| dc.description.abstract | This paper discusses how to utilize Artificial Intelligence (AI) to assist in business development. As a company expands and increases production, the rising costs of raw materials and labor become burdensome. These costs inadvertently create pressure on the company. Poor product quality can lead to significant rework costs and damage the company′s reputation and image. To survive, companies must not only lower raw material costs and extensively use machinery to replace manual labor, but they also rely heavily on human resources for quality inspection. Frequent changes in production lines, personnel turnover, and inconsistent employee quality can lead to human errors. When products are returned, companies must incur additional costs for recovery and allocate more manpower to disassemble, reassemble, and re-inspect the products, which is time-consuming and labor-intensive.
If there was a technology that could replace manual visual inspection by simulating human vision to inspect the appearance quality of products, and it would not cause human errors due to factors such as fatigue of inspectors or talent turnover, which would then affect the company′s shipments? product quality. Therefore, the main motivation of this paper is to improve the quality of product inspection and save personnel costs, which can not only meet customer expectations, but also maintain a good image of the company.
Therefore, this thesis investigates how to use image recognition technology in deep learning (DL) and machine learning (ML) to train machines to recognize product appearances. This technology aims to replace human visual inspection with machine vision, ensuring product quality control. Since machines can operate 24/7, this reduces the risks of errors due to shift changes or inspector fatigue and improves inspection efficiency.
The goal of this research is to use deep learning (DL) and the YOLO algorithm to label product images and leverage computer processing power to enhance the consistency of product quality inspection. Through this experiment, we hope to replace manual inspection with this research′s findings, ultimately improving the quality of shipped products. | en_US |