A Novel Black Box Process Quality Optimization Approach based on Hit Rate
This addresses quality optimization in industrial processes like steel production, but it is an incremental method combining existing techniques.
The paper tackles the non-convex problem of optimizing hit rate for process product quality in industrial processes by proposing a data-driven quasi-convex approach that converts it into convex feasible problems, achieving improvements of at least 41.11% and 31.01% in hit rates on real datasets.
Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models.