Smart data driven defect detection method for surface quality control in manufacturing

Abstract

The use of machine vision systems for quality control of reflective metal surfaces has increased over the years with new systems that combine higher resolution cameras and better illumination of inspected objects. With the advances in artificial intelligence pattern recognition of images, the integration of machine vision systems in a manufacturing line for accurate automatic classification of defects would work towards the application of a smart manufacturing concept. To investigate the feasibility of such integration, a vision system that combines a 4K camera and chromatic confocal technology was employed to analyze surfaces of copper parts after the laser machining process. By the application of three machine-learning algorithms (decision trees, random forest and multi-layer perceptron) on features extracted from the Sobel edge detector, segmentation of defects has been performed using the Weka segmentation plugin. A simple convolutional neural network (CNN) was also applied for the classification of defects. Later on, using smart data rather than big data, transfer learning (TL) has been successfully performed with retraining the mobilenet-v1 model, which is based on CNN. This lean learning process can be implemented in devices that are limited by their computation resources. The maximum average of validation accuracy achieved using TL trained over 500 epochs was 90.5%. Whereas for the simple CNN classification models, the best validation accuracy was achieved by a model with a batch size equal to ten and with 40% of validation data with an average equal to 98.7% over 500 epochs.

Microstructure Evolution Mechanism of AISI 1045 Steel Under High Speed Deformation

“…microstructure inspection of second shear zone of the chips during high-speed cutting of aiSi 1045 steel (a) Chip root microstructure detected by eBSD [8], (b) Chip root microstructure detected by Tem [10] angle micro-orientation angle interface (generally considered at 5℃) formed in the deformation process, based on the internal dislocation, gradually moves to the cell wall [24][25][26]. The time required to transform the cell wall into a large-angle microorientation interface is generally considered at 15℃ [27,28]. for dislocation cells with small angle micro-orientation angle, the average dislocation cell wall energy per unit length η is given by [29,30]:…”

AnomalySeg: Deep Learning-Based Fast Anomaly Segmentation Approach for Surface Defect Detection

“…Machine vision is a technology that deals with imaging-based inspection and analysis. For decades, this technique has been widely adopted in many industrial applications, e.g., metallic surface defect detection [5][6][7], steel defect detection [8][9][10], quality control in manufacturing [11][12][13], health monitoring of concrete structures [14][15][16][17][18], and condition assessment of product components [19][20][21][22], among others. According to the employed technology routes, the methods used above can be concluded into two categories [5]: image processing-based and machine learning-based.…”

An online color and shape integrated detection method for flexible packaging surface defects

“…Machine vision techniques are widely used in defect detection on product surfaces. The procedures of machine vision for surface defect detection are pre-processing, defect feature extraction, and defect classification [4][5][6]. Nagata et al [7] and Liu et al [8] used a deep learning approach that enabled good defect detection on conventional printed surfaces, while it was not suitable for surface defect detection of flexible packages.…”