Theoretical and empirical coupled modeling on the surface roughness in diamond turning

He, Chao; Zong, Wenjun; Cao, Zhimin; Sun, Tao

doi:10.1016/j.matdes.2015.05.058

Cited by 43 publications

(15 citation statements)

References 22 publications

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“…The side flow of material has been taken into account to predict the machined surface roughness [156,162,164,169]. In 2006, Liu et al [164] established a relationship between the roughness due to side flow R p and the rheological factor x, which is formulated as…”

Section: Influence On Surface Generationmentioning

confidence: 99%

“…In determining the value of the coefficient k 3 , the feed rate and tool nose radius are considered. In 2015, He et al [169] thought the side flow is one of the uncertain components determining the surface roughness which is predicted empirically by a radial basis function (RBF) neural network. In 2016, a new prediction model was proposed by them [162].…”

Section: Influence On Surface Generationmentioning

confidence: 99%

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Recent Advances in Micro/Nano-cutting: Effect of Tool Edge and Material Properties

Fang

2018

Nanomanuf Metrol

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Micro-and nano-machining technology has been applied in industry to generate high-precision parts with micro/nanometric accuracy or feature size in the recent decades. Cutting is one of the most powerful manufacturing processes, and the material removal mechanism is urgently demanded by the industry to understand and improve the micro/nano-machining process efficiently at a low cost. This paper presents the recent advances in cutting mechanism and its applicability for predicting the surface generation and chip formation, especially when material is removed in micro-and nanoscale. In addition to the industry-concerned performance parameters, fundamental physical parameters such as stresses, strains, temperatures, phase transformation, minimum uncut chip thickness and size effects are discussed in this paper for the indepth understanding of the micro/nano-cutting process.

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Section: Influence On Surface Generationmentioning

confidence: 99%

Section: Influence On Surface Generationmentioning

confidence: 99%

Recent Advances in Micro/Nano-cutting: Effect of Tool Edge and Material Properties

Fang

2018

Nanomanuf Metrol

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“…The common aim of these studies is to obtain the optimum surface roughness and determine the best parameters for the factors affecting surface roughness. The two most important parameters affecting the roughness of the surface are the feed rate and insert geometry [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Statistical Investigation of Surface Roughness in Turning of AISI 4140 Steel

Akkuş

2019

Sakarya University Journal of Science

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In this study, 48 HRC hardness AISI 4140 is turned on in different cutting parameters and cooling environment. The Taguchi L9 test design was developed based on the three-level cutting speed (V), feed rate (f), depth of cut (a) and cooling environment parameters. According to the L9 experimental design, the mean surface roughness (Ra) values were measured. Chip form occurring during turning is photographed. The S/N (Signal/Noise) ratios of the Taguchi experiment design in the Minitab program have been determined. According to the experimental results, the most significant effect on the Ra from the four factors was found in the hand made by the depth of cut. In ANOVA, it was respectively determined that depth of cut, cutting speed, feed rate and cooling environment affected 95% confidence in Ra value. It has been found that the repeat experiments for the optimum parameters yielded about 90% accuracy compared to the Taguchi estimate.

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“…Ho et al [12] put forward a surface roughness model using an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm, which had been validated by trails and the mean relative error was 4.65%. To improve the prediction accuracy, Zong et al [13] and He et al [14] presented a theoretical model coupled with RBF network to predict surface roughness in single point diamond turning operations. In addition, subsequently, the PSO algorithm was employed to acquire optimal parameters.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness modeling and milling parameter optimization of 15-5PH stainless steel in milling arc surface

Zheng

Yao

Dong

2018

J Braz. Soc. Mech. Sci. Eng.

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In this study, a comprehensive prediction model of surface roughness on arc surface of 15-5PH stainless steel is constructed, which is considered to consist of the certain part and uncertain part. The certain part considered to be dependent on path interval and scallop height is formulated in theory using kinematics analysis and geometric modeling. The uncertain part related to the machining system stability, cooling condition and milling modes is empirically predicted by partial least squares based on experimental data. Then, the prediction model is integrated into particle swarm optimization algorithm to find the optimal parameters for the maximum material removal rate (MRR). Finally, the verification experiments considering validation of prediction model and optimal parameters are conducted. The experimental results show that the prediction accuracy presents well with the mean prediction error 10.49%. The optimization also satisfies the constraint of surface roughness with a 262% increase in MRR.

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Theoretical and empirical coupled modeling on the surface roughness in diamond turning

Cited by 43 publications

References 22 publications

Recent Advances in Micro/Nano-cutting: Effect of Tool Edge and Material Properties

Recent Advances in Micro/Nano-cutting: Effect of Tool Edge and Material Properties

Experimental and Statistical Investigation of Surface Roughness in Turning of AISI 4140 Steel

Surface roughness modeling and milling parameter optimization of 15-5PH stainless steel in milling arc surface

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