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Simulation Based Parameterization for Process Monitoring of Machining Operations

机译:基于仿真的加工操作过程监控参数化

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摘要

Process monitoring can prevent machine and tool failure in metal-cutting. A successful process monitoring of cutting processes depends on reliable monitoring limits for the process. In industrial applications these limits have to be generated in a learning phase during a ramp-up process. In order to enable process monitoring for single batch production without a learning phase, this paper describes a simulation based approach for generating reference data to set process limits. As a foundation for calculation of monitoring limits a position-based process simulation has to be established. In a first step an approach of modeling material removal is evaluated to check whether it fits the application for parameterizing the process monitoring. In this context the potentials of a process simulation for calculating process limits are clarified. Additionally the quality of data generated by this kind of simulation is discussed. In a second step a method is described to implement machine properties by a virtual machine tool within a simulation of material removal. For that purpose a method to use actual data of axis position and tool within the simulation of material removal is necessary. With these data a way-based simulation of material removal can generate reference parameters for monitoring limits instead of using data from a learning phase during the ramp-up process. By using position data of a virtual machine tool a reliable source for the actual position of all axes enables the position-based simulation to perform material removal in a more accurate way.
机译:过程监控可以防止机器和刀具在金属切割中进行故障。切割过程的成功过程监测取决于该过程的可靠监测限制。在工业应用中,必须在增速过程中在学习阶段产生这些限制。为了在没有学习阶段的情况下启用单批量生产的过程监控,本文介绍了一种基于模拟的方法,用于生成参考数据以设置过程限制。作为监测限制计算的基础,必须建立基于位置的过程模拟。在第一步中,评估模拟材料的方法,以检查它是否适合参数化过程监视的应用。在这种情况下,阐明了计算过程限制的过程模拟的电位。另外,讨论了这种模拟产生的数据的质量。在第二步骤中,描述了一种方法来通过虚拟机床在拆卸材料的模拟中实现机器性质。为此,需要一种使用轴位置和工具的实际数据的方法,是必要的材料移除的模拟中。利用这些数据,材料拆卸的基于方式的方式可以生成用于监视限制的参考参数,而不是在加速过程中使用来自学习阶段的数据。通过使用虚拟机工具的位置数据,所有轴的实际位置的可靠源使基于位置的仿真能够以更准确的方式执行材料去除。

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