Neural network based object recognition system for manufacturing applications

The manufacturing environment these days is characterised by rapid change, posing new challenges and problems to the production and operations manager in the industry. Under this circumstances, process flexibility is fast becoming a major priority for many organisations as they attempt to deal with these changes. In response to the need for process flexibility, increasing attention is being given to integration of computing technologies with the manufacturing systems leading to the development of flexible manufacturing systems aided with high performance vision capabilities. The flexible manufacturing between various batches. Ideally, FMS should be able to accommodate the batch size of one component. This is possible if the programs for machining of different batches can be loaded instantly without operator intervention. An automatic component (object) recognition system, capable of identifying the component loaded on any given machine is required for meeting this objective. Existence of such a system provides an additional latitude in the scheduling of FMS to reduce the down time and improve the utilisation. This requires development of high performance industrial vision systems, which is quite a challenging task, as vision is a complex and computational intensive task, and the typical manufacturing environment is characterised by several different parts with simple or complex geometric descriptions, partially occluded parts, or parts with surface anomalies, blemishes and varying shapes. In this paper the recognition performance of the neural network based classifiers is reported for an industrial object recognition system being developed at the author’s laboratory. The intelligent object recognition system being developed has a modular structure with each module dedicated to different levels of vision in the vision hierarchy. A comparison of two different cases where the proposed neural network based classifiers perform much better than the general statistical classifiers is reported. The first case considered is that of the one degree of freedom tools where the backpropagation neural net has the better recognition performance and the second case considered is that of partially occluded objects where the Hopfield neural net yields better performance.