This widely used artificial neural network employs the perceptron as simple processor. The model of the perceptron, proposed by Rosenblatt is as seen in Figure 5 (Appendix). In this diagram, the X represent the inputs and Y the output of the neuron. Each input is multiplied by the weight w, a threshold b is subtracted from the result and finally Y is processed by the application of an activation function f. The weights of the connection are adjusted during a learning phase using backpropagation algorithm
Published in Chapter:
ANN-Based Defects' Diagnosis of Industrial Optical Devices
Matthieu Voiry (University of Paris, France, and SAGEM REOSC, France), Véronique Amarger (University of Paris, France), Joel Bernier (SAGEM REOSC, France), and Kurosh Madani (University of Paris, France)
Copyright: © 2009
|Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch020
Abstract
A major step for high-quality optical devices faults diagnosis concerns scratches and digs defects detection and characterization in products. These kinds of aesthetic flaws, shaped during different manufacturing steps, could provoke harmful effects on optical devices’ functional specificities, as well as on their optical performances by generating undesirable scatter light, which could seriously damage the expected optical features. A reliable diagnosis of these defects becomes therefore a crucial task to ensure products’ nominal specification. Moreover, such diagnosis is strongly motivated by manufacturing process correction requirements in order to guarantee mass production quality with the aim of maintaining acceptable production yield. Unfortunately, detecting and measuring such defects is still a challenging problem in production conditions and the few available automatic control solutions remain ineffective. That’s why, in most of cases, the diagnosis is performed on the basis of a human expert based visual inspection of the whole production. However, this conventionally used solution suffers from several acute restrictions related to human operator’s intrinsic limitations (reduced sensitivity for very small defects, detection exhaustiveness alteration due to attentiveness shrinkage, operator’s tiredness and weariness due to repetitive nature of fault detection and fault diagnosis tasks). To construct an effective automatic diagnosis system, we propose an approach based on four main operations: defect detection, data extraction, dimensionality reduction and neural classification. The first operation is based on Nomarski microscopy issued imaging. These issued images contain several items which have to be detected and then classified in order to discriminate between “false” defects (correctable defects) and “abiding” (permanent) ones. Indeed, because of industrial environment, a number of correctable defects (like dusts or cleaning marks) are usually present beside the potential “abiding” defects. Relevant features extraction is a key issue to ensure accuracy of neural classification system; first because raw data (images) cannot be exploited and, moreover, because dealing with high dimensional data could affect learning performances of neural network. This article presents the automatic diagnosis system, describing the operations of the different phases. An implementation on real industrial optical devices is carried out and an experiment investigates a MLP artificial neural network based items classification.