- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | SUN Yi, GAO Hongli, SONG Hongliang, YOU Zhichao. Study on Tool Condition-Integrated Online Optimization of Process Parameters[J].Journal of Southwest Jiaotong University.doi:10.3969/j.issn.0258-2724.20240578
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As demands for manufacturing quality and production efficiency continue to rise in modern industry, tool wear has emerged as a critical constraint affecting surface roughness. Traditional tool condition monitoring and process parameter optimization methods are often based on empirical models or static optimization strategies, limiting their adaptability to complex, dynamically changing, multivariable environments. In response, this study proposes an innovative approach integrating multi-scale distribution ratio (MSDR) with Bayesian multi-armed bandit (BMAB) for process parameter online optimization, incorporating real-time tool condition data into the optimization framework. Additionally, by combining Bayesian optimization and multi-armed bandit strategies, this method enables real-time adjustments to process parameters in dynamic manufacturing environments, effectively balancing exploration and exploitation to maximize machining efficiency. Compared to mainstream methods, MSDR demonstrates exceptional precision and stability in tool condition monitoring, achieving MAE, SMER, and RMSE values of 0.145, 0.258, and 0.194, respectively. BMAB also performs exceptionally in optimizing cutting efficiency and computational effectiveness, achieving
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