The chemical composition of AA6061.

Direct recycling of aluminum waste is crucial in sustainable manufacturing to mitigate environmental impact and conserve resources. This work was carried out to study the application of hot press forging (HPF) in recycling AA6061 aluminum chip waste, aiming to optimize operating factors using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Genetic algorithm (GA) strategy to maximize the strength of recycled parts. The experimental runs were designed using Full factorial and RSM via Minitab 21 software. RSM-ANN models were employed to examine the effect of factors and their interactions on response and to predict output, while GA-RSM and GA-ANN were used for optimization. The chips of different morphology were cold compressed into billet form and then hot forged. The effect of varying forging temperature (Tp, 450–550°C), holding time (HT, 60–120 minutes), and chip surface area to volume ratio (AS:V, 15.4–52.6 mm2/mm3) on ultimate tensile strength (UTS) was examined. Maximum UTS (237.4 MPa) was achieved at 550°C, 120 minutes and 15.4 mm2/mm3 of chip’s AS: V. The Tp had the largest contributing effect ratio on the UTS, followed by HT and AS:V according to ANOVA analysis. The proposed optimization process suggested 550°C, 60 minutes, and 15.4 mm2 as the optimal condition yielding the maximum UTS. The developed models’ evaluation results showed that ANN (with MSE = 1.48%) outperformed RSM model. Overall, the study promotes sustainable production by demonstrating the potential of integrating RSM and ML to optimize complex manufacturing processes and improve product quality.铝废料直接回收对于可持续制造而言至关重要，可有效降低环境影响并节约资源。本研究旨在探究热压锻造（Hot Press Forging, HPF）在AA6061铝屑废料回收中的应用，采用响应面法（Response Surface Methodology, RSM）、人工神经网络（Artificial Neural Network, ANN）与遗传算法（Genetic Algorithm, GA）相结合的策略对工艺参数进行优化，以最大化回收工件的力学强度。本研究借助Minitab 21软件，采用全因子设计与响应面法完成实验方案的规划。本研究采用RSM-ANN耦合模型分析各工艺参数及其交互作用对响应值的影响并预测输出结果，同时借助GA-RSM与GA-ANN模型开展参数优化工作。将不同形貌的铝屑经冷压制成坯料后，再进行热锻造加工。本研究探究了锻造温度（Tp，450~550℃）、保温时长（HT，60~120min）以及铝屑比表面积（AS:V，15.4~52.6 mm²/mm³）对极限抗拉强度（Ultimate Tensile Strength, UTS）的影响规律。当锻造温度为550℃、保温时长120min且铝屑比表面积为15.4 mm²/mm³时，可获得最高极限抗拉强度（237.4 MPa）。经方差分析（Analysis of Variance, ANOVA）可知，锻造温度对极限抗拉强度的影响贡献率最大，其次为保温时长与铝屑比表面积。本研究提出的优化方案建议采用550℃、60min保温时长以及15.4 mm²/mm³的铝屑比表面积作为最优工艺参数，以实现极限抗拉强度的最大化。模型评估结果显示，人工神经网络模型（均方误差Mean Squared Error, MSE=1.48%）的预测性能优于响应面法模型。综上，本研究通过展示响应面法与机器学习（Machine Learning, ML）耦合策略在复杂制造工艺优化及产品质量提升中的应用潜力，为可持续生产提供了可行路径。