Application of the Monte Carlo method for the optimization of surface roughness during precise turning of NiTi shape memory alloy

Kowalczyk, M.

doi:10.1117/12.2501421

Cited by 5 publications

(12 citation statements)

References 12 publications

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“…x n (7) corresponds to the mean Sa and Sz [47,63]. The uncertainty of type B associated with the measurement of factors Sa and Sz is:…”

Section: Results Of the Measurements And Associated Uncertainties Obtained Experimentallymentioning

confidence: 99%

“…However, it should be remembered that the practical use of these materials is inextricably corresponded to the methods of their production and treatment. In order to manufacture new products from difficult-to-machine materials, such as shape memory alloys, titanium alloys, nickel alloys, and special ceramics, there is a need to search for more and more effective treatment methods that exceed technological barriers [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the great advancement of research in the field of forecasting and optimization of the various machinability indices (surface roughness, tool wear, etc.) in relation to the processing of various construction materials, the literature lacks or highlights difficulties in finding items for which a detailed assessment of the issues related to prediction and optimization of surface roughness in the process of turning shape memory alloys has been carried out [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The ability to predict surface roughness before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction of surface roughness is currently determined by using various techniques such as theoretical models [ 3 , 32 , 33 , 34 , 35 ], response surface methodology (RSM) [ 3 , 6 , 9 , 36 , 37 , 38 ], the Taguchi procedure [ 3 , 6 , 28 , 38 , 39 , 40 , 41 , 42 , 43 ], multiple linear regression equations [ 44 ], the Monte Carlo (MC) method [ 7 , 24 , 33 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], artificial intelligence through the use of the artificial neural networks (ANNs) [ 1 , 3 , 26 , 29 , 30 , 53 , 54 , 55 , 56 ], genetic algorithms (GAs) [ 3 , 57 ], fuzzy logic (FL) [ 3 , 36 , 54 , 58 , …”

Section: Introductionmentioning

confidence: 99%

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Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Kowalczyk

Tomczyk

2020

Materials

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The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

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“…x n (7) corresponds to the mean Sa and Sz [47,63]. The uncertainty of type B associated with the measurement of factors Sa and Sz is:…”

Section: Results Of the Measurements And Associated Uncertainties Obtained Experimentallymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Kowalczyk

Tomczyk

2020

Materials

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“…In order to manufacture new products from difficult-to-machine materials, such as shape memory alloys, there is a need to search for more and more effective treatment methods that exceed technological barriers [10][11][12]. Therefore, an adapted process strategy, prediction and optimization of cutting force is very important when machining NiTi [2,3,13].…”

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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Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

Zhao,

Cui,

Sivalingam

et al. 2023

Materials

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This study aimed to optimize machining parameters to obtain better surface roughness and remnant depth ratio values under dry turning of NiTi-shape memory alloy (SMA). During the turning experiments, various machining parameters were used, including three different cutting speeds vc (105, 144, and 200 m/min), three different feed rates f (0.05, 0.1, and 0.15 mm/rev), and three different depths of cut ap (0.1, 0.15, and 0.2 mm). The effects of machining parameters in turning experiments were investigated on the response surface methodology (RSM) with Box–Behnken design (BBD) using the Design Expert 11; how the cutting parameters affect the surface quality is discussed in detail. In this context, the cutting parameters were successfully optimized using a genetic algorithm (GA). The optimized processing parameters are vc = 126 m/min, f = 0.11 mm/rev, ap = 0.14 mm, resulting in surface roughness and remnant depth ratio values of 0.489 μm and 64.13%, respectively.

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Application of the Monte Carlo method for the optimization of surface roughness during precise turning of NiTi shape memory alloy

Cited by 5 publications

References 12 publications

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

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

Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

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