Experimental Investigation and Optimization of Forming Parameters in Single Point Incremental Forming of AZ31 Magnesium Alloy

A novel and adaptable forming method, known as Single Point Incremental Forming (SPIF), has emerged to meet the growing needs of the manufacturing industry. This technique is precious for producing innovative products from sheet metal, offering enhanced flexibility and precision in fabrication. The input parameters during the forming process play a crucial role in determining the final product's formability in terms of maximum formable depth (MFD) and surface quality in terms of average surface roughness (Ra). The present research aims to optimise the forming parameters for formability and surface quality during SPIF of material AZ31 magnesium alloy. In the present research, AZ31 is selected as the target material due to its widely recognized excellent strength-to-weight ratio, making it ideal for lightweight applications in the automotive, aerospace, and electronics industries. The experiments are accomplished based on the use of a Taguchi's design of experiments. The tool diameter (TD), tool rotational speed (TRS), tool feed rate (TFR), and incremental step depth (ISD) were chosen as variable parameters and, keeping other parameters constant for maximum formable depth and average surface roughness as response parameters. The best parameter settings were found, and the statistically significant parameters of the responses were examined using the Analysis of Variance (ANOVA). The results revealed that the maximum formable depth (23.5 mm), representing the material's formability, increases with larger tool diameters (10 mm and 12 mm), and higher tool rotational speeds (5000 rpm and 6000 rpm) but decreases with higher tool feed rates (500 mm/min and 600 mm/min). On the other hand, surface roughness improves (decreases) with higher tool rotational speeds, while it increases with larger tool diameters, higher tool feed rates and greater incremental step depths. Furthermore, the findings of a confirmation experiment using the optimal conditions showed a good agreement with the experimental observation. Additionally, linear regression models for predicting the maximum forming depth and average surface roughness were developed by applying the response surface methodology (RSM), which also had good agreement with experiments conducted on optimal parameters.