Multi Objective Optimization of Turning Operation Using Hybrid Decision Making Analysis

Sofuoğlu, Mehmet Alper; Arapoğlu, R. Aykut; Orak, Sezan

doi:10.18038/aubtda.287801

Cited by 17 publications

(13 citation statements)

References 34 publications

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“…Reference Ideal Method (RIM) is a method used for multi-objective optimization, which was first introduced in 2014 [33]. This method has been applied for multi-objective optimization in the selection of military aircraft for the Spanish army forces [34], and for optimization of turning process [35]. However, up to now, there have been no published studies on the application of this method for multi-objective optimization of milling process.…”

Section: Engineeringmentioning

confidence: 99%

Study on multi-objective optimization of X12M steel milling process by reference ideal method

Lam

2021

Eureka: PE

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For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. Milling is a popular machining method in the machine building industry. This is considered to be one of the most productive machining methods, capable of machining many different types of surfaces. With the development of the cutting tool and machine tool manufacturing industries, this method is increasingly guaranteed with high precision, sometimes used as the final finishing method. Milling using a face milling cutter is more productive than using a cylindrical cutter because there are multiple cutter s involved at the same time. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is X12M steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. The Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study

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Section: Engineeringmentioning

confidence: 99%

Study on multi-objective optimization of X12M steel milling process by reference ideal method

Lam

2021

Eureka: PE

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“…There is still a lack of specialized work in HRES, in fact by selecting the best hybrid configuration among the energy sources in a particular place by considering various parameters such as economic, technical, and environmental factors. Hence, considering all these essential requirements, the aim of this proposed work is focused on optimum configuration planning and sizing of the different components used in the HRES through Multicriteria Decision Analysis [28] in the study area southern region of India (Karaikudi, Tamilnadu) and to help the decision-makers and identifies the sustainable HRES configuration by including the multicriteria indicators using BWM MCDA method.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Decision Analysis for Renewable Energy Integration Design: For an Institutional Energy System Network

Dharani

Balasubramonian

2021

International Journal of Photoenergy

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The hybridization of a renewable energy generator with an existing system in a specific building plays a vital role in solving many important problems of electrical energy systems such as reliability, cost-effective, and ecological option. In this perspective, a modified energy system is proposed in an existing building with integrated renewable energy using multicriteria decision analysis (MCDA). To achieve the sustainable energy solution, the hybrid power source configurations available in the locations are analyzed to ensure reliability using the HOMER software tool. Among these, the best configuration is identified by using the Best Worst Method (BWM) based MCDA approach. The study examined the technical, economic, and environmental perspectives for the achievement of the Sustainable Development Goals (SDGs) such as affordable, reliable, sustainable, and modern energy. The results of the survey in the building show that the best optimal hybrid configuration of the proposed location is the Gird-PV-Wind-based systems for the specific location based on the key techno-economic-environmental criteria of the SDGs.

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“…In order to solve this problem, techniques such as hybrid decision analysis, Taguchi method, and gray correlation analysis are used for multi-objective optimization of cutting parameters. 23–25 However, accurate prediction of surface roughness is the basis for processing optimization. Thus, as an important indicator for evaluating the processing quality, surface roughness also needs to be accurately predicted.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

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Multi Objective Optimization of Turning Operation Using Hybrid Decision Making Analysis

Cited by 17 publications

References 34 publications

Study on multi-objective optimization of X12M steel milling process by reference ideal method

Study on multi-objective optimization of X12M steel milling process by reference ideal method

Multicriteria Decision Analysis for Renewable Energy Integration Design: For an Institutional Energy System Network

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

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