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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63307
Volume:
10 |
5 |
May 2026
Pages:
1-16
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Mechanical Engineering
Annamacharya Institute of Technology and sciences
Mechanical Engineering
Annamacharya Institute of Technology and sciences
In highly competitive manufacturing industries nowadays every manufacturing industry aims at producing a large number of products within relatively lesser time, but it is felt that reduction in manufacturing time may causes server quality loss. In order to embrace these two conflicting criteria it is necessary to check quality level of the item either on-line or off-line. In on-line quality control controller and related equipments are provided with the job under operation and continuously the quality is being monitored. In off-line quality control the method is either to check the quality of few products from a batch or lot (acceptance sampling) or to evaluate the best process environment capable of producing desired quality product. This invites optimization problem which seeks identification of the best process condition or parametric combination for the said manufacturing process. If the problem is related to a single quality attribute then it is called single objective (or response) optimization. If more than one attribute comes into consideration it is very difficult to select the optimal setting which can achieve all quality requirements simultaneously. This study investigated the multi-optimization of the turning process on AISI1045 steel material with CNMG cutting tool for an optimal parametric combination to yield the minimum surfaces roughness with the maximum MRR using a combination of a Grey relational analysis (GRA),Utility concept and the Taguchi method. In view of the fact, that traditional Taguchi method contact solves a multi-objective optimization problem: To overcome this limitations Grey relation theory and Utility concept has been coupled with Taguchi method. Nine experimental runs based on an Orthogonal array of the Taguchi method were performed to drive objective functions to be optimized within the experimental domain the signification of the factors on the overall quality characteristics of the cutting process was also evaluated quantitatively using the Analysis of Variance ANOVA method. Optimal results verified through additional experiments.
KEYWORDS
1. Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library Metadata Architecture. Int. J. Digit. Libr. In this study, the Utility concept and Grey-based Taguchi method was applied for the multiple performance characteristics of turning operations. Utility concept and grey relational analysis of the material-removal rate and the surface roughness obtained from the Taguchi method reduced from the multiple performance characteristics to a single performance characteristic which is called the utility index grade and grey relational grade. Therefore, the optimization of the complicated multiple performance characteristics of the processes can be greatly simplified using the extended Taguchi method. It is also shown that the performance characteristics of the turning operations, such as the material removal rate, the cutting time and the surface roughness are greatly enhanced by using this method. The aforesaid extended Taguchi method can be applied for continuous quality improvement of the product/process and off-line quality control. According to this analysis, the most effective parameters with respect to the material-removal rate and the surface roughness are the feed rate, depth of cut, cutting speed and tool nose radius. The percentage contribution indicates the relative power of a factor to reduce the variation. For a factor with a high percentage contribution, there is a great influence on the performance. The percent contributions of the cutting parameters on the material-removal rate and the surface roughness are shown in table (10.3&10.4.) From the table 10.3 factors A (Cutting Speed), B (Feed rate) and C (Depth of cut), and D (tool nose radius) has significant effect on the output parameters. The depth of cut has high influence (57.47%) on surface roughness Ra and feed has (27.16%) of influence on surface quality character Ra. the cutting speed were found to be the second- and third-ranking factors respectively. From the table 10.4 factors A (Cutting Speed), B (Feed rate) and C (Depth of cut), and D (tool nose radius) has significant effect on the output parameters. The depth of cut has high influence (54.94%) on surface roughness Ra and feed has (27.93%) of influence on surface quality character Rq. the cutting speed were found to be the second- and third-ranking factors respectively. In this study, the Utility concept and Grey-based Taguchi method was applied for the multiple performance characteristics of turning operations. Utility concept and grey relational analysis of the material-removal rate and the surface roughness obtained from the Taguchi method reduced from the multiple performance characteristics to a single performance characteristic which is called the utility index grade and grey relational grade. Therefore, the optimization of the complicated multiple performance characteristics of the processes can be greatly simplified using the extended Taguchi method. It is also shown that the performance characteristics of the turning operations, such as the material removal rate, the cutting time and the surface roughness are greatly enhanced by using this method. The aforesaid extended Taguchi method can be applied for continuous quality improvement of the product/process and off-line quality control. According to this analysis, the most effective parameters with respect to the material-removal rate and the surface roughness are the feed rate, depth of cut, cutting speed and tool nose radius. The percentage contribution indicates the relative power of a factor to reduce the variation. For a factor with a high percentage contribution, there is a great influence on the performance. The percent contributions of the cutting parameters on the material-removal rate and the surface roughness are shown in table (10.3&10.4.) From the table 10.3 factors A (Cutting Speed), B (Feed rate) and C (Depth of cut), and D (tool nose radius) has significant effect on the output parameters. The depth of cut has high influence (57.47%) on surface roughness Ra and feed has (27.16%) of influence on surface quality character Ra. the cutting speed were found to be the second- and third-ranking factors respectively. From the table 10.4 factors A (Cutting Speed), B (Feed rate) and C (Depth of cut), and D (tool nose radius) has significant effect on the output parameters. The depth of cut has high influence (54.94%) on surface roughness Ra and feed has (27.93%) of influence on surface quality character Rq. the cutting speed were found to be the second- and third-ranking factors respectively.
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Publication Date
5/16/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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