Dynamic Optimization of the Grinding Process in Batch Production

This paper presents a novel dynamic optimization framework for the grinding process in batch production. The grinding process exhibits time-varying characteristics due to the progressive wear of the grinding wheel. Nevertheless, many existing frameworks for the grinding process can optimize only one cycle at a time, thereby generating suboptimal solutions. Moreover, a dynamic scheduling of dressing operations in response to process feedback would require significant human intervention with existing methods. We propose a unique dynamic programming - evolution strategy (DP-ES) framework to optimize a series of grinding cycles depending on the wheel condition and batch size. In the proposed framework, a dynamic programming module dynamically determines the frequency and parameter of wheel dressing while the evolution strategy (ES) locates the optimal operating parameters of each cycle subject to the constraints on the operating ranges and part quality. A case study based on experimental data is conducted to demonstrate the advantages of the proposed method over conventional approaches.