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.

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.

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Single-point incremental forming is an innovative flexible and inexpensive technique to form sheet products when prototypes or small batches are required. The process allows complex geometries to be produced using a computer numerical control machine, eliminating the need for a special die. This study reports on the effects of four important single-point incremental forming process parameters on produced surface profile accuracies. The profile accuracy was estimated by measuring the side angle errors and surface roughness and also waviness and circularity of the product inner surface. Full factorial design of experiments was used to plan the study, and the analysis of variance was used to analyze and interpret the results. The results indicate that the tool diameter (d), step depth (s), and sheet thickness (t) have significant effects on the produced profile accuracy, while the feed rate (f) is not significant. As a general rule, thin sheets with greater tool diameters yielded the best surface quality. The results also show that controlling all surface quality features is complex because of the contradicting effects of, and interactions between, a number of the process parameters.

Incremental sheet forming is a specific group of sheet forming methods that enable the manufacture of complex parts utilizing computer numerical control instead of specialized tools. It is an incredibly adaptable operation that involves minimal usage of sophisticated tools, dies, and forming presses. Besides its main application in the field of rapid prototyping, incremental sheet forming processes can be used for the manufacture of unique parts in small batches. The goal of this study is to broaden the knowledge of the deformation process in single-point incremental forming. This work studies the deformation behavior in single-point incremental forming by experimentally investigating the principal stresses, principal strains, and thinning of single-point incremental forming products. Conical-shaped components are fabricated using AA1050-H14 aluminum alloy at various combinations of fundamental variables. The factorial design is employed to plan the experimental study and analysis of variance is conducted to analyze the results. The grey relational analysis approach coupled with entropy weights is also implemented to identify optimum process variables for single-point incremental forming. The results show that the tool diameter has the greatest effect on the thinning of the SPIF product, followed by the sheet thickness, step size, and feed rate.

Electron beam melting (EBM) is gaining more interest due to its near-net-shape production and ability to process di cult to manufacture materials, such as titanium alloys. However, due to the poor surface quality of the EBM parts, nishing operations, such as machining, are required. The main issue in the nishing of the EBM parts is their directional properties that lead to different surface roughness values for different part orientations for the same employed machining parameters. In this study, EBM Ti6Al4V parts were subjected to heat treatment to suppress the effect of the part/layers' orientations effect during the nish milling process. Three different heat treatments were employed to the as-fabricated EBM Ti6Al4V parts. The ne lamellar microstructure was set as a target from the heat treatment due to its overall superior mechanical properties and also, to keep a fair comparison in the machining of the asfabricated and heat-treated EBM Ti6Al4V parts. The results of the heat treatments showed that the EBM Ti6Al4V parts heated at 600°C for 3 hours and then air-cooled exhibited the same ne lamellar microstructure and microhardness as that of the as-fabricated EBM parts. The other two heat treatments including heating at 950°C and 800°C for 2 hours followed by air cooling were not able to maintain the ne lamellar structure due to the increase in the width of the alpha grains. The as-fabricated and heattreated EBM Ti6Al4V parts were subjected to the milling operation by considering the three possible parts/layers orientation with respect to the tool feed directions. The milling results showed that the asfabricated parts showed up to a 27% difference in surface roughness for different part orientations. In contrast, the heat-treated parts showed uniform surface roughness for the three part orientations with a variation of 8%. Similarly, considerable differences were observed in the surface integrity and the machined surface microhardness (18%) of the as-fabricated EBM parts as compared to the heat-treated parts) 2%). The heat-treated parts showed more uniform and superior surface morphology across all the part orientations. This is because of the more uniform microstructure, less porosity, higher consolidation of the EBM layers, and elimination of the columnar grains in the case of the heat-treated parts as compared to the EBM parts. This study revealed that in the case of the heat-treated EBM part the effect of part orientation with respect to milling tool feed direction was almost removed.

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.

Single Point Incremental Forming (SPIF) is an innovative forming approach for sheet metal that promises an inexpensive and flexible way to produce sheet metal parts in small batches. SPIF allows the production of complex geometries using a computer numerical control machine. In this study, SPIF has been conducted to investigate the effects of sheet thickness, tool diameter, feed rate, and step size on part depth. Statistical tools were used to design the experiments. Analysis of variance, as well as regression and optimization techniques were used to analyze the resulting part depth. Two levels of each parameter were included in a full factorial design. The study found several relations amongst the process parameters and the part depth. In summary, it was proved that the sheet thickness and tool diameter have the greatest effect on the part depth, whereas the step size has a small, but significant one.