A Data-drivenParameter Planning Method for Structural Parts NC Machining. (April 2021)

• Record Type: Journal Article
• Authors: Deng, Tianchi; Li, Yingguang; Liu, Xu; Wang, Pengcheng; Lu, Kai
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 68(2021)
• Publication Date: 2021-04
• Subjects: Structural parts -- Machining parameter planning -- Data-driven -- Graph neural networks; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2020.102080
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Structural parts are generallyused to compose the main load-bearing components in various mechanical products, and are usuallyproduced by NC machining where the machining parameters heavily determine the final production quality, efficiency and cost. Due to the complex structures and high precision requirements, a large amount of human interactions are usually required to modify the machining parameters generated by existing optimisation model-based or expert system-based methods, which will induce unstable machining quality and low efficiency. This paper proposes a data-driven methodfor machining parameter planning by learningthe parameter planning knowledge from thehigh-qualityhistorical processing files. An attribute graph is first defined to represent the part model. Then for each of the machining operations in the historical processing files, the machining parameters are correlated to a sub-graph that refers to the faces to be machined in this operation. By this way, a graph dataset of machining parameters could beconstructed from the historical processing files, and graph neural networks (GNN) are established to learn the planning models for machining parameters. The proposed method provides an end-to-end strategy for constructing machining parameter planning models thus human interactions can be greatly reduced and the performance of the models are able to be improved as the increase in historical processing files. In the case study, the historical processing files of aircraft structural parts machining are used to train the GNN models for planning cutting width, cutting depth and machining feedrate, and the prediction accuracies reach 95.50%, 94.79%, 95.02% respectively.