Apparatus and Method of Defect Detection for Resin Films

Abstract

A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films.Appl. Sci. 2020, 10, 1206 2 of 23 3.In some cases, the manufacturer may want to tolerate no defect larger than 25 µm. However, human vision is limited to objects larger than approximately 50 µm; this limit is not acceptable for high-quality products. 4.Defect detection is not immediate. Using human vision for the inspection process is considerably time consuming and inefficient. The average number of plastic resin films that can be checked by a person in a day is approximately 12 to 15.Automatic procedures, including machine vision-based methods of defect detection, have gained considerable popularity for their high speed, high precision, and real-time performance. Several strategies for defect detection have been explored in numerous studies. These machine vision-based methods are used in the inspection of many industrial products, such as metal [1], fabric [2], steel [3], car surfaces [4], and light-emitting diode chips [5]. The methods of surface defect detection can be divided into three types, as depicted in Figure 1, namely traditional image processing, feature extraction with machine learning, and deep learning methods. Appl. Sci. 2020, 10, 1206 2 of 23 miss some defects. Furthermore, it is nearly impossible to standardize human vision; each inspector's eyesight will have its own peculiarities. 3. In some cases, the manufacturer may want to tolerate no defect larger than 25 μm. However, human vision is limited to objects larger than approximately 50 μm; this limit is not acceptable for high-quality products. 4. Defect detection is not immediate. Using human vision for the inspection process is considerably time consuming and inefficient. The average number of plastic resin films that can be checked by a person in a day is approximately 12 to 15.Automatic procedures, including machine vision-based methods of defect detection, have gained considerable popularity for their high speed, high precision, and real-time performance. Several strategies for defect detection have been explored in numerous studies. These machine vision-based methods are used in the inspection of many industrial products, such as metal [1], fabric [2], steel [3],...

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

“…There can be found multiple application of artificial-intelligence-aided computer vision in a variety of automated manufacturing inspection cases, such as steel [ 20 , 21 , 22 ], wood [ 23 , 24 , 25 , 26 ], and resin/plastic [ 27 , 28 , 29 ]. The mentioned investigations utilize deep neural networks as an algorithm to distinguish defects.…”

“…A similar approach (regarding small neural network architecture) can be found in wood defects detection and classification investigation [ 24 ]. A minimalistic convolutional neural network can be seen in resin defection research [ 27 ], where the LeNet-5-like model is being utilized. Moreover, light-weight segmentation approaches are investigated by Huang et al [ 41 ], where only one step of upscaling is employed and another enhancement, atrous spatial pyramid pooling (ASPP) [ 3 ], is utilized.…”