Toward digitally twinning the process of creating machine controller digital twins – A G-code generation scenario

Abstract

This paper proposes BatchEGO, an extension of Efficient Global Optimization (EGO), in an active learning context for creating and maintaining digital twins (DT). The motivation for this study is to address the challenges of obtaining high-quality yet cost and time effective data for developing accurate DTs of machine tool controllers that are robust to noise and physical system changes. BatchEGO is employed to generate manufacturing recipes (G-codes) recursively and iteratively, based on the spatial constraints of the physical machine, providing G-code based data for DTs. BatchEGO Interm is also proposed that extends BatchEGO by sampling additional intermediate points on the generated trajectory, to improve convergence and accuracy of DTs. To account for the stochastic nature of proposed methods, a total of 70 experiments based around repetitions of 4 correlation functions, 3 acquisition functions, and 4 sampling schemes are used to evaluate the performance of both methods. The results showcase impressive and faster convergence, in terms of sum of squared errors, for BatchEGO Interm with specific settings producing highly accurate and smooth G-codes. For instance, BatchEGO Interm with Matérn 3/2 correlation function and acquisition function of lower confidence bound can reach an error range of 0.0026 within 3-5 iterations. The work demonstrates the novelty and potential of such frameworks for enhancing data quality and quantity for DTs. The importance of G-code generation for DTs is also highlighted, as it can enable better control and optimization of machine tools in various industrial applications. The paper also discusses the different approaches for stitching the sampled points into G-code and compares their accuracy and efficiency.