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RISS 인기검색어
- 검색키워드
- 저자 : Dinesh Kumar Kasdekar (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Development of Artificial Intelligence Model for the Prediction of MRR in Turning
Vinay Kumar Chaurasia,Dinesh Kumar Kasdekar,Vaibhav Shivhare 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.2
In machining operations, the extents of important effect of the process parameters like speed, feed, and depth of cut are different for different responses. This paper investigates the effect of process parameters in turning of AA6061 T6 on conventional lathe. The problem appeared owing to selection of parameters increases the deficiency of turning process. Modeling can facilitate the acquisition of a better understanding of such complex process, save the machining time and make the process economic. Thus, the present work clearly defines the development of an artificial neural network (ANN) model for predicting the material removal rate. This study presents a new method to prediction the material removal rate (MRR) on a lathe turning Process. Firstly, Process parameters namely, Spindle speed, depth of cut and feed rate are designed using the Box behnken (DOE) was employed as the experimental strategy. The result shows that the ANN model can predict the material removal rate effectively. This approach helps in economic lathe machining.
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Application of GRA for Optimal Machining Parameter Selection in EDM
BrijKishor Singh,Dinesh Kumar Kasdekar,Vishal Parashar 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Electrical discharge machining (EDM) is one of the most extensively used nonconventional material removal process. The Taguchi method has been utilized to determine the optimal EDM conditions in several industrial fields. The method was design to optimize only a single performance characteristic. To remove this limitation, the Grey relational theory has been used to resolve the complicated interrelationship among the multiple performance characteristics. In the present study, we attempt to find the optimal machining conditions under which the Material removal rate(MRR) to be maximize and Tool wear rate(TWR) to be minimize. This paper summarizes the Grey relation theory and Taguchi optimization technique in order to optimize the cutting parameters in EDM for SS304. The Taguchi method was used to determine the relations between the machining and Response parameters. GRA was used to investigate the optimal machining parameters, among which the pulse on-time, pulse off-time are found to be the most desirable. Finally optimal machining conditions are pulse on time (50 μs), pulse off time (35 μs), discharge current (12A), and voltage (50V). Experimentation was planned as per Taguchi’s L9 (33) orthogonal array. Analysis of variance (ANOVA) is applied to identify the level of importance of the machining parameters on the multiple performance characteristics considered. Finally confirmation result was carried out to identify the effectiveness of this proposed method.
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