Multi-objective Energy Consumption Optimization of Aluminum Extrusion Forming Based on RBMO-BP-MOPSO

LI Shixuan; CHEN Jiadui; LIU Dan; YANG Kai

doi:10.3969/j.issn.1674-6457.2026.06.009

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (6) : 85-98. DOI: 10.3969/j.issn.1674-6457.2026.06.009

Light Alloy Forming

Multi-objective Energy Consumption Optimization of Aluminum Extrusion Forming Based on RBMO-BP-MOPSO

LI Shixuan, CHEN Jiadui^*, LIU Dan, YANG Kai

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Abstract

The work aims to employ numerical simulation dataset and RBMO-BP neural network of extrusion processes for complex cross-sectional thin-walled aluminum profiles to develop predictive models for both extrusion energy consumption and temperature variance, thereby providing a practical foundation for multi-objective optimization and decision-making in aluminum extrusion. Firstly, a finite element model of a complex cross-sectional aluminum profile with 1 mm wall thickness was constructed and numerically simulated to generate a dataset of the extrusion process. Secondly, this dataset was used to train and predict outcomes with the proposed RBMO-BP neural network. Thirdly, a multi-objective optimization model for aluminum extrusion forming was established and solved with the RBMO-BP-MOPSO algorithm. Finally, an integrated entropy weight method and grey relational analysis were applied to make decisions among the obtained Pareto-optimal solutions. Compared with the conventional BP neural network, the RBMO-BP model achieved significant improvements in prediction accuracy. For extrusion energy consumption, the MAE, MSE, RMSE, and MAPE decreased by 19.91 kJ, 679.55 kJ², 23.73 kJ, and 1.61%, respectively. For exit temperature mean square deviation, these metrics decreased by 1.83 ℃, 5.09 ℃², 1.84 ℃, and 23.45%, respectively. Bu using entropy weight method and grey relational analysis to select the optimal solution from the Pareto front generated by RBMO-BP-MOPSO, the best extrusion parameters were identified. Under these optimized parameters, extrusion energy consumption was reduced by 14.22% and the exit temperature mean square deviation decreased by 37.28% compared to the original process parameters. By integrating numerical simulation, data-driven prediction, and multi-objective optimization methods, this work accurately predicts both energy consumption and temperature uniformity in the extrusion of complex cross-sectional thin-walled aluminum profiles, successfully optimizes the extrusion process parameters, and validates their effectiveness. The results demonstrate that the proposed multi-objective optimization approach for complex thin-walled aluminum extrusion is both feasible and advanced.

Key words

thin-walled aluminum profiles / extrusion forming / process parameters / RBMO-BP-MOPSO / multi-objective energy consumption optimization

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LI Shixuan, CHEN Jiadui, LIU Dan, YANG Kai. Multi-objective Energy Consumption Optimization of Aluminum Extrusion Forming Based on RBMO-BP-MOPSO[J]. Journal of Netshape Forming Engineering. 2026, 18(6): 85-98 https://doi.org/10.3969/j.issn.1674-6457.2026.06.009

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