On-machine surface defect detection using light scattering and deep learning

Design of an optical linear-discriminant filter: optimization for enhancement of filter transmittance and discrimination accuracy

To discriminate fine concave and convex defects on a dielectric substrate, an optical machine learning system is proposed. This system comprises an optical linear-discriminant filter (OLDF) that performs linear discriminant analysis (LDA) of the scattered-wave distribution from target samples. However, the filter output from the OLDF is considerably weak and cannot be measured experimentally. Therefore, an algorithm is also proposed to improve the discrimination accuracy and filter transmittance. The designed filter is validated using a rigorous optical simulator based on vector diffraction theory. We also analyze and discuss a mechanism that provides high transmittance with high discrimination accuracy.

Machine vision system based on a coupled image segmentation algorithm for surface-defect detection of a Si3N4 bearing roller

Defect detection is a critical way to ensure quality for silicon-nitride-bearing rollers. To improve detection efficiency and precision for silicon-nitride-bearing roller surface defects, in this paper, a novel machine vision system for the detection of its surface defects is designed. This method combines image segmentation and wavelet fusion to extract features from an image. In turn, the features are used in a classifier based on the K-nearest neighbor for defect classification. The optimized image segmentation algorithm that is combined with wavelet fusion is the innovation of the proposed method. It is evaluated using different defect images acquired by the machine vision system. Our experiments show that the proposed machine vision system's precision in anomaly detection of the silicon-nitride-bearing roller surface can achieve 98.5%; further, its classification precision of various defects is greater than 91.5%. It has resulted in a solution for the automatic identification of the silicon-nitride-bearing roller surface defects.

AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)

Sheet metal-based products serve as a major portion of the furniture market and maintain higher quality standards by being competitive. During industrial processes, while converting a sheet metal to an end product, new defects are observed and thus need to be identified carefully. Recent studies have shown scratches, bumps, and pollution/dust are identified, but orange peel defects present overall a new challenge. So our model identifies scratches, bumps, and dust by using computer vision algorithms, whereas orange peel defect detection with deep learning have a better performance. The goal of this paper was to resolve artificial intelligence (AI) as an AI landing challenge faced in identifying various kinds of sheet metal-based product defects by ALDB-DL process automation. Therefore, our system model consists of multiple cameras from two different angles to capture the defects of the sheet metal-based drawer box. The aim of this paper was to solve multiple defects detection as design and implementation of Industrial process integration with AI by Automated Optical Inspection (AOI) for sheet metal-based drawer box defect detection, stated as AI Landing for sheet metal-based Drawer Box defect detection using Deep Learning (ALDB-DL). Therefore, the scope was given as achieving higher accuracy using multi-camera-based image feature extraction using computer vision and deep learning algorithm for defect classification in AOI. We used SHapley Additive exPlanations (SHAP) values for pre-processing, LeNet with a (1 × 1) convolution filter, and a Global Average Pooling (GAP) Convolutional Neural Network (CNN) algorithm to achieve the best results. It has applications for sheet metal-based product industries with improvised quality control for edge and surface detection. The results were competitive as the precision, recall, and area under the curve were 1.00, 0.99, and 0.98, respectively. Successively, the discussion section presents a detailed insight view about the industrial functioning with ALDB-DL experience sharing.

Advances in optical metrology and instrumentation: introduction

Optical measurement and characterization are two of the pillars of metrology. The ability to measure precisely with high dynamic range and accuracy betters our understanding of nature and the universe. In this feature issue, we present a collection of articles that delves into the fundamental techniques used to advance the field.