This study aims to investigate chip formation and surface generation during the precision turning of stainless steel 316L samples. A Finite Element Method (FEM) was used to simulate the chipping process of the stainless steel but with only a restricted number of process parameters. A set of turning tests was carried out using tungsten carbide tools under similar cutting conditions to validate the results obtained from the FEM for the chipping process and at the same time to experimentally examine the generated surface roughness. These results helped in the analysis and understanding the chip formation process and the surface generation phenomena during the cutting process, especially on micro scale. Good agreement between experiments and FEM results was found, which confirmed that the cutting process was accurately simulated by the FEM and allowed the identification of the optimum process parameters to ensure high performance. Results obtained from the simulation revealed that, an applied feed equals to 0.75 of edge radius of new cutting tool is the optimal cutting conditions for stainless steel 316L. Moreover, the experimental results demonstrated that in contrast to conventional turning processes, a nonlinear relationship was found between the feed rate and obtainable surface roughness, with a minimum surface roughness obtained when the feed rate laid between 0.75 and 1.25 times the original cutting edge radius, for new and worn tools, respectively.

This study presents a comprehensive evaluation of Equal Channel Angular Pressing (ECAP) processing on the structural evolution and mechanical properties of AA2xxx aluminum alloy. Finite element analysis (FE) was used to study the deformation behavior of the AA2xxx billets during processing in addition investigate the strain homogeneity in the longitudinal and transverse direction. Billets of AA2011 aluminum alloy were processed successfully through ECAP up to 4-passes with rotating the sample 90° along its longitudinal axis in the same direction after each pass (route Bc) at 150 °C. The microstructural evolution and crystallographic texture were analyzed using the electron back-scatter diffraction (EBSD) and optical microscopy (OM). An evaluation of the hardness and tensile properties was presented and correlated with the EBSD findings and FE simulations. The FE analysis results were in good agreement with the experimental finding and microstructural evolution. Processing through 4-passes produced an ultrafine-grained structure (UFG) and a recrystallized fine grain dominated the structure coupled with a geometric grain subdivision which indicated by grain refining and very high density of substructures. This reduction in grain size was coupled with an enhancement in the hardness, tensile strength by 66.6%, and 52%, respectively compared to the as-annealed counterpart. Processing through 1-pass and 2-passes resulted in a strong texture with significant rotation for the texture components whereas 4-passes processing led to losing the symmetry of the texture with significant reduction in the texture intensity.

This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2xxx series) was employed in the current study, and Al₂O₃ was used as a reinforcement. The performance evaluation of the Al-4Cu/Al₂O₃ composite involved analyzing the influence of the Al₂O₃ weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al₂O₃ reinforcement, namely 0%, 2.5%, 5.0%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4Cu/Al₂O₃ composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4Cu/Al₂O₃ composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results.

Copper and its related alloys are frequently adopted in contemporary industry due to their outstanding properties, which include mechanical, electrical, and electronic applications. Equal channel angular pressing (ECAP) is a novel method for producing ultrafine-grained or nanomaterials. Modeling material design processes provides exceptionally efficient techniques for minimizing the efforts and time spent on experimental work to manufacture Cu or its associated alloys through the ECAP process. Although there have been various physical-based models, they are frequently coupled with several restrictions and still require significant time and effort to calibrate and enhance their accuracies. Machine learning (ML) techniques that rely primarily on data-driven models are a viable alternative modeling approach that has recently achieved breakthrough achievements. Several ML algorithms were used in the modeling training and testing phases of this work to imitate the influence of ECAP processing parameters on the mechanical and electrical characteristics of pure Cu, including the number of passes (N), ECAP die angle (φ), processing temperature, and route type. Several experiments were conducted on pure commercial Cu while altering the ECAP processing parameters settings. Linear regression, regression trees, ensembles of regression trees, the Gaussian process, support vector regression, and artificial neural networks are the ML algorithms used in this study. Model predictive performance was assessed using metrics such as root-mean-squared errors and R² scores. The methodologies presented here demonstrated that they could be effectively used to reduce experimental effort and time by reducing the number of experiments runs required to optimize the material attributes aimed at modeling the ECAP conditions for the following performance characteristics: impact toughness (I_T), electrical conductivity (E_C), hardness, and tensile characteristics of yield strength (σ_y), ultimate tensile strength (σ_u), and ductility (D_u)

This article presents the results of an experimental investigation into the machinability of Ti6Al4V alloy during hard turning, including both conventional and high-speed machining, using polycrystalline diamond (PCD) inserts. A central composite design of experiment procedure was followed to examine the effects of variable process parameters; feed rate, cutting speed and depth of cut (each at five levels) and their interaction effects on surface roughness and cutting temperature as process responses. The results revealed that cutting temperature increased with increasing cutting speed and decreasing feed rate in both conventional and high-speed machining. It was found that high-speed machining showed an average increase in cutting temperature of 65% compared with conventional machining. Nevertheless, high-speed machining showed better performance in terms of lower surface roughness despite using higher feed rates compared to conventional machining. High-speed machining of Ti6Al4V showed an improvement in surface roughness of 11% compared with conventional machining, with a 207% increase in metal removal rate (MRR) which offered the opportunity to increase productivity. Finally, an inverse relationship was verified between generated cutting temperature and surface roughness. This was attributed mainly to the high cutting temperature generated, softening, and decreasing strength of the material in the vicinity of the cutting zone which in turn enabled smoother machining and reduced surface roughness.

This article presents the results of an experimental investigation into the effect of process parameters in the precision hard turning of Ti-6Al-4V on chip morphology at both macro and micro levels. It also reports on the control of chip generation to improve chip evacuation and breakability at the macro level by varying the process parameters, namely, feed rate, cutting speed and depth of cut during turning tests. A scanning electron microscope (SEM) was used to examine the chips produced for a better understanding of chip curling mechanisms at the micro level. Surface roughness of the machined specimens was measured to assess the effect of chip evacuation on obtainable surface quality. From the results, it was found that the interaction of process parameters has a significant effect on the control of chip formation. In particular, the interaction of higher cutting speeds and greater depths of cut produced chip entanglement with the workpiece for all values of feed rates. Using relatively higher feed rates with a low depth of cut showed good results for chip breaking when machining at higher cutting speeds. Different chip curling mechanisms were identified from the SEM results. Chip side-curl formation showed different segmentation patterns with an approximately uniform chip thickness along the chip width, while chip up-curl occurred due to variations in chip thickness. Finally, it was found that the tangling of the chip with the workpiece has a significant effect on the final surface quality.