A ANN-Based Defects ’ Diagnosis of IndustrialOptical Devices (original) (raw)
Related papers
Data Dimensionality Reduction for Neural Based Classification of Optical Surfaces Defects
International Journal of Computing
A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterization in products. This challenging operation is very important since it is directly linked with the produced optical component’s quality. A classification phase is mandatory to complete optical devices diagnosis since a number of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on the behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can decrease the problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evaluate their impact on classification tasks performances.
Proceedings of the 2nd International Workshop on Artificial Neural Networks and Intelligent Information Processing
A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects detection and characterization in products. This challenging operation is very important since it is directly linked with the produced optical component's quality. A new scratches and digs defects detection and characterization method exploiting Nomarski microscopy issued imaging has been developed. The items detected using this high-performance approach can correspond to real defects on the structure but some dusts and cleaning marks are detected too. Thus, a classification phase is necessary to complete optical devices diagnosis. In this paper, we describe a data extraction method, which supplies pertinent features from raw Nomarski images issued from industrial process. Then we apply this method to construct a database from real images. Finally we analyse the pertinence of features and the complexity of obtained database by clustering operation using an unsupervised Self Organizing Maps technique.
Defect Detection and Classification of Optical Components : A Review
Optics engineering is a field that is rapidly changing, inventive, and developing. To address the growing demand for optical components, product quality requirements must be maintained. Automatic optical inspection (AOI) is a non-destructive technology used in product quality inspection. This technology is regarded reliable and may be used to replace human inspectors who are tired and bored while doing inspection chores. Hardware and software configurations make comprise a fully automated optical inspection system. The hardware setup is responsible for acquiring the digital picture, while the software portion implements an inspection algorithm to extract the attributes of the collected images and categories them as defected or non-defective depending on the user requirements. To distinguish between faulty and excellent items, a sorting system might be utilized. This study examines the numerous AOI systems utilized in the electronics, microelectronics, and optoelectronics sectors. The imaging, pre-processing, feature extraction, and classification technologies used to detect flaws in optical components are reviewed.
2005
The aim of this work is to define a procedure to develop diagnostic systems for Printed Circuit Boards, based on Automated Optical Inspection with low cost and easy adaptability to different features. A complete system to detect mounting defects in the circuits is presented in this paper. A lowcost image acquisition system with high accuracy has been designed to fit this application. Afterward, the resulting images are processed using the Wavelet Transform and Neural Networks, for low computational cost and acceptable precision. The wavelet space represents a compact support for efficient feature extraction with the localization property. The proposed solution is demonstrated on several defects in different kind of circuits.
Artificial neuronal networks for optical inspection in PCB quality control
Visión electrónica, 2017
This paper is the result of the research work on the application of an artificial neural network algorithm applied in decision making in the process of AIO (Automatic Optical Inspection) for quality control from an electronic prototyping company, generating models for the assurance of Quality in the PCB (Printed Circuit Board) product, covering the fields of decision making, quality management, production processes, neural computer systems and artificial vision among others. It is intended to develop an algorithm of artificial neural networks that provides an approach to human recognition and perception when performing a quality inspection of the final product, based on image analysis and recognition. It is presented the theoretical concepts explored and the results obtained. Initially a problem definition was made to model, then the data processing was performed, the artificial neural network model was selected to be applied, then the relevant adjustments made to the model to finally obtain a simulation and validation of the same.
Frontiers in Manufacturing Technology
This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process...
Locating and Classifying Defects with Artificial Neural Networks
Applied Mechanics and Materials, 2008
Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1x1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.
Neural network diagnosis for visual inspection in printed circuit boards
In this paper we present an Automatic Optical Inspection system to diagnose Printed Circuit Boards mounted in Surface Mounting Technology. The diagnosis task is handled as a classification problem with a neural network approach. The Printed Circuit Board tested images are preprocessed by means of several methods to reduce the amount of data to feed to the neural networks. We compare the results obtained in the diagnosis for all methods. The Automatic Optical Inspection system seems to be a good solution in an industrial application because of the low cost, very fast diagnosis and easiness to set-up and handle.
Automated visual fault inspection of optical elements using machine vision technologies
Journal of Applied Engineering Science, 2018
Light-emitting diode (LED) lenses are one kind of common optical elements applied in many modern electronic devices. The LED lens with textured and uneven surface is hard to inspect appearance faults. This research suggests a wavelet packet transform-based partial least squares method to inspect visual faults of optical lenses with textured and uneven surfaces. Three major procedures are conducted to complete the process of fault detection. Firstly, a testing image is transformed to wavelet pack domain and the wavelet characteristics of the sub-band images are extracted. Secondly, the partial least squares scheme is used to multivariate transform with wavelet characteristics to obtain latent images. Thirdly, the latent images are fi tted by a regression model to produce a predicted image. After comparing with the original image, we can obtain the residual image where the appearance faults have been separated. Thus, the intricate faults embedded in the complicated appearances of optical lenses could be precisely identifi ed by the suggested method. The effectiveness and accuracy of the developed method are confi rmed by expert assessments, as well as by comparative analysis with the known methods in the fi eld of spatial localizations and classifi cation effects of fault inspection.