Towards Data-Driven Material Removal Rate Estimation in Bonnet Polishing
Towards Data-Driven Material Removal Rate Estimation in Bonnet Polishing
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DOI：
10.1109/iccma59762.2023.10375024
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Michal Darowski;Muhammad Faisal Aftab;Hongyu Li;David Walker;Guoyu Yu;Chenghui An;C. W. Omlin
中科院分区：
文献类型：
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作者：
Michal Darowski;Muhammad Faisal Aftab;Hongyu Li;David Walker;Guoyu Yu;Chenghui An;C. W. Omlin
超精密抛光是一种复杂的工艺，涉及到纳米级精度的表面抛光。尽管大部分过程是使用计算机数控（CNC）机器进行的，但完全自主制造这种精密零件目前尚不可行。工艺的复杂性、缺乏完美的确定性、抛光过程中的表面不可接近性以及对广泛的专业知识的需求都是超精密制造的重大挑战。在这项工作中，我们提出了一个系统设计，用于监测超精密阀盖抛光过程中的参数，如抛光浆的化学性质或过程中的刀具力。我们还分享了在系统验证阶段遇到的一些初步发现和挑战，这些需要进一步考虑。此外，我们概述了在阀盖抛光的材料去除率估计中实现机器学习的计划。
Ultra-precision polishing is a complex process that involves polishing surfaces with nanometre-level accuracy. Even though much of the process is performed using computer numerically controlled (CNC) machines, fully autonomous manufacturing of such fine parts is currently not feasible. The complexity of the process, lack of perfect determinism, surface inaccessibility during the polishing process, and the need for extensive expert knowledge all present significant challenges in ultra-precision manufacturing. In this work, we present a system design for monitoring parameters in the ultra-precision bonnet polishing process, such as the chemical properties of the polishing slurry or tool forces during the process. We also share some initial findings and challenges encountered during the validation stage of the system, which require further consideration. Additionally, we outline our plan for implementing machine learning in material removal rate estimation for bonnet polishing.