Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks. (arXiv:2106.09719v1 [cs.LG])

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



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