Parametric optimization on hot air assisted hybrid machining of soda-lime glass using Taguchi based grey relational analysis

Autor: Y. Nagaraj, N. Jagannatha, N. Sathisha, S. J. Niranjana
Rok vydání: 2021
Předmět:
Zdroj: Multiscale and Multidisciplinary Modeling, Experiments and Design. 4:169-185
ISSN: 2520-8179
2520-8160
Popis: The present research underlines the development of a hybrid method for the machining of soda-lime glass known as the hot air assisted hybrid machining. It is a combination of conventional machining assisted with the jet of hot air. The influence of process variables such as feed of the cutting tool, flow of hot air, depth of cut, and the air temperature on the material removal rate (MRR) and surface roughness (Ra) applied to the grooving operation have been investigated. The Taguchi orthogonal array L27 was considered to reduce the number of experiments. The ANOVA was used to recognize the major influencing process parameters for the MRR and Ra. The results of ANOVA indicate that the air temperature is the most significant parameter for the objective of maximum MRR and minimum Ra with contributions ratios of 56.91% and 52.68% respectively for the grooving operation on soda-lime glass. The optimal machining parameters for the maximum MRR and minimum Ra were found to be A1–B1–C3–D3 and A1–B1–C1–D3 respectively. The multi-objective optimization was performed using the Taguchi based grey relational analysis (GRA). The optimal level of parameters based on GRA for maximum MRR and minimum Ra was found to be A1–B1–C3–D3. In addition, the material removal process was explained with the help of SEM micrographs.
Databáze: OpenAIRE
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