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Intelligent Machine Tool for Autonomic Process Optimization

用于自主流程优化的智能机床

基本信息

批准号：
385522239
负责人：
Professor Dr.-Ing. Berend Denkena
金额：
--
依托单位：
Institut für Fertigungstechnik und Werkzeugmaschinen (IFW)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/385522239?language=en
关键词：
Intelligent Machine Tool Autonomic Process

项目摘要

Self-excited vibrations during machining are the main reason for limit cutting parameters. In general, an optimization of cutting parameters is performed by machine operators based on their know-how or by determining a stability lobe chart. Up to now, only the parameters cutting speed and axis feed are adapted automatically during the machining process. The potential of adapting depth and width of cut remains unexploited. Main goal of the project is to investigate an algorithm that adapts cutting parameters for an autonomic process optimization. The algorithm rates process parameters during machining by the means of sensor signals and a continuously expanding knowledge data-base. Machine integrated strain gauges and accelerometers serve to detect chatter and hence to determine the maximal material removal rate. Different chatter detection approaches are analyzed regarding the new measurement concept. Furthermore, the autonomous segmentation of tool path will be analyzed for a process parallel adaption of the depth and width of cut. Thus, the common approach will be extended and the process parameters (vc, vf, ae, ap) are used for optimization. Additionally, fundamental knowledge will be generated by creating decision rules required for the autonomic process optimization. Therefore, the project is a step forward towards autonomic machine tools.

加工过程中的自激振动是造成极限切削参数的主要原因。通常，机器操作员根据他们的技术诀窍或通过确定稳定性波瓣图来执行切割参数的优化。到目前为止，在加工过程中，只有切削速度和轴向进给参数是自动调整的。调整切割深度和宽度的潜力仍未开发。该项目的主要目标是研究一种自适应切割参数的算法，以实现自动工艺优化。该算法利用传感器信号和不断扩充的知识库对加工过程中的工艺参数进行评级。机器集成应变计和加速度计用于检测抖动，从而确定最大材料去除率。针对新的测量概念，分析了不同的颤振检测方法。此外，还将分析刀具路径的自主分割，以实现切割深度和宽度的并行适应。因此，将对常用方法进行扩展，并使用工艺参数(Vc、Vf、Ae、Ap)进行优化。此外，基础知识将通过创建自主流程优化所需的决策规则来生成。因此，该项目是向自主机床迈进的一步。