ANOVA result of UTS.

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，是减轻环境影响、节约资源的关键举措。本研究探究了热压锻造（hot press forging, HPF）在回收AA6061铝切屑废料中的应用，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）的组合策略优化工艺参数，以最大化回收制件的强度。实验方案借助Minitab 21软件，通过全因子设计（Full factorial）与响应面法（RSM）进行构建。研究采用RSM-ANN模型分析工艺参数及其交互作用对响应值的影响并完成输出预测，同时使用GA-RSM与GA-ANN模型开展参数优化。将不同形貌的切屑经冷压制成坯料后进行热锻造，考察了锻造温度（Tp，450–550℃）、保温时间（HT，60–120 min）、切屑比表面积与体积之比（AS:V，15.4–52.6 mm²/mm³）对极限抗拉强度（ultimate tensile strength, UTS）的影响。当锻造温度为550℃、保温时间120 min、切屑AS:V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，Tp对UTS的贡献占比最高，其次为HT与AS:V。本研究提出的最优工艺参数为550℃、60 min、15.4 mm²/mm³，可实现最大UTS值。所构建模型的评估结果显示，人工神经网络（ANN，均方误差（Mean Squared Error, MSE）=1.48%）的性能优于响应面法（RSM）模型。总体而言，本研究证实了将响应面法与机器学习（Machine Learning, ML）相结合以优化复杂制造工艺、提升产品质量的可行性，有力推动了可持续生产的发展。