Optimization of machining
parameters in face milling using multi-objective Taguchi technique

Fedai, Yusuf; Kahraman, Funda; Akin, Hediye Kirli; Başar, Gökhan

doi:10.31803/tg-20180201125123

Cited by 25 publications

(24 citation statements)

References 15 publications

(13 reference statements)

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“…Therefore, the mean value and signal-to-noise ratio (S/N) of the surface roughness (Ra) and tool wear (VB) are calculated by equation (3), which means the lower the better. 24 All the results are in Table 4.…”

Section: Experiments Projectmentioning

confidence: 99%

Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

Wang

Li²

2021

Advances in Mechanical Engineering

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To improve machining quality and processing efficiency, the Taguchi analysis method is employed to design the milling tests of titanium alloy TC17. According to results based on the signal-to-noise ratio method, the cutting depth plays a critical role in improving the surface roughness and tool wear. The grey correlation analysis is a multi-objective optimization method that can help to acquire process parameters combination of the optimal surface roughness and the optimal tool wear. Finally, the correctness of multi-objective optimization results is verified through comparison experiments. The research results can provide process guidance and data reference for the actual production processing.

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Section: Experiments Projectmentioning

confidence: 99%

Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

Wang

Li²

2021

Advances in Mechanical Engineering

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“…Finally, the Taguchi method has also been used together with the weighting method for the purpose of multi-objective optimization of AISI 4140 steel milling process [23]. TiAlN+TiN coating cutting tool was used in this study.…”

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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“…Tong et al (1997) and Antony (2001) have used the Taguchi based utility concept or a multi-objective optimization technique by transforming multiple responses to a single response through addition of normalized quality loss values with appropriate weight factors. Many researchers have considered the multiple quality characteristics simultaneously using the Taguchi quality loss function for multiple responses optimization (Gaitonde et al, 2009;Kaladhar et al, 2011;Fedai et al, 2018).…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

Dharmendra

Kodali

Boggarapu

2019

MMMS

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Purpose The purpose of this paper is to adopt the multi-objective optimization technique for identifying a set of optimum abrasive water jet machining (AWJM) parameters to achieve maximum material removal rate (MRR) and minimum surface roughness. Design/methodology/approach Data of a few experiments as per the Taguchi’s orthogonal array are considered for achieving maximum MRR and minimum surface roughness (Ra) of the Inconel718. Analysis of variance is performed to understand the statistical significance of AWJM input process parameters. Findings Empirical relations are developed for MRR and Ra in terms of the AWJM process parameters and demonstrated their adequacy through comparison of test results. Research limitations/implications The signal-to-noise ratio transformation should be applied to take in to account the scatter in the repetition of tests in each test run. But, many researchers have adopted this transformation on a single output response of each test run, which has no added advantage other than additional computational task. This paper explains the impact of insignificant process parameter in selection of optimal process parameters. This paper demands drawbacks and complexity in existing theories prior to use new algorithms. Practical implications Taguchi approach is quite simple and easy to handle optimization problems, which has no practical implications (if it handles properly). There is no necessity to hunt for new algorithms for obtaining solution for multi-objective optimization AWJM process. Originality/value This paper deals with a case study, which demonstrates the simplicity of the Taguchi approach in solving multi-objective optimization problems with a few number of experiments.

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Optimization of machining parameters in face milling using multi-objective Taguchi technique

Cited by 25 publications

References 15 publications

Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

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

Multi-objective optimization for optimum abrasive water jet machining process parameters of Inconel718 adopting the Taguchi approach

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