Lightweight materials, such as titanium alloys, magnesium alloys, and aluminium alloys, are characterised by unusual combinations of high strength, corrosion resistance, and low weight. However, some of the grades of these alloys exhibit poor formability at room temperature, which limits their application in sheet metal-forming processes. Lightweight materials are used extensively in the automobile and aerospace industries, leading to increasing demands for advanced forming technologies. This article presents a brief overview of state-of-the-art methods of incremental sheet forming (ISF) for lightweight materials with a special emphasis on the research published in 2015–2021. First, a review of the incremental forming method is provided. Next, the effect of the process conditions (i.e., forming tool, forming path, forming parameters) on the surface finish of drawpieces, geometric accuracy, and process formability of the sheet metals in conventional ISF and thermally-assisted ISF variants are considered. Special attention is given to a review of the effects of contact conditions between the tool and sheet metal on material deformation. The previous publications related to emerging incremental forming technologies, i.e., laser-assisted ISF, water jet ISF, electrically-assisted ISF and ultrasonic-assisted ISF, are also reviewed. The paper seeks to guide and inspire researchers by identifying the current development trends of the valuable contributions made in the field of SPIF of lightweight metallic materials.
An attempt was made to model input-output relationships of an electrical discharge machining process based on the experimental data (collected according to a central composite design) using multiple regression analysis. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor, and two outputs, namely, material removal rate (MRR) and surface roughness (SR) had been considered for the said modeling. The value of regression coefficient was determined for each model. The performances of the developed models were tested with the help of some test cases collected through the real experiments and were found to be satisfactory. It had been posed as an optimization problem and solved using a genetic algorithm to determine the set(s) of optimal parameters for ensuring the maximum MRR and minimum SR. It was also formulated as a multi-objective optimization problem and a Pareto-optimal front of solutions had been obtained.
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