A selection of material using a novel type decision-making method: Preference selection index method

Maniya, Kalpesh D.; Bhatt, M. G.

doi:10.1016/j.matdes.2009.11.020

Cited by 345 publications

(179 citation statements)

References 15 publications

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“…The author also used principle component analysis (PCA) to benchmark each other to get better decision in selection of the materials. Maniya and Bhatt [53] studied three difference types of materials selection problems and conclude that PSI is the most appropriate technique in materials selection. In addition, the relative importance between alternative materials selection attributes is finally defined the best solution and it is the beauty of PSI method.…”

Section: Preference Selection Index (Psi)mentioning

confidence: 99%

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Muhammad¹,

Sapuan²,

Mastura³

2018

J. MECH. ENG. SCI.

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Materials selection in manufacturing process is an important stage and should be performed in parallel with selection of manufacturing process. In automotive industry, production of green automotive component could utilize the natural sources such as plant fibres. In recent years, several multi-criteria decision-making (MCDM) techniques are suggested to choose the best materials for particular application. Materials selection tools for natural fibre composites are studied from past researchers with the summary of the advantage and disadvantages. In addition, new optimization approach in materials selection by using statistical analysis such as multiple linear regression (MLR), response surface methodology (RSM) and Taguchi method (TM) is proposed in this study. The proposed method could evaluate the criteria or attribute in materials selection precisely by analyse the relationship of the parameters, goodness of fit, correlation, analysis of variance (ANOVA), determination of coefficient and the significant criteria in to the desired goal of the design problem.

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Section: Preference Selection Index (Psi)mentioning

confidence: 99%

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Muhammad¹,

Sapuan²,

Mastura³

2018

J. MECH. ENG. SCI.

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“…Preference selection index method was developed by Maniya and Bhatt [10] for solving the multi-criteria decision-making problems. In this approach, it is not necessary to assign a relative importance between attributes.…”

Section: Methodology 31 Preference Selection Indexmentioning

confidence: 99%

PSI and TOPSIS Based Selection of Process Parameters in WEDM

Diyaley

Shilal²,

Shivakoti

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionWith the development of technology, more challenging problems are being faced by the technologists in the field of manufacturing. The difficulty in adopting the conventional manufacturing processes can be attributed mainly to the machining principle that requires a cutting tool which must be harder than the material of the work-piece. New materials which have a high strength-to-weight ratio, heat resistance and high hardness are tough to machine and also timeconsuming by conventional methods. Hence, advanced machining processes are being developed to overcome these difficulties. These newly developed techniques provide a very high accuracy and can machine complexly shaped surfaces. These processes, however, are not meant for replacing the conventional processes but are a supplement to them.Optimization and selection of proper process parameters play a vital role in any of the manufacturing or metal processing industries. Selection of proper cutting conditions is essential for efficient machining. Improper selection of machining parameters leads to several adverse effects during machining. Optimization technique provides a proper selection of cutting parameters. Many research attempts have been made to select the proper cutting parameters in WEDM. In past response surface methodology has been used to enumerate the relationship between input and output responses in the WEDM process and the effects of input process parameters on MRR and surface roughness were studied by Gowd et al. [1]. Tilekar et al. [2] investigated to analyze the effect of process parameters on surface roughness and kerf width of aluminium and mild steel. Taguchi method opted for the optimization. Spark on time, spark off time, input current and wire feed rate were used as input parameters and ANOVA results showed that spark on time and input current have statistically significant effect in the case of aluminium and mild steel respectively. Dabade and Karidkar [3] analyzed the machining conditions of material removal rate, surface roughness and kerf width and dimensional deviation during WEDM of Inconel 718 using Taguchi technique. It was observed that pulse on time was the most influencing factor. Patel et al. [4] applied AHP/TOPSIS

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“…The process of transforming attributes value into a range of [0,1] is called normalization and it is required in MADM methods to transform performance rating with different data measurement unit in a decision matrix into a compatible unit [19][20][21]. The normalization method adopted from the paper [22], and the formulas are given as follows: Step 5.…”

Section: Group Eigenvalue Methodsmentioning

confidence: 99%

A Novel Machine Selection Method Combining Group Eigenvalue Method with TOPSIS Method

Yang¹,

Xiong³

2016

IJFGCN

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A selection of material using a novel type decision-making method: Preference selection index method

Cited by 345 publications

References 15 publications

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

Multi-criteria decision-making tools for material selection of natural fibre composites: A review

PSI and TOPSIS Based Selection of Process Parameters in WEDM

A Novel Machine Selection Method Combining Group Eigenvalue Method with TOPSIS Method

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