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Path planning algorithm based on adaptive model predictive control and learning-based parameter identification approach for incremental forming processes for product precision improvements

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Abstract

Incremental sheet forming (ISF) is an emerging flexible manufacturing technique. However, it is currently facing the challenge of popularization in industry since the poor geometric precision of the finished parts cannot meet the engineering standard. Aiming at enhancing product geometric precision, online path planning algorithms had been proposed by researchers. However, the existing algorithms had been applied successfully in manufacturing products with very simple geometries only and had limited capabilities of being generalized to complex products, which prevented the widespread application of ISF. To solve this long-lasting problem, more powerful and generic path planning algorithm needs to be proposed for the ISF process. In this study, an adaptive MPC-based path planning algorithm was developed for the ISF process, aiming at enhancing the geometric accuracies of products with more complex geometric features. The algorithm was formulated based on the state-space modelling of the process, the learning-based parameter identification approach, and quadratic programming optimization. The performance and the generality of the present adaptive MPC algorithm were experimentally validated in manufacturing parts with several complex geometric features that were typical in industrial parts using closed-loop ISF processes. In the first test, the present adaptive MPC algorithm showed a significant performance improvement of 56% in the reduction of maximum error on the base compared to the non-adaptive MPC. In the other two tests, the present algorithm also received satisfactory performances. Compared to the non-adaptive algorithm, the test results indicated that the present adaptive algorithm had a better performance and a higher generality.

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Funding

The authors received support and collaboration with Queensland Government, Boeing Research & Technology—Australia, The University of Queensland, and QMI Solutions through the Advanced Queensland Innovation Partnerships Project 2016000418.

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Authors and Affiliations

  1. School of Mechanical & Mining Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    An He, Chenhao Wang, Sheng Liu & Paul A. Meehan

Authors
  1. An He
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  2. Chenhao Wang
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  3. Sheng Liu
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  4. Paul A. Meehan
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Contributions

An He proposed the algorithm, developed the program, performed the experiments, and wrote the manuscript. Paul Meehan got the research funding. All authors contributed to the theoretical analysis of problems and experimental processes.

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Correspondence to An He.

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The authors declare no competing interests.

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Appendices

Appendix 1 Showings of target geometries

Figures 

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Showings of the “dog bone”–shaped part with varying wall angles in a 3D view, b top view, and c vertical sectional view

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Showings of the free-form part with varying wall angles in a 3D view, b top view, and c vertical sectional view

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Showings of the circular part with varying wall angles in a 3D view, b top view, and c vertical sectional view

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Appendix 2 Regions on the target geometries

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Demonstrations of the edge, wall, conjunction, and base zones on a the “dog bone”–shaped part, b the free-form part, and c the circular part from top views

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He, A., Wang, C., Liu, S. et al. Path planning algorithm based on adaptive model predictive control and learning-based parameter identification approach for incremental forming processes for product precision improvements. Int J Adv Manuf Technol 125, 4513–4528 (2023). https://doi.org/10.1007/s00170-023-10989-y

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  • DOI: https://doi.org/10.1007/s00170-023-10989-y

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