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Enhancing machining performance of Inconel 825 alloy using grey relation analysis and JAYA-TLBO optimization techniques
https://orcid.org/http://orcid.org/0000-0001-8899-3569
The widespread application of Inconel alloy in the automotive and aerospace industries underscores the significance of optimizing machining parameters. This optimization is crucial for enhancing the performance outcomes of the machining process, directly impacting both product quality and cost-effectiveness. In machining operations, variables such as feed rate (f), cutting speed (S), and depth of cut (t) play pivotal roles. The resulting output performance indices of particular relevance include cutting force (Rf), material removal rate (MRR), and surface roughness (Ra). ANOVA has been done to find the influential variable on the output responses and it has been noticed that the ‘t’ was the most influential variable, followed by ‘f’ with respect to the Fr, Ra, and MRR during machining of Inconel 825. This study focuses on evaluating the overall process performance during the machining of Inconel 825 alloy. To achieve this, grey relation analysis is employed to transform multiple output variables into a unified response known as the overall grey relation index (OGI). Subsequently, a nonlinear regression model is developed, which expresses the OGI as a function of the aforementioned input variables. For the optimization process, the JAYA and TLBO algorithms are utilized, employing the OGI as the fitness function. The optimized settings determined are a feed rate of 0.333 mm/rev, a cutting speed of 247 RPM, and a depth of cut of 0.4 mm, resulting in an approximate fitness function value of 0.6982 for maximizing the OGI. To validate the solution, a confirmatory test is conducted, revealing an improvement of 1.9% in the OGI.
Enhancing machining performance of Inconel 825 alloy using grey relation analysis and JAYA-TLBO optimization techniques
https://orcid.org/http://orcid.org/0000-0001-8899-3569
The widespread application of Inconel alloy in the automotive and aerospace industries underscores the significance of optimizing machining parameters. This optimization is crucial for enhancing the performance outcomes of the machining process, directly impacting both product quality and cost-effectiveness. In machining operations, variables such as feed rate (f), cutting speed (S), and depth of cut (t) play pivotal roles. The resulting output performance indices of particular relevance include cutting force (Rf), material removal rate (MRR), and surface roughness (Ra). ANOVA has been done to find the influential variable on the output responses and it has been noticed that the ‘t’ was the most influential variable, followed by ‘f’ with respect to the Fr, Ra, and MRR during machining of Inconel 825. This study focuses on evaluating the overall process performance during the machining of Inconel 825 alloy. To achieve this, grey relation analysis is employed to transform multiple output variables into a unified response known as the overall grey relation index (OGI). Subsequently, a nonlinear regression model is developed, which expresses the OGI as a function of the aforementioned input variables. For the optimization process, the JAYA and TLBO algorithms are utilized, employing the OGI as the fitness function. The optimized settings determined are a feed rate of 0.333 mm/rev, a cutting speed of 247 RPM, and a depth of cut of 0.4 mm, resulting in an approximate fitness function value of 0.6982 for maximizing the OGI. To validate the solution, a confirmatory test is conducted, revealing an improvement of 1.9% in the OGI.
Enhancing machining performance of Inconel 825 alloy using grey relation analysis and JAYA-TLBO optimization techniques
https://orcid.org/http://orcid.org/0000-0001-8899-3569
The widespread application of Inconel alloy in the automotive and aerospace industries underscores the significance of optimizing machining parameters. This optimization is crucial for enhancing the performance outcomes of the machining process, directly impacting both product quality and cost-effectiveness. In machining operations, variables such as feed rate (f), cutting speed (S), and depth of cut (t) play pivotal roles. The resulting output performance indices of particular relevance include cutting force (Rf), material removal rate (MRR), and surface roughness (Ra). ANOVA has been done to find the influential variable on the output responses and it has been noticed that the ‘t’ was the most influential variable, followed by ‘f’ with respect to the Fr, Ra, and MRR during machining of Inconel 825. This study focuses on evaluating the overall process performance during the machining of Inconel 825 alloy. To achieve this, grey relation analysis is employed to transform multiple output variables into a unified response known as the overall grey relation index (OGI). Subsequently, a nonlinear regression model is developed, which expresses the OGI as a function of the aforementioned input variables. For the optimization process, the JAYA and TLBO algorithms are utilized, employing the OGI as the fitness function. The optimized settings determined are a feed rate of 0.333 mm/rev, a cutting speed of 247 RPM, and a depth of cut of 0.4 mm, resulting in an approximate fitness function value of 0.6982 for maximizing the OGI. To validate the solution, a confirmatory test is conducted, revealing an improvement of 1.9% in the OGI.
Enhancing machining performance of Inconel 825 alloy using grey relation analysis and JAYA-TLBO optimization techniques
Int J Interact Des Manuf
Gandhi, Anadh (Autor:in) / Kumari, Soni (Autor:in) / Sunil Kumar, M. (Autor:in) / Rakesh Kumar, V. (Autor:in) / Abhishek, Kumar (Autor:in) / Bandhu, Din (Autor:in) / Revathi, V. (Autor:in) / Kumar, Ashish (Autor:in) / Abdullaev, Sherzod (Autor:in)
01.02.2025
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimisation , Machining , Turning , Inconel 825 , MRR , Surface , Roughness , JAYA , TLBO , Grey , Relation , Analysis Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design