The increasing demand for sustainable manufacturing has revived the interest in solid-state recycling (SSR) as a promising alternative method for aluminum waste. In this context, chips generated during machining processes constitute a substantial portion of aluminum waste, offering significant potential for recycling and mitigating waste. However, the machining chip morphology significantly impacts the properties of chip-based recycled parts. This review paper examines the current state-of-the-art solid-state recycling methods, focusing on hot forging, extrusion, equal channel angular pressing, friction stir extrusion and field-assisted sintering. It investigates the impact of aluminum chip morphology on the properties of the directly recycled material, emphasizing the chip machining consequence on the final quality of the product. Several studies reported that the strain and operating temperature are the most influential factors in SSR processes, followed by chip size with an average length of less than 4 mm. Yet, the heating time up to 3 h also had a major impact on chip weld strength. The findings highlighted the significance of aluminum chip morphology in improving the quality of recycled material. The properties of direct recycled samples primarily depend on chip weld strength and microstructure. Overall, this study presented a comprehensive overview of the current state of solid-state recycling and emphasized the significance of chip morphology in advancing the recycling process. Consequently, it equips researchers with a valuable resource for developing effective strategies for sustainable recycling of aluminum chips with high quality.
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.
In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model's surface through circular dimension analysis. This involves sending the model's image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.
The optimal conditions of applied factors to reuse Aluminium AA6061 scraps are (450, 500, and 550) ⁰C preheating temperature, (1-15) % Boron Carbide (B4C), and Zirconium (ZrO2) hybrid reinforced particles at 120 min forging time via Hot Forging (HF) process. The response surface methodology (RSM) and machine learning (ML) were established for the optimisations and comparisons towards materials strength structure. The Ultimate Tensile Strength (UTS) strength and Microhardness (MH) were significantly increased by increasing the processed temperature and reinforced particles because of the material dispersion strengthening. The high melting point of particles caused impedance movements of aluminium ceramics dislocations which need higher plastic deformation force and hence increased the material's mechanical and physical properties. But, beyond Al/10 % B4C + 10 % ZrO2 the strength and hardness were decreased due to more particle agglomeration distribution. The optimisation tools of both RSM and ML show high agreement between the reported results of applied parameters towards the materials' strength characterisation. The microstructure analysis of Field Emission Scanning Electron Microscopy (FE-SEM) and Atomic Force Microscope (AFM) provides insights mapping behavioural characterisation supports related to strength and hardness properties. The distribution of different volumes of ceramic particle proportion was highlighted. The environmental impacts were also analysed by employing a life cycle assessment (LCA) to identify energy savings because of its fewer processing steps and produce excellent hybrid materials properties.