Meltless recycling technique has been utilized to overcome the lack of primary resources, focusing on reducing the usage of energy and materials. Hot press was proposed as a novel direct recycling technique which results in astoundingly low energy usage in contrast with conventional recycling. The aim of this study is to prove the technical feasibility of this approach by characterizing the recycled samples. For this purpose, AA6061 aluminium chips were recycled by utilizing hot press process under various operating temperature (Ts = 430, 480, and 530 °C) and holding times (ts = 60, 90, and 120 min). The maximum mechanical properties of recycled chip are Ultimate tensile strength (UTS) = 266.78 MPa, Elongation to failure (ETF) = 16.129%, while, for surface integrity of the chips, the calculated microhardness is 81.744 HV, exhibited at Ts = 530 °C and ts = 120 min. It is comparable to theoretical AA6061 T4-temper where maximum UTS and microhardness is increased up to 9.27% and 20.48%, respectively. As the desired mechanical properties of forgings can only be obtained by means of a final heat treatment, T5-temper, aging after forging process was employed. Heat treated recycled billet AA6061 (T5-temper) are considered comparable with as-received AA6061 T6, where the value of microhardness (98.649 HV) at 175 °C and 120 min of aging condition was revealed to be greater than 3.18%. Although it is quite early to put a base mainly on the observations in experimental settings, the potential for significant improvement offered by the direct recycling methods for production aluminium scrap can be clearly demonstrated. This overtures perspectives for industrial development of solid state recycling processes as environmentally benign alternatives of current melting based practices.
Solid-state recycling, which involves the direct recycling of scrap metal into bulk material using severe plastic deformation, has emerged as a potential alternative to the conventional remelting and recycling techniques. Hot press forging has been identified as a sustainable direct recycling technique that has fewer steps and maintains excellent material performance. An experimental investigation was conducted to explore the hardness and density of a recycled aluminum-based metal matrix composite by varying operating temperature and holding time. A mixture of recycled aluminum, AA6061, and aluminum oxide were simultaneously heated to 430, 480, and 530 °C and forged for 60, 90, and 120 min. We found a positive increase in microhardness and density for all composites. The hardness increased approximately 33.85%, while density improved by about 15.25% whenever the temperature or the holding time were increased. Based on qualitative analysis, the composite endures substantial plastic deformation due to the presence of hardness properties due to the aluminum oxide embedded in the aluminum matrix. These increases were significantly affected by the operating temperature; the holding time also had a subordinate role in enhancing the metal matrix composite properties. Furthermore, in an effort to curb the shortage of primary resources, this study reviewed the promising performance of secondary resources produced by using recycled aluminum and aluminum oxide as the base matrix and reinforcement constituent, respectively. This study is an outline for machining practitioners and the manufacturing industry to help increase industry sustainability with the aim of preserving the Earth for our community in the future.
Melting aluminium waste to produce a secondary bulk material is such an energy-intensive recycling technique that it also indirectly threatens the environment. Hot press forging is introduced as an alternative. Mixing the waste with another substance is a proven practice that enhances the material integrity. To cope with the technology revolution, a finite element is utilised to predict the behaviour without a practical trial. Utilising commercial software, DEFORM 3D, the conjectures were demonstrated scientifically. The flow stress of the material was modified to suit the material used in the actual experiment. It is acknowledged that the stress⁻strain had gradually increased in each step. Due to the confined forming space, the temperature decreased by ~0.5% because the heat could not simply vacate the area. A reduction of ~10% of the flesh observed in the simulation is roughly the same as in the actual experiment. Above all, the simulation abides by the standards and follows what has been done previously. Through the finite element utilisation, this study forecasted the performance of the recycled composite. The results presented may facilitate improvement of the recycling issue and conserve the environment for a better future.
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.