Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging

• Main Authors: M. Altharan, Yahya, Shamsudin, Shazarel, Lajis, Mohd Amri, Al-Alimi, Sami, Yusuf, Nur Kamilah, M. Ghaleb, Atef, Zhou, Wenbin
• Format: Article
• Language: English
• Published: Plos One 2024
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/11951/1/J17616_3f2e7cbd4b41778d1b8908faffb4d48f.pdf; http://eprints.uthm.edu.my/11951/; https://doi.org/10.1371/journal.pone.0300504
• Institution: Universiti Tun Hussein Onn Malaysia

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.