Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.

Laser-powder bed fusion (L-PBF) process is a family of modern technologies, in which functional, complex (3D) parts are formed by selectively melting the metallic powders layer-by-layer based on fusion. The machining of L-PBF parts for improving their quality is a difficult task. This is because different component orientations (L-PBF-layer orientations) produce different quality of machined surface even though the same cutting parameters are applied. In this paper, stainless steel grade SS 316L parts from L-PBF were subjected to the finishing (milling) process to study the effect of part orientations. Furthermore, an attempt is made to suppress the part orientation effect by changing the layer thickness (LT) of the parts during the L-PBF process. L-PBF parts were fabricated with four different layer thicknesses of 30, 60, 80 and 100 μm to see the effect of the LT on the finish milling process. The results showed that the layer thickness of 60 μm has significantly suppressed the part orientation effect as compared to the other three-layer thicknesses of 30, 80 and 100 μm. The milling results showed that the three-layer thickness including 30, 80 and 100 μm presented up to a 34% difference in surface roughness among different part orientations while using the same milling parameters. In contrast, the layer thickness of 60 μm showed uniform surface roughness for the three-part orientations having a variation of 5–17%. Similarly, the three-layer thicknesses 30, 80 and 100 μm showed up to a 25%, 34% and 56% difference of axial force (Fa), feed force (Ff) and radial force (Fr), respectively. On the other hand, the part produced with layer thickness 60 μm showed up to 11%, 25% and 28% difference in cutting force components F_a, F_f and F_r, respectively. The three-layer thicknesses 30, 80 and 100 μm in micro-hardness were found to vary by up to 14.7% for the three-part orientation. Negligible micro-hardness differences of 1.7% were revealed by the parts with LT 60 μm across different part orientations as compared to 6.5–14% variations for the parts with layer thickness of 30, 80 and 100 μm. Moreover, the parts with LT 60 μm showed uniform and superior surface morphology and reduced edge chipping across all the part orientations. This study revealed that the effect of part orientation during milling becomes minimum and improved machined surface integrity is achieved if the L-PBF parts are fabricated with a layer thickness of 60 μm.

The quest for advanced cooling/lubrication approaches for energy-efficient, eco-benign, and cost-effective sustainable machining processes is garnering attention in academia and industry. Electrical and embodied energy consumption plays an important role in reducing CO₂ emissions. In the present study, new empirical models are proposed to assess sustainable indicators. The embodied energy, environmental burden, and cost of coolant/lubricant have been added in the proposed models. Initially, optimal levels of minimum quantity lubrication (MQL) oil flow rate, liquid LN₂ flow rate, air pressure, and nanoparticle concentration were found. Based on optimal technological parameters, experiments were performed under the same cutting conditions (machining parameters) for MQL and cryogenic LN₂-assisted external turning of Ti6-Al-4V titanium alloy. The electric power and energy consumption, production time/cost, and CO₂ emissions were assessed for a unit cutting-tool life. Later, specific responses were measured and compared between both cooling and lubrication approaches. Results showed that hybrid Al-GnP nanofluid consumed 80.6% less specific cumulative energy and emitted 88.7% less total CO₂ emissions. However, cryogenic LN₂ extended tool life by nearly 70% and incurred 4.12% less specific costs with 11.1% better surface quality. In summary, after Energy–Economy–Ecology–Engineering technology (4E)-based analysis, cryogenic LN₂ is sustainable economically but not environmentally and there is a need to improve the sustainable production of LN₂ at an industrial scale to achieve environmental sustainability. The present study provides useful information to establish clean machining processes.

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.

The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.

The machining of the electron beam melting (EBM) produced parts is a challenging task because, upon machining, different part orientations (EBM layers’ orientations) produce different surface quality even when the same machining parameters are employed. In this paper, the EBM fabricated parts are machined in three possible orientations with regard to the tool feed direction, where the three orientations are “tool movement in a layer plane” (TILP), “tool movement perpendicular to layer planes” (TLP), and “tool movement parallel to layers planes” (TPLP). The influence of the feed rate, radial depth of cut, and cutting speed is studied on surface roughness, cutting force, micro-hardness, microstructure, chip morphology, and surface morphology of Ti6Al4V, while considering the EBM part orientations. It was found that different orientations have different effects on the machined surface during milling. The results show that the EBM parts can achieve good surface quality and surface integrity when milled along the TLP orientation. For instance, surface roughness (Sa) can be improved up to 29% when the milling tool is fed along the TLP orientation compared to the other orientations (TILP and TPLP). Furthermore, surface morphology significantly improves with lower micro-pits, redeposited chips, and feed marks in case of the TLP orientation.