Accurate prediction of machining cycle times and feedrates with deep neural networks using BiLSTM

Abstract

This paper presents a deep neural network-based approach to predict machining cycle (run) times along complex 3-axis machining part programs. CAD/CAM systems are utilized to generate part programs to machine parts with complex geometries and sculptured surfaces. The part program is processed by the numerical control (NC) unit of the machine tool to plan a feed profile. For such complex part programs, the actual (observed) feed profile and commanded (desired) one are significantly different. This paper uses bi-directional long short-term memory deep networks (bi-LSTM) to model the interpolation behavior of the NC systems to accurately predict how the feedrate profile is planned for any given part program. The G-line length, commanded feed, change in the feed direction through consecutive G-lines are used as a primary set of inputs to the network. The neural network is then trained by running series of toolpaths on the NC system and recording the resultant feedrate profile. Simulation and experimental studies are conducted on NC kernels to validate effectiveness of the proposed strategy. It is shown that machining cycle times on complex toolpaths can be predicted with > 94 % accuracy.