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30 January - 3 February 2012
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Tata Steel: Image Recognition of Shape
Defects in Hot Steel Rolling
Leo Kampmeijer
Tata Steel Research, Development & Technology / IJmuiden Technology Centre
Tata Steel IJmuiden
Tata Steel is the 2nd largest steel producer in Europe with major manufacturing
sites in the UK, the Netherlands, Germany, France and Belgium. The IJmuiden site
in The Netherlands host a fully integrated production process in which coils of coated steel strips
are produced out of raw materials such as iron ore and coal.
Hot Strip Mill
One of the key installations on the site is the Hot Strip Mill. Slabs with a length of 10 m and a thickness of 20 cm, weighing over 20 tons, are heated up to a temperature of 1200 ºC. Along a trajectory of about half a kilometre they are rolled in consecutive steps into strips of up to 1000 m long and 2 mm thin. After the final reduction step, where the strips can reach a speed of 20 m/s, they are cooled to 700 ºC and coiled in less than 60 seconds. In this way 250 000 coils leave the hot strip mill each year for further processing and finally end up in construction, in lifting & excavating machines, in consumer goods such as white goods (refrigerators and stoves) and in the automotive and packaging industries.
Tail Pinching
The final reduction steps, where the strips can reach a speed over 20 m/s are very critical. Any errors in the gap settings of the mill or other process errors can lead to defects in the shape of the strip. Especially at tailing out of the strip such shape defects may lead to pinches, damaging the strip as well as the rolls.
Tail pinching is clearly visible by ripples in the strip. In some case the strip surface is torn apart. It is not exactly known in what circumstances such pinches occur. To determine a statistical relation, and ultimately a causal relation between certain process conditions and pinching, it could be very useful to detect these pinches automatically. Once the mechanism is better understood an online detection system might also be used to modify the process in order to prevent more pinches.
Commercially available surface inspection systems need to be trained with categorized images. Once trained the system computes the likelihood that a certain defect corresponds to a certain category on the basis of their dimensions, orientation on the strip surface and the grey scale distribution. In this way simple defects are detected quite successfully. More complex defects are often not classified correctly. This also holds for pinches.
Problem Description
The goal of the assignment is to create a method or algorithm that with a large collective of grey scale images can determine for individual strips
• if tail pinching has occurred or not,
• the location of the pinches relative to the image frame,
• if the surface is merely rippled at the pinch location or if also holes are present.
One of the difficulties of this problem is that pinches can have different appearances which are on one way or the other similar to other shape defects such as wavy edges, centre buckles, loops etc. It may even be that such shape defects and pinches occur simultaneously. It would be nice if such shape defects could also be recognized.
