Multi-response optimization of hard turning parameters: a comparison between different hybrid Taguchi-based MCDM methods

Sristi, Nafisa Anzum; Zaman, Prianka B.; Dhar, Nikhil Ranjan

doi:10.1007/s12008-022-00849-6

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…Finally, Figure 12(a)–(d) exposes the evaluation of ( Ecs ) as a function of ( f and ap ) and three different ( Vc ) for the cutting material used. 52 It can be observed that ( Ecs ) decreases as ( Vc , f , and ap ) increase. This is beneficial for cutting tools.…”

Section: Resultsmentioning

confidence: 91%

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Gasmi,

Yallese,

Boucherit

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study focuses on the performance evaluation of CBN and ceramic tools in dry machining of gray cast iron EN GJL-350. The machining factors taken into account during turning are: cutting speed ( Vc), feed rate ( f), depth of cut ( ap), and cutting tool material (CBN, white ceramic, mixed ceramic, and silicon nitride). The first part of this investigation concerns the evaluation of the four cutting materials performance used in terms of tool wear evolutions, 2D and 3D surface roughness and cutting forces variation according to working parameters. The second part exposes the results according to L32 Taguchi design of experiment. Statistical treatment by ANOVA allowed to quantify the impact of the input factors on the performance parameters, namely the surface roughness ( Ra), the cutting force ( Fz), the cutting power ( Pc), and the specific cutting energy ( Ecs). The response surface methodology (RSM), and the artificial neural network (ANN) approach were adopted to develop mathematical models for predicting the different output parameters. The results of the two methods were compared and discussed. A multi-criteria optimization was performed using the desirability function (DF) approach. The genetic algorithm (GA) was also applied to find pareto fronts. The results found show that CBN is the most efficient material in terms of lower tool wear, surface roughness and cutting forces. The DF method allowed to find an optimal combination ( Vc = 660 m/min, f = 0.13 mm/rev, ap = 0.232 mm, and the CBN material) leading to a compromise between the minimization of ( Ra, Fz, Pc, and Ecs) and the maximization of (MRR). The Pareto fronts found by the (GA) method make it possible to propose a multitude of solutions according to the desired objectives.

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Section: Resultsmentioning

confidence: 91%

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Gasmi,

Yallese,

Boucherit

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Similarly, the Ra of the MQL system was found to be lower in use compared to dry cutting. 14,15,31 In addition, built up edge (BUE) formation is reduced as the coolant lowers the temperature in the cutting zone. Supplementary Figure 4 also shows the BUE formed in the cutting tool under dry CC.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, it has been seen in studies that the use of MQL reduces the cutting forces compared to dry cutting. 31 Similarly, the use of CO 2 reduces thermal softening. As thermal softening decreases, vibration increases.…”

Section: Vibrationmentioning

confidence: 99%

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Şahinoğlu

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this experimental study, vibration, energy consumption, power consumption and surface roughness values that occur in the machining of AISI H11 tool steel under CO2, coolant and dry cutting conditions were investigated. The effects of different cutting parameters and cooling systems on machinability were investigated. Analysis of variance was done. Regression equations were obtained. Relationships between power consumption, vibration and surface roughness are explained with mathematical equations. Finally, the optimum CC were determined by the multiple optimization methods. According to the test results, while the instantaneous power consumption increases by increasing cutting parameters, energy consumption decreases as the processing time is shortened. Vibration value increases by increasing cutting parameters. The highest vibration value occurs in cutting with CO2. Compared to dry and CO2 cutting, the vibration is lowest in coolant cutting. The friction decreases with the coolant and the vibration value decreases. There is a similar relationship between vibration and surface roughness value. The most effective parameter on the surface roughness value is the feed rate. It was seen that the most suitable CC for the most efficient cutting, the lowest energy consumption, vibration and surface roughness value, under coolant cutting, 0.2 mm depth of cut, 175 m/min cutting speed and 0.119 mm/rev feed rate. With optimum CC, the vibration value was reduced by 5.18%, the surface roughness value by 37.12%, energy consumption by 36.19% and the machine efficiency was increased by 7.16%.

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“…The ability to predict and optimization cutting force before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction and optimization of cutting force is currently determined by using various techniques such as theoretical models [14][15], FE method [4,[15][16][17], the Taguchi procedure [1,2,11,[17][18][19][20], response surface methodology (RSM) [13,[21][22][23], the Multi-Objective Ant Lion Optimizer MOALO [21] the multi-response TOPSIS method [3,19], artificial intelligence through the use of the artificial neural networks (ANNs) [15,, genetic algorithms (GAs) [18] and fuzzy logic (FL) [26]. any research works show the use of these methods in the forecasting and also optimization of cutting force [13].…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of Monte Carlo and RSM method for the optimization of cutting force during turning of NiTi shape memory alloy

Kowalczyk

2023

Materials Research Proceedings

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Abstract. This study was conducted to understand the exact turning of the NiTi shape memory alloy and consisted of four stages: experimental work, function modelling using RSM method, Monte Carlo method optimization and hardware implementation of Monte Carlo method . This article has the following main objectives: to develop a framework for solving machining optimization problems using the Monte Carlo method and hardware implementation of MC method. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the cutting forces components Fc, Fp and Ff during turning of NiTi shape memory alloy.

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Multi-response optimization of hard turning parameters: a comparison between different hybrid Taguchi-based MCDM methods

Cited by 16 publications

References 32 publications

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Experimental investigation on the performance of ceramics and CBN cutting materials during dry machining of cast iron: Modeling and optimization study using RSM, ANN, and GA

Investigation of machinability properties of AISI H11 tool steel for sustainable manufacturing

Hardware implementation of Monte Carlo and RSM method for the optimization of cutting force during turning of NiTi shape memory alloy

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