An Improved Intelligent Cloud-based Structure for Automated Product Quality Control

Abstract

Regardless of the products, quality is the most crucial concern for all manufacturers in any manufacturing system. Quality management is a collection of procedures and instruments that can improve the overall system performance of a manufacturing facility. The dynamic nature of today's business climate and intense rivalry in the market have made quality tools and continuous improvement (CI) essential for long-term success and overcoming obstacles. The methodology of automating quality assurance with cloud computing is also explored in this study. Given how popular cloud computing has become lately. An increasing number of manufacturers are thinking about switching to a cloud-based quality system for their quality management. Because of this, the cloud's power, agility, and cost advantages are now essential for surviving in today's dynamic and uncertain manufacturing environment. Thus, utilizing machine learning methods and cloud computing, this work has provided a service-based proposal system for visual quality assurance. The model's accuracy in detecting defects in manufactured parts and response time are assessed. The model's average accuracy was estimated to be approximately 93%, while the average reaction delay was estimated to be approximately 8 seconds.