利用物理贝叶斯机器学习预测铣削过程中的颤振和加工阻尼

IF 4.6 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Vahid Ostad Ali Akbari , Andrea Eichenberger , Konrad Wegener
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引用次数: 0

摘要

铣削操作中的颤振稳定性是一个复杂的现象,在制造业中造成了严重的生产率问题,但却缺乏一种可在车间实施的解决方案。本文采用物理支持的贝叶斯机器学习方法,并将加工阻尼对加工稳定性的潜在影响纳入其中。利用基于奈奎斯特稳定性准则的似然函数,学习系统监控任意切割过程中工艺的实际稳定性状态,并完善结构动力学、切割力系数以及工艺阻尼中的基础模型参数不确定性。该框架可在训练数据有限的情况下运行,并向机器操作员显示稳定性预测中的剩余不确定性。实验案例研究表明了所提方法的有效性,并强调了考虑某些立铣刀加工阻尼的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-supported Bayesian machine learning for chatter prediction with process damping in milling
Chatter stability of milling operations is a complicated phenomenon causing serious productivity issues in the manufacturing industry, yet a shop-floor implementable solution is lacking. This paper follows a physics-supported Bayesian machine learning approach and incorporates the potential effect of process damping on the stability of the process. Using a likelihood function based on the Nyquist stability criterion, the learning system monitors the actual stability state of the process during arbitrary cuts and refines the underlying model parameter uncertainties in the structural dynamics, cutting force coefficients, as well as the process damping. The framework can operate with limited training data and display the remaining uncertainties in stability predictions to the machine operator. Experimental case studies show the effectiveness of the proposed method and highlight the importance of considering process damping for certain endmills.
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来源期刊
CIRP Journal of Manufacturing Science and Technology
CIRP Journal of Manufacturing Science and Technology Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
6.20%
发文量
166
审稿时长
63 days
期刊介绍: The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.
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