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Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks
arXiv - CS - Systems and Control Pub Date : 2021-06-18 , DOI: arxiv-2106.09719
Chao Sun, Javier Dominguez-Caballero, Rob Ward, Sabino Ayvar-Soberanis, David Curtis

Accurate prediction of machining cycle times is important in the manufacturing industry. Usually, Computer Aided Manufacturing (CAM) software estimates the machining times using the commanded feedrate from the toolpath file using basic kinematic settings. Typically, the methods do not account for toolpath geometry or toolpath tolerance and therefore under estimate the machining cycle times considerably. Removing the need for machine specific knowledge, this paper presents a data-driven feedrate and machining cycle time prediction method by building a neural network model for each machine tool axis. In this study, datasets composed of the commanded feedrate, nominal acceleration, toolpath geometry and the measured feedrate were used to train a neural network model. Validation trials using a representative industrial thin wall structure component on a commercial machining centre showed that this method estimated the machining time with more than 90% accuracy. This method showed that neural network models have the capability to learn the behavior of a complex machine tool system and predict cycle times. Further integration of the methods will be critical in the implantation of digital twins in Industry 4.0.

中文翻译:

加工周期时间预测:使用神经网络对机床进给率行为进行数据驱动建模

准确预测加工周期时间在制造业中很重要。通常,计算机辅助制造 (CAM) 软件使用来自刀具路径文件的指令进给率使用基本运动学设置来估计加工时间。通常,这些方法不考虑刀具路径几何形状或刀具路径公差,因此大大低估了加工循环时间。消除了对机器特定知识的需求,本文通过为每个机床轴构建神经网络模型,提出了一种数据驱动的进给率和加工周期时间预测方法。在这项研究中,由指令进给率、名义加速度、刀具路径几何形状和测量进给率组成的数据集用于训练神经网络模型。在商用加工中心上使用具有代表性的工业薄壁结构部件进行的验证试验表明,该方法估计加工时间的准确度超过 90%。该方法表明,神经网络模型能够学习复杂机床系统的行为并预测循环时间。这些方法的进一步集成对于在工业 4.0 中植入数字孪生至关重要。
更新日期:2021-06-25
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