in a manufacturing process, that can compare a defective portion in a selling store with a checked result portion in a factory by a development to calculate a correlation ratio, and quickly specify the cause of the failure in a manufacturing process based on judgment from the correlation ratio....
AI-based computer vision applications are helping to catch defects in the manufacturing process much faster and more effectively than traditional methods, enabling companies to increase yield, deliver products with consistent quality, and reduce false positives. In fact, 64% of manufac...
high cost and low yield of SiC manufacturing process are the most urgent issues yet to be solved. It has been shown that the performance of SiC devices is largely influenced by the presence of so-called killer defects, formed during the process of crystal growth. In parallel...
This paper presents a visual inspection system aimed at the automatic detection and classification of bare-PCB manufacturing errors. The interest of this CAE system lies in a twofold approach. On the one hand, we propose a modification of the subtraction method based on reference images that allow...
Manufacturing defects happen when an error in the manufacturing process deviates from the intended design. Example: A contaminated batch of medicine Warning defects hold the manufacturer, supplier, or vendor responsible for inadequate warning labels or instructions for use on a product. Examples: ...
Can arise from errors in specification, design, or manufacturing. 13 Detection Identified during software testing. Can be detected at any production or usage stage. 11 Impact Affects software performance and user experience. Impact varies widely depending on the nature of the defect. 10 Resolution ...
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defective samples, has been a long-standing problem that hinders the successful application of deep lea...
4.1.1 Steel manufacturing The steel defect detection method has recently received a lot of attention [92–94]. These approaches aim to find the crack on the metal surface to prevent any fault in the process. Since the available data on the cracks is not enough for training the model, Yun...
During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection ...
Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presen...