A comparison of the different multiple response optimization techniques for turning operation of AISI O1 tool steel

Kataria, Ravinder; Kumar, Jatinder

doi:10.7603/s40632-014-0030-z

Cited by 14 publications

(9 citation statements)

References 41 publications

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“…TOPSIS thus confers a resolution that is closest to hypothetically best and farthest to the hypothetically worst solution. The basic procedure of the TOPSIS method consists of the following steps (Kataria and Kumar 37 ):…”

Section: Resultsmentioning

confidence: 99%

Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Kataria

Kumar

Pabla

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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In manufacturing industries, WC–Co composite material is highly demanded due to its excellent properties such as toughness with hardness, good dimensional stability, and higher mechanical strength. However, the difficulties in its machining restrict the application and competitiveness of this material. This investigation is aimed at studying the impact of different experimental conditions (by varying cobalt content, thickness of workpiece, tool geometry, tool material, abrasive grit size, and power rating) on responses of interest (cutting ratio, overcut, and taper angle) in ultrasonic drilling of WC–Co composite material. The experiments have been planned using Taguchi’s L-36 orthogonal array, and analytic hierarchy process–based technique for order preference by similarity to ideal solution has been applied for optimization of multiple responses. Analysis of variance is also employed to identify the significant factors and the optimized process settings for the different responses. The experimental results showed that abrasive grit size and power rating are most influential for cutting ratio, overcut, and taper angle.

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Section: Resultsmentioning

confidence: 99%

Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Kataria

Kumar

Pabla

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Overall usefulness of the process or product represented by unified index termed as "overall utility", it's sum of individual utility of various quality parameters of the process or product. The methodology for utility approach is to convert the estimate value of each quality parameters into a common index [6,13]. If Xy is the effectiveness measure of a quality parameters (response) y, p evaluating attributes of the outcome space, and then the joint utility function expressed as below:…”

Section: Utility Theorymentioning

confidence: 99%

Multi Response Optimization of Submerged Arc Welding Using Taguchi Fuzzy Logic Based on Utility Theory

2017

IJSR

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Abstract:The submerged-arc welding (SAW) process is an essential metal-joining process used in industry. SAW is applied for pressure vessels, heat exchangers, shipbuilding, line piping, and petroleum industries. The present work investigated the optimization of SAW parameters of 10-mm thick SA516 grade 70 steel to achieve the desired mechanical properties of the weld. Three SAW process parameters were investigated, each at three different levels: welding current (300, 350, and 400 A); arc voltage (32, 36, and 40 V); and welding speed (26, 28, and 30 cm/min). Sample plates measuring 500×500×10 mm were used to test the parameters. The weld quality and properties were evaluated using the following response parameters: Ultimate Tensile Strength (UTS), Ultimate Bending Force (UBF), HB Macrohardness (HB), and the Charpy Impact Test (CIT). Utility-based fuzzy logic was used to convert the complex multiple objectives into a single utility, Multi Performance Characteristic Index (MPCI). The MPCI response values were measured using the fuzzy inference system, Mamdani type. The results revealed that the optimal SAW parameters are welding current 400 A, arc voltage 40 V, and welding speed 30 cm/min. All process parameters had significant effects based on analysis of variance (ANOVA) (P <0.05 for all). Welding current had a major effect (37.03%) on the response parameters, followed by welding speed (32.9%) and arc voltage (30.07%).

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“…Different researchers used the statistical techniques for the experimental design to investigate the predictive model of response characteristics (cutting cost, cutting forces, material removal rate, SR, tool wear, tool life) (Kataria and Kumar, 2014; Sharma et al , 2015; Gupta and Kumar, 2015; Jangra et al , 2016; Kataria et al , 2017; Jha et al , 2018; Singh et al , 2020; Gupta et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization while machining of stainless steel 316L using intelligent approach of grey theory and grey-TLBO

Sharma

Дабра²,

Singh

et al. 2021

WJE

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Purpose Stainless steel is widely used in different manufacturing sectors. The purpose of this study is to optimize the process parameters of machining while processing SS316L alloy. The optimization of machining characteristics in the case of SS316L alloy greatly improves the quality and productivity economically. Design/methodology/approach The machining variables in current research are depth of cut, spindle speed and feed rate. The optimization of response characteristics was carried out using the intelligent approach of grey, regression and teaching learning-based optimization (TLBO) and Taguchi-Grey approach. Planning of experiments was made using Taguchi’s based L27 orthogonal array. With the implementation of grey, the response characteristics were normalized and converted into a single response. The regression analysis was used for empirical modeling of the single response induced from the grey application. TLBO is further used to investigate the combinations of machining variables and compared with grey theory. Findings The grey-TLBO based multi-criteria decision-making approach suggests that the optimized setting for material removal rate, mean roughness depth (Rz) and cutting force (Fz) is spindle speed (N): 720 rpm; feed rate (F): 0.3 mm/rev; depth of cut (DoC): 1.7 mm. The grey theory suggests an optimized setting as N: 720 rpm; F: 0.2 mm/rev and DoC: 1.7 mm. Originality/value The parametric optimization during the turning of SS316L using grey-TLBO based intelligent approach is not performed till now. Thus, this intelligent approach will give a path to the researchers working in this direction. However, the grey theory performs better as compared to the grey-TLBO approach.

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A comparison of the different multiple response optimization techniques for turning operation of AISI O1 tool steel

Cited by 14 publications

References 41 publications

Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Ultrasonic machining of WC–Co composite material: Experimental investigation and optimization using statistical techniques

Multi Response Optimization of Submerged Arc Welding Using Taguchi Fuzzy Logic Based on Utility Theory

Multi-response optimization while machining of stainless steel 316L using intelligent approach of grey theory and grey-TLBO

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