Minimizing energy usage in multi-point manufacturing: a dimensional transfer learning strategy

Abstract

Abstract With the increasing complexity of industrial manufacturing tasks, optimizing Multi-Point Manufacturing (MPF) operations, such as drilling and spot welding, has become essential for improving energy efficiency and execution speed. These tasks can be formulated as multi-dimensional Traveling Salesman Problems (TSPs), where robotic manipulators must traverse multiple points optimally while minimizing energy consumption. However, traditional heuristic algorithms, such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO), face significant limitations. Their iterative nature leads to long computation times, and their performance heavily relies on precise parameter tuning to achieve high-quality solutions. To address these challenges, this study proposes a novel minimum energy consumption scheduling strategy for MPF problems, utilizing a Graph Neural Network (GNN)-based approach enhanced by Transfer Learning. The method employs Self-Adjusting Multi-modal Artificial Neural Networks (SAMANN) to reduce the 6D task nodes into a 2D representation, enabling the reuse of pre-trained 2D TSP models. Fine-tuning is then applied to further optimize performance. Compared with traditional heuristic algorithms, the proposed method significantly reduces computation time while maintaining solution quality. It is also found that the new approach surpasses the performance of the current state-of-the-art (SOTA) neural model in terms of both solution quality and training time across multi-dimensional TSPs and MPF problem domains, offering a robust and efficient solution for real-world MPF applications.