ANN-Based Defects' Diagnosis of Industrial Optical Devices

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
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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.
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Defects’ Detection And Classification

The suggested diagnosis process is described in broad outline in the diagram of Figure 1. Every step is presented, first detection and data extraction phases and then classification phase coupled with dimensionality reduction. In a second part, some investigations on real industrial data are carried out and the obtained results are presented.

Key Terms in this Chapter

Data Dimensionality Reduction: Data dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. The goal is to find the important relationships between parameters and reproduce those relationships in a lower dimensionality space. Ideally, the obtained representation has a dimensionality that corresponds to the intrinsic dimensionality of the data. Dimensionality reduction is important in many domains, since it facilitates classification, visualization, and compression of high-dimensional data. In our work it’s performed using Curvilinear Distance Analysis

Artificial Neural Networks: A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis

C lassification: Affectation of a phenomenon to a predefined class or category by studying its characteristic features. In our work it consists in determining the nature of detected optical devices surface defects (for example “dust” or “other type of defects”).

Data Raw Dimension: When data is described by vectors (sets of characteristic values), data raw dimension is simply the number of components of these vectors

Detection: Identification of a phenomenon among others from a number of characteristic features or “symptoms”. In our work, it consists in identifying surface irregularities on optical devices

Backpropagation algorithm: Learning algorithm of ANNs, based on minimising the error obtained from the comparison between the outputs that the network gives after the application of a set of network inputs and the outputs it should give (the desired outputs)

MLP (Multi Layer Perceptron): 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

Data Intrinsic Dimension: When data is described by vectors (sets of characteristic values), data intrinsic dimension is the effective number of degrees of freedom of the vectors’ set. Generally, this dimension is smaller than the data raw dimension because it may exist linear and/or non-linear relations between the different components of the vectors

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