机床离散事件动力学与加工动力学的集成：建模、分析与算法
Integration of discrete-event dynamics and machining dynamics for machine tool: modeling, analysis and algorithms
摘要：
加工动力学研究通过提供稳定的主轴转速和切削深度组合为加工操作奠定了坚实的基础。此外，机器学习已被应用于预测刀具寿命作为切削速度的函数。然而，现有的研究并没有考虑离散事件动态的机械加工车间，即，在各种实际生产要求下，机床需要加工一系列排队的零件。本文讨论了离散事件动力学和加工动力学的集成，以实现加工成本的节约。针对所研究的机床集成最优化问题，提出了一种基于学习的费用函数。提出的费用函数利用不同稳定切削速度下的刀具寿命预测值进一步优化机床速度选择，以处理离散事件动态加工车间。然后，根据实际生产要求，建立了考虑成本、完工时间和交货期的相关集成最优化问题的数学最优化模型。数值结果表明了本文方法的有效性和实用价值。
英文摘要：
Machining dynamics research lays a solid foundation for machining operations by providing stable combinations of spindle speed and depth of cut. Furthermore, machine learning has been applied to predict tool life as a function of cutting speed. However, the existing research does not consider the discrete-event dynamics in machine shop, i.e., the machine tool needs to process a series of parts in queue under various practical production requirements. This paper addresses the integration of discrete-event dynamics and machining dynamics to achieve cost savings in machining. We first propose a learning-based cost function for the studied integrated optimization problem of machine tool. The proposed cost function utilizes the predicted tool life under different stable cutting speeds for further optimizing speed selection of machine tool to deal with the discrete-event dynamics in machine shop. Then, according to the practical production requirements, we develop several mathematical optimization models for the related integrated optimization problems with the consideration of cost, makespan and due date. Numerical results show the effectiveness of our proposed methods and also the potential to be used in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
