Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks

TL;DR

This paper introduces a neural network-based data-driven approach to accurately predict machining cycle times by modeling machine tool feedrate behavior, outperforming traditional CAM estimates and aiding digital twin development.

Contribution

It presents a novel neural network model for each machine axis that incorporates toolpath geometry and measured feedrates, improving cycle time prediction accuracy.

Findings

Achieved over 90% accuracy in cycle time estimation.

Neural networks effectively learn complex machine tool behaviors.

Method enhances digital twin integration in Industry 4.0.

Abstract

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.