AI-Based Sequence Determination in Manufacturing Systems through MST Graph Optimization
DOI:
https://doi.org/10.61704/pr.615Keywords:
MST Algorithm, Delta Volume, Machining Operations Sequence, AI-Enhanced Graph Optimization, Artificial Intelligence in Combinatorial Optimization, Automated Process Planning (APP)Abstract
In the realm of computational optimization, the Minimum Spanning Tree (MST) remains a cornerstone algorithm for solving complex networking and sequence-related problems. This research explores a novel application of MST within the field of Automated Process Planning (APP). By leveraging the "Delta Volume" decomposition method, we represent the material removal process as a structured graph of interdependent data nodes. The core contribution of this work lies in the development of a specialized algorithmic framework, implemented via MATLAB, which treats machining volumes as a discrete search space. Our approach utilizes MST-based logic to navigate this space and determine the most efficient execution sequence, effectively transforming a traditional manufacturing challenge into a graph-theory optimization problem. By integrating these computational techniques, the study provides a robust automated solution that bridges the gap between geometric modeling and intelligent process sequencing, ensuring both precision and algorithmic efficiency.
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Copyright (c) 2026 Soud M. Amen, Salim A. Saleh, Harith Hamoodat
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
