Multi-objective parametric optimization on machining with wire electric discharge machining

KUMAR, KAPIL; Agarwal, Sanjay

doi:10.1007/s00170-011-3833-1

Cited by 74 publications

(35 citation statements)

References 26 publications

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“…For the multi-objective optimization problem, it is reasonable to find a Pareto Solution Set (PSS) instead of an optimal solution [38]. In order to do so, a GA is proposed.…”

Section: Optimization Algorithmmentioning

confidence: 99%

A joint optimization strategy for scale-based product family positioning

Ji¹,

Tang²,

Yu³

et al. 2014

IJCIS

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With the development of modern technologies and global manufacturing, it becomes more difficult for companies to distinguish themselves from their competitors. In order to keep their competitive advantages, companies must properly position their product families by offering a right product portfolio to each target market. To evaluate competitive advantages for a scale-based product family, this paper takes product family competitive advantage (PFCA) as a measure metric which is consisted of customer choice probability, sales, and profit. Meanwhile, to keep lower manufacturing costs, a commonality index of scale-based product family is proposed based on product design technology parameters in a product family. A multi-objective joint optimization model that balances the competitive advantages and the commonality is proposed. Based on a case study of motor product family positioning, Pareto frontier solutions are generated by genetic algorithm, and the results show that the joint optimization model excels in supporting product family positioning.

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“…For the multi-objective optimization problem, it is reasonable to find a Pareto Solution Set (PSS) instead of an optimal solution [38]. In order to do so, a GA is proposed.…”

Section: Optimization Algorithmmentioning

confidence: 99%

A joint optimization strategy for scale-based product family positioning

Ji¹,

Tang²,

Yu³

et al. 2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…The algorithm generates a set of evenly distributed solutions using non-dominated sorting and a crowdedcomparison approach. Nowadays, NSGA-II widely used in various fields of manufacturing process (Kondayya D and Krishna AG 2010, Kumar K and Agarwal S 2012, Senthilkumar C et al, 2010 due to the effectiveness and reduced computational complexity etc. Algorithm process of NSGA-II flow chart is shown in Figure 1.…”

Section: Non-dominated Sorting Genetic Algorithm-iimentioning

confidence: 99%

“…Algorithm process of NSGA-II flow chart is shown in Figure 1. The step-by-step procedure of algorithm as follows (Kumar K andAgarwal S 2012, Sheshadri A 2006): 1. Set the initial run parameters for the algorithm, viz.…”

Section: Non-dominated Sorting Genetic Algorithm-iimentioning

confidence: 99%

Investigations and optimization for hard milling process parameters using hybrid method of RSM and NSGA-II

2016

Rev. Téc. Ing. Univ. Zulia.

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The present work investigates the effect and optimization of process parameters on the cutting temperature, tool wear and metal removal rate during hard milling of 100MnCrW4 (AISI O1) tool steel using (TiN/TiAlN) coated carbide tools. The central composite rotatable design is utilized to plan the experiments. The empirical models were developed and analysis of variance tests were used for the investigation of significant parameters and adequacy of models. Scars, adhered materials and coating peel off were observed on the rake face of the tool due to the existence tool flank wear through the SEM and EDX analysis. In order to seek optimal parameters, a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted and a set of Pareto-optimal solution set was obtained. Further, a set of confirmation experiments were conducted and the adopted optimization method was proved to be feasible. Also, the results would be more useful to guide the actual hard milling process parameters for predicting the responses.Keywords Hard milling, Central composite rotatable design, Cutting temperature, Tool wear, Metal removal rate, NSGA-II. INTRODUCTIONHard milling is a viable green machining technique, which is more alternative to replace the grinding process at the semi-finish stage and also replacing the EDM process (Ding T et al., 2010 andDavim JP, 2011). The technology offers for significant savings in cost, good surface finish with large metal removal rate as compared with EDM (Gopalsamy BM et al., 2010). A successful implementation of hard milling describes that to boost the productivity with improved surface quality. On the other hand, it generates more fluctuated cutting forces and temperature as compared with conventional machining process. This causes severe tool wear in the cutting tool, then reduces the tool life and deteriorates machining quality. The tool wear is associated with cutting temperature and metal removal rate, being three important responses in hard milling to designate the required surface finish. Therefore, the influence of hard milling process parameters is still questionable to predict the quantitatively the technological performance of machining operations at economic level. Ding T et al. (2010) studied the effect of cutting speed, feed rate and depth of cut on the cutting forces, and surface roughness in hard milling of AISI H13 (50±1 HRC) steel with coated (TiN/TiAlN) carbide tool. Siller HR et al. (2009) demonstrated the special tool geometric features of coated carbide tools in face milling of hardened D3 tool steel (60HRC) components of dies and molds. The surface roughness was revealed in the range between 0.1-0.3 µm with an acceptable level of tool life. Zhang S et al. (2012) carried out experimental investigations on hard milling of AISI H13 (50±1HRC) tool steel to determine the effects of surface texture, cutting parameters, phase transformation and in-depth residual stress distributions on the surface. It has been concluded that the surface texture of machined surface highly cor...

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“…Kamal Jangra et al [4] performed optimization of multi machining characteristics namely material removal rate, surface roughness, angular error and radial overcut using grey relational analysis (GRA) coupled with entropy measurement method, while wire EDM of WC-5.3%Co composite. Kapil Kumar et al [5] optimized the machining conditions for maximum material removal rate and maximum surface finish based on multi-objective genetic algorithm for wire EDM of high-speed steel (M2, SKH9). Ali Vazini et al [6] performed multi objective optimization using mathematical model-desirability function approach and neural network integrated particle swarm optimization approach during dry wire cut machining of WC-10%Co.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization during wire EDM of WC-4.79%Co composite using controlled NSGA II

2017

IJMTER

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Abstract-Wire EDM being an effective machining process for composite materials needs proper selection of control factors to get the desired performance characteristics. But due to conflicting nature of performance characteristics it is unfeasible to find a single combination of control factors because desired combination may vary from situation to situation. That is why it is preferred to find a set of combination of control factors. In this work an attempt has been made to find set of combination of control factors for each workpiece thickness. Taguchi based L 18 orthogonal array has been used for conducting experiments. The performance characteristics considered are cutting rate and surface roughness. Workpiece thickness, peak current, pulse on time, pulse off time and wire feed rate are considered as input parameters. A mathematical model has been developed for each machining performance using least square method to create relationships between process factors and responses. Multi-objective optimization has been performed using controlled NSGA II to find a set of machining conditions for each thickness of workpiece using the obtained mathematical models as objective functions. The predicted results have been verified by conducting confirmation experiments.

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Multi-objective parametric optimization on machining with wire electric discharge machining

Cited by 74 publications

References 26 publications

A joint optimization strategy for scale-based product family positioning

A joint optimization strategy for scale-based product family positioning

Investigations and optimization for hard milling process parameters using hybrid method of RSM and NSGA-II

Multiobjective optimization during wire EDM of WC-4.79%Co composite using controlled NSGA II

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