Home / Regular Issue / JTAS Vol. 32 (2) Mar. 2024 / JST-4498-2023


Optimal Constrained Groove Pressing Process Parameters Applying Modified Taguchi Technique and Multi-Objective Optimization

Muni Tanuja Anantha, Sireesha Koneru, Saritha Pyatla, Parameshwaran Pillai Thiruvambalam Pillai, Tanya Buddi and Nageswara Rao Boggarapu

Pertanika Journal of Tropical Agricultural Science, Volume 32, Issue 2, March 2024

DOI: https://doi.org/10.47836/pjst.32.2.21

Keywords: AA6061, displacement rate, grain size, micro hardness, number of passes, plate thickness, tensile strength

Published on: 26 March 2024

Most engineering problems are complicated, and developing mathematical models for such problems requires understanding the phenomena through experiments. It is well known that as processing parameters with assigned levels increase, so does the number of experiments. By minimizing the number of experiments, Taguchi’s method of experimental design will help to furnish the idea of full factorial experimental design. Taguchi’s method is more appropriate for single-objective optimization problems and needs modifications while dealing with multi-objective optimization problems. Aluminum alloys are in great demand in today’s automotive and aerospace sectors due to their low density, good corrosion resistance, and excellent machinability. The material is subjected to a constrained groove pressing (CGP) process to obtain microstructural grain refinement with enhanced mechanical behavior. This paper considers AA6061 material having major alloys such as silicon and magnesium. For this work, 3 CGP process parameters (viz., displacement rate, plate thickness and number of passes) are assigned 3 levels to each parameter, acquired the test data, viz., grain size (gs), micro hardness (hs), and tensile strength (ult) based on L9 orthogonal array of Taguchi. Using a modified version of Taguchi’s methodology, it is possible to estimate the range of grain size (gs), micro hardness (hs), and tensile strength (σult) for effective combinations of the CGP processing parameters and validate the results with existing test data. A more dependable and simpler multi-objective optimization procedure is used to choose the optimal CGP processing parameters.

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