Practical Batch Quality Trend Analysis Based on Regression Algorithms

Batch production enables the manufacture of things in large quantities in a flexible manner while lowering manufacturing costs and improving quality. In modern industry, prediction and monitoring of quality trends are critical for batch production quality management. Correspondingly this study utilizes linear regression and exponential regression algorithms to anticipate the future values of industrial data, as well as employs trigonometric function regression methods to compute and construct periodic curves. Numerical results show that with appropriately selected parameters, our proposed methods can accurately predict batch production quality and give early warning of production problems.