to a classification unit which uses three different methods to decide what kind of defect is present. For steel strip, for example, the defects could be scratches, dents, bumps, stains, manufacturing flaws and so on. Defects arising from impurities are particularly important to recognise because most stainless steel products are sold mainly by appearance, rather than their mechanical properties. (For example, cutlery, pots and trays.) In addition, impurities can break the metallic structure and create a weak point mechanically, or they could affect the rust-free feature of stainless steel.

Defect classification

The first classification method, widely used in image-recognition applications, simply compares the set of parameters with a standard set for each type of defect to determine which class they fall into. It is not very efficient in distinguishing defects where the permitted ranges of parameters may overlap with each other.
Another method, one of several possible statistical approaches, compares each defect with a sample of defects from a database and calculates the probability of a match to each one. The class with the highest probability is chosen. This method achieves 70-75% correct classification.
A third method uses a neural network, a simple computer which can be trained to recognise different kinds of defect by being shown many examples. Although its success rate is only slightly better than the statistical method, it works much faster.
All three methods are available to the operator, who can view the results displayed on a standard PC and monitor. The final result is a map of the whole strip, showing the location of each defect marked by a symbol according to its type.
The system has been tested on a large database of known defects, and is now 75-80% successful in distinguishing six classes of defect on stainless steel strip. CSM is looking to improve this figure by 'context analysis' which examines the relationship between each defect and neighbouring defects. The classification efficiency would need to reach 90% or higher to make it commercially attractive.

Still a role for humans

The original intention of a completely automated inspection system, however, has been modified in the light of experience. It will not be practicable in the near future to do away with visual inspection, because it is not yet possible to completely replace human skill and experience in recognising the many forms of defects in the steel.
How useful would such a system be in practice? While defects on steel strip cannot normally be remedied, the inspection system does provide rapid warning of problems in the manufacturing process which can be dealt with before too much steel is wasted. It would be particularly valuable at early stages of manufacture, where faults can be identified before the material is passed to subsequent stages. In addition, automatic inspection of the finished strip could be combined, for example, with selective cutting, so that only the best sections of strip are used and defective portions rejected.
CSM envisages applications for the inspection system in any industrial process in which sheet material of uniform appearance is produced in continuous strips, such as papermaking, plastics and metals other than steel. Of course, the nature of the defects will be different from process to process, but the principle remains the same.
CSM is already negotiating with a potential partner to commercialise the system. There are several on-line inspection systems on the market, but none is efficient at classifying defects correctly. The classification algorithm is the most innovative component of the CSM system and could even find market potential in its own right.



Project Title:  
Prototype system for on-line surface inspection

Industrial and Materials Technologies (BRITE-EURAM/CRAFT/SMT)

Contract Reference: ECSC 7210-ZZ-611

Cordis DatabaseFor more information on this project,
go to the CORDIS Database Record