article · Journal of Computational Methods in Engineering Applications · 2024

GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning

S. Sanchez Cruz, Hugo Tanzarella Teixeira

Cited by 0Open access

Abstract

Motor design complexity arises from intricate parameter interdependencies, where traditional experience-driven approaches prove inefficient and difficult to optimize through experimentation alone. Growing demands for enhanced motor performance in electric vehicles and intelligent manufacturing necessitate advanced solutions for multi-objective, multi-constraint optimization challenges. This paper presents a performance optimization algorithm integrating Graph Neural Networks (GNN) with adaptive weighting to overcome these limitations. GNNs excel at modeling structured parameter relationships through feature propagation, automatically extracting critical design features that conventional methods fail to capture effectively. Complementing this, Mixed-Integer Linear Programming (MILP) provides robust global optimization under complex decision variables and constraints, resolving convergence issues inherent in traditional algorithms. An adaptive weighting mechanism dynamically prioritizes parameters based on their performance impact, ensuring context-sensitive optimization. By synthesizing GNN representation learning, MILP optimization, and adaptive weighting, our framework addresses three core deficiencies of existing methods: computational inefficiency, poor global convergence, and static parameter valuation. This integrated machine learning and optimization approach establishes an efficient paradigm for next-generation motor design.

Cite this paper

APA 7

Cruz, S. S., & Teixeira, H. T. (2024). GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning. Journal of Computational Methods in Engineering Applications, 1–15. https://doi.org/10.62836/jcmea.v4i1.04010484

MLA 9

Cruz, S. Sanchez, and Hugo Tanzarella Teixeira. “GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning.” Journal of Computational Methods in Engineering Applications, 2024, pp. 1–15, https://doi.org/10.62836/jcmea.v4i1.04010484.

Chicago

Cruz, S. Sanchez, and Hugo Tanzarella Teixeira. 2024. “GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning.” Journal of Computational Methods in Engineering Applications: 1–15. https://doi.org/10.62836/jcmea.v4i1.04010484.

Harvard

Cruz, S.S. and Teixeira, H.T. (2024) ‘GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning’, Journal of Computational Methods in Engineering Applications, pp. 1–15. Available at: https://doi.org/10.62836/jcmea.v4i1.04010484.

BibTeX

@article{cruz2024representation,
  title = {{GNN-Representation Enabled Adaptive Weighting Algorithm for Mechanical Performance Tuning}},
  author = {Cruz, S. Sanchez and Teixeira, Hugo Tanzarella},
  journal = {Journal of Computational Methods in Engineering Applications},
  pages = {1--15},
  year = {2024},
  doi = {10.62836/jcmea.v4i1.04010484}
}

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