Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks. (June 2022)

• Record Type: Journal Article
• Authors: Sun, Chao; Dominguez-Caballero, Javier; Ward, Rob; Ayvar-Soberanis, Sabino; Curtis, David
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 75(2022)
• Publication Date: 2022-06
• Subjects: Data-driven model -- Neural networks -- Feedrate -- Machine Tool -- digital twins -- Industry 4.0; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2021.102293
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: Proposed a data-driven method of building machining time prediction models for industrial machines. The model estimated the machining time with more than 90% accuracy. 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 underestimate 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 center 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.