Abstract:

To facilitate the innovative design of large-scale mining machinery, the external characteristics of LHDs were summarized. Additionally, a lattice topology optimization method and EWM were proposed for designing and evaluating the appearance of LHDs. The lattice topology optimization method was used to refine the appearance of the LHD. Subsequently, the analytic hierarchy process-EWM (AHP-EWM) was employed to assign weights to the evaluation indicators and construct the hierarchical criterion model. Five indicators, namely creativity, aesthetics, functionality, economic viability, and technical ability, were identified to constitute the evaluation system. The AHP was used to assign subjective weights to first-level indicators, and the objective weights of second-level indicators were analyzed by EWM. According to python, the weights of five methods, namely grey relational degree, multi-attribute decision analysis weight (CRITIC), independence weight, fuzzy comprehensive evaluation method, approximate ideal solution sorting method, and ideal point method (TOPSIS), were calculated. The feasibility of the AHP-EWM method was demonstrated. Finally, alternative designs were optimized based on the indicator weights and their corresponding scores, and the least squares support vector machine (LSSVM) was used to validate the model. The results show that the LSSVM model achieves a coefficient of determination above 0.93 and a mean square error below 0.01 in regression analysis. It outperforms traditional models with higher accuracy, confirming the effectiveness of the optimization decision model combining the lattice topology optimization method and EWM.

Key words: lattice topology optimization method, entropy weight method (EWM), load-haul-dump (LHD), appearance design, scheme optimization