EICF – An Enhanced Intelligent Cloud-based Framework for Automated Product Quality Assurance

Quality is the most important issue for all producers in every manufacturing system, regardless of the products. Quality management is a set of tools and processes that can help a manufacturing unit's overall system performance. With the continuously changing company environment and fierce market competition, quality tools and continuous improvement (CI) has become critical for long-term success and embracing difficulties. A combination of increasing customer expectations and technical improvements has increased the complexity of industrial systems. Stronger quality control systems must be purchased, especially when a small manufacturing error could result in a big loss for the company. A non-automated quality control system necessitates a large human staff, which increases costs and lowers reliability. Consequently, this paper presents an outline of quality control for products that exhibit automated behavior. The paper also explores a methodology of using cloud computing to automate quality assurance. As cloud computing has risen to prominence in recent years. A growing number of manufacturers are considering moving their quality management to a cloud-based quality system. Therefore, in today's ever-changing and unpredictable manufacturing environment, the cloud's cost benefits, power, and agility have become critical to survival. Therefore, this paper has presented a service-based proposal system for visual quality assurance using machine learning algorithms and cloud computing. The model is evaluated in terms of response delay and accuracy of defect detection in manufacturing parts. The average response delay was evaluated to be approx. 8sec and the average accuracy of the model were approx. 93%.