Toward axial accuracy prediction and optimization of metal tube bending forming: A novel GRU-integrated Pb-NSGA-III optimization framework. (September 2022)

• Record Type: Journal Article
• Authors: Sun, Chang; Wang, Zili; Zhang, Shuyou; Liu, Xiaojian; Wang, Le; Tan, Jianrong
• Is Part Of: Engineering applications of artificial intelligence. Volume 114(2022)
• Publication Date: 2022-09
• Subjects: Optimization framework -- Priority-based NSGA-III (Pb-NSGA-III) -- GRU-based deep learning network -- Radius changes series (RCS) -- Rotary draw bending (RDB); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105193
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Springback is particularly common in metal tube bending, which extremely affects the metal tube axial accuracy. At present, the springback mechanism still remains unclear due to the complex plastic deformation characteristics of metal materials. It is difficult to obtain the accurate axial springback information before bending forming, not to mention formulating a reasonable processing plan to compensate springback. To alleviate this difficulty, a novel optimization framework is constructed which takes the radius changes series (RCS) as the new axial accuracy evaluation index for the first time. The optimization framework contains a GRU-based deep learning network as the prediction module to predict the springback more reliably. Subsequently, NSGA-III has been improved by the proposed guiding factor (GF) and dynamic reference points (DRF) algorithm, i.e, priority-based NSGA-III (Pb-NSGA-III), which can more efficiently deal with the objectives with different priorities when generating the processing plan. With the help of the finite element (FE) and bending experiment, the springback dataset construction and the accuracy verification can be achieved. The results show that the framework can achieve high-precision and robust prediction of the tube axial accuracy. Compared with the existing commonly used multi-objective algorithms, the proposed Pb-NSGA-III shows its superiority in engineering application. Graphical abstract: Highlights: The proposed RCS index fully characterizes the springback phenomenon of bent-tube. GRU-based deep network performs well in the prediction of RCS. Proposed Pb-NSGAIII can deal with objectives have different priorities better. Higher-precision processing plan can be generated by the framework.