Parametric optimization of magnetic-field-assisted abrasive flow machining by the Taguchi method

2002 ◽  
Vol 18 (4) ◽  
pp. 273-283 ◽  
Author(s):  
Sehijpal Singh ◽  
H. S. Shan ◽  
Pradeep Kumar
Keyword(s):  
Magnetic Field ◽  
Taguchi Method ◽  
Parametric Optimization ◽  
Abrasive Flow Machining

scholarly journals Parametric Optimization of Electroless Ni-P coatings using Taguchi method

2019 ◽  
Vol 653 ◽  
pp. 012001
Author(s):  
Subhasish Sarkar ◽  
Rishav Kumar Baranwal ◽  
Rajat Subhra Sen ◽  
Buddhadeb Oraon ◽  
Siddhartha Mukherjee ◽  
...  
Keyword(s):  
Taguchi Method ◽  
Parametric Optimization ◽  
Electroless Ni

Optimization to the parameters of abrasive flow machining by Taguchi method

2018 ◽  
Vol 5 (2) ◽  
pp. 4720-4729 ◽  
Author(s):  
Ravi Butola ◽  
Rishabh Jain ◽  
Priyesh Bhangadia ◽  
Ashwani Bandhu ◽  
R.S Walia ◽  
...  
Keyword(s):  
Taguchi Method ◽  
Abrasive Flow Machining

Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sakthivel Murugan R. ◽  
Vinodh S.
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Parametric Optimization ◽  
Multiple Objective ◽  
Fused Deposition Modelling ◽  
Automotive Component ◽  
Content Type ◽  
Fused Deposition ◽  
Relative Closeness ◽  
Ranking Order

Purpose This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation. Design/methodology/approach The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done. Findings The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA. Research limitations/implications In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order. Practical implications The study presents the case of an automotive component, which illustrates practical relevance. Originality/value In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

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