Parametric Optimization of Two Point Incremental Forming Using GRA and TOPSIS

Abstract

Different innovative forming methods have been developed in order to make custom made goods at a reasonable cost. Due to stress induced between the tool and die, the procedure facilitates the ability of metal sheets to be formed. The dimensional accuracy of formed items can be improved by selecting the best Two Point Incremental Forming (TPIF) process parameters. In this work, TPIF of SS316L sheets of 0.8 mm was performed by varying the process variables such as die angle, step of forming, rate of feed and tool rotational speed to form a double wall angle circular cone with a forming height of 40 mm. The output responses such as surface roughness and thickness of the formed components were measured. An ANOVA was performed with a confidence level of 95 % to identify the most influential process parameter on the output response. The accuracy of the proposed methods, Grey Relational Analysis (GRA) and TOPSIS, were validated by choosing the optimal process parameter combination resulted from several experimental trial runs.

Hybrid Taguchi-GRA-CRITIC Optimization Method for Multi-Response Optimization of Micro-EDM Drilling Process Parameters

“…GRA is based on the theory of grey relation space [33][34][35]. It measures the degree of correlation among factors based on their similarity or dissimilarity.…”

“…Therefore, normalizing the experimental data within the range of zero to one is required. Normalizing the data is known as pre-processing or grey relational generation [34]. Next, the Grey relational coefficient is calculated using normalized experimental data to indicate the correlation between the actual and desired experimental data.…”