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.

This study presents a detailed computational fluid dynamics (CFD) investigation into the aerodynamic performance of the NREL Phase VI wind turbine, focusing on torque and power generation under different turbulence models. The primary objective was to analyse the effect of various turbulence models and their responses in wind turbine torque generation. Furthermore, it also investigates the distance effect on wind velocity deficit. The research utilizes 2D and 3D simulations of the S809 airfoil and the full rotor, examining the predictive capabilities of the k-epsilon, k-omega, and k-omega SST turbulence models. The study incorporates both experimental validation and wake analysis using the Gaussian wake model to assess wind velocity deficits. Simulations were conducted for a wind speed range of (6–10 m/s), with results indicating that the k-epsilon model provided the closest match to experimental data, particularly at higher wind speeds within the targeted range. Even though k-epsilon results had better agreement when validated with experimental data, theoretically k-omega (SST) should perform better as it combines k-epsilon and k-omega advantages in predicting the flow regardless of its farness from the wall. However, in simulations using the k-omega (SST), the separation of flow and the shear stress transients were only visible at wind speeds of 10 m/s or higher. Wake effects, on the other hand, were found to cause significant velocity deficits behind the turbine, following an exponential decay pattern. The findings offer valuable insights into improving wind turbine performance through turbulence model selection and wake impact analysis, providing practical guidelines for future wind energy optimizations.

The current practice in aluminum recycling plants is to change the waste into molten metal through the conventional recycling (CR) manufacturing process. However, the CR technique is so energy-intensive that it also poses an indirect threat to the environment. This paper presents a study on meltless direct recycling hot press forging (DR-HPF) as an alternative sustainable approach that has fewer steps with low energy consumption, as well as preventing the generation of new waste. A laboratory experiment was conducted to study the mechanical properties and surface integrity of AA7075 aluminum alloy by employing a hot press forging (HPF) process under different temperatures (380, 430, and 480 °C) and holding times (0, 60, and 120 min). It was found that as the parameter increased, there was a positive increase in ultimate tensile strength (UTS), elongation to failure (ETF), density, and microhardness. The recycled chips exhibit the best mechanical properties at the highest parameters (480 °C and 120 min), whereas the UTS = 245.62 MPa and ETF = 6.91%, while surface integrity shows that the calculated microhardness and density are 69.02 HV and 2.795 g/cm³, respectively. The UTS result shows that the highest parameters of 480 °C and 120 min are comparable with the Aerospace Specification Metals (ASM) Aluminum AA7075-O standard. This study is a guide for machinists and the manufacturing industry to increase industry sustainability, to preserve the earth for future generations.

Multi-inlet cyclone separators can play a vital role in industrial processes by enhancing particle separation efficiency and minimizing energy consumption. This numerical study investigates multi-inlet cyclone separators to enhance their performance using a constant flow rate with a varying inlet height and width. By systematically varying the inlet height and width, three-inlet and four-inlet cyclone separators are developed and evaluated, termed 3 inlet-a, 4 inlet-a, 3 inlet-b, and 4 inlet-b. The findings reveal that increasing the number of inlets without changing the total inlet area does not improve the separation performance. However, strategic modifications to the inlet height and width significantly enhance the separation efficiency. Notably, the 3 inlet-a and 4 inlet-a designs achieve higher separation efficiencies at a 1.22 ${\mathrm{m}}^3$/s flow rate without increasing the pressure drop. Meanwhile, the 3 inlet-b and 4 inlet-b models demonstrate superior performances, with a higher separation efficiency and a pressure drop only marginally higher than the two-inlet design. This study provides valuable insights into the impact of inlet variations on cyclone separator performance, guiding future efforts to enhance the separation efficiency in multi-inlet designs.

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.