Multi-Stage Assembly Optimization Study Based on Decision Tree Algorithms

This paper presents a thorough study on multi-stage assembly optimization utilizing decision tree algorithms. A novel approach for writing and recognizing “decision codes” is introduced and applied to decision trees to acquire a coherent and accessible set of decision-making options. To mitigate potential cyclic repetition that may compromise stability, a limit fitting method is employed for systematic control. The decision tree algorithm is utilized to process various parameters across scenarios, culminating in a comparison of outcomes based on performance metrics, thereby identifying the most advantageous assembly strategy. Furthermore, this research extends to an examination of independent multi-process models, analyzing the transition from components to semi-finished products and ultimately to finished products. By evaluating the overall weight of each component, this study identifies elements with minimal impact on the overall system, thereby optimizing the model without detracting from accuracy. This optimization significantly enhances model efficiency while maintaining accuracy through the preservation and transfer of state data between stages, thereby ensuring stable and precise multi-stage assembly processes.