Surface roughness Ra prediction in Selective Laser Melting of 316L stainless steel by means of artificial intelligence inference

被引：21

作者：

La Fé-Perdomo I. ^{[1
,2
]}

Ramos-Grez J. ^{[1
]}

Mujica R. ^{[1
]}

Rivas M. ^{[2
]}

机构：

[1] Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna, Santiago, Macul

[2] Centre for Advanced and Sustainable Manufacturing Studies, University of Matanzas, Autopista a Varadero, km 3½, Matanzas

来源：

Journal of King Saud University - Engineering Sciences | 2023年 / 35卷 / 02期

关键词：

Neural network; Neuro-fuzzy systems; Selective Laser Melting; Surface roughness;

D O I：

10.1016/j.jksues.2021.03.002

中图分类号：

学科分类号：

摘要：

Selective Laser Melting (SLM) is a widely used metal additive manufacturing process due to the possibility of elaborating complicated and customized tridimensional parts or components. This paper presents research on predicting surface roughness of 316L stainless steel manufactured SLM parts using the well-known multilayer perceptron (MLP) and an adaptive neuro-fuzzy inference system (ANFIS). Two models were adjusted to predict the top surface quality for different values of laser power, scanning speed, and hatch distance. The obtained results were evaluated and compared in order to ensure the goodness of fit of both techniques. The multilayer perceptron-based model has proved, to possess better predictive capability of the non-linear relationships of the SLM process. However, adequate results were also obtained with the adjusted ANFIS. The consistency of the presented models is also compared with previously published empirical formulations and discussed. As a final result, has been demonstrated that both fitted models outperform the previously published statistic-based approaches. © 2021 The Authors

引用

页码：148 / 156

页数：8

共 44 条

[1]

Abele E., Kniepkamp M., Analysis and optimisation of vertical surface roughness in micro selective laser melting, Surf. Topogr.: Metrol. Prop., 3, 3, (2015)

[2]

Aboulkhair N.T., Everitt N.M., Ashcroft I., Tuck C., Reducing porosity in AlSi10Mg parts processed by selective laser melting, Addit. Manuf., 1-4, pp. 77-86, (2014)

[3]

Al-Ahmari A.M., Direct Digital Manufacturing, J. King Saud Univ. – Eng. Sci., 29, 3, (2017)

[4]

Alrbaey K., Wimpenny D., Tosi R., Manning W., Moroz A., On optimization of surface roughness of selective laser melted stainless steel parts: a statistical study, J. Mater. Eng. Perform., 23, 6, pp. 2139-2148, (2014)

[5]

Aqilah D.N., Sayuti A.K.M., Farazila Y., Suleiman D.Y., Amirah M.A.N., Izzati W.B.W.N., Effects of process parameters on the surface roughness of stainless steel 316L parts produced by selective laser melting, J. Test. Eval., 46, 4, pp. 1673-1683, (2018)

[6]

Beruvides G., Castano F., Quiza R., Haber R.E., Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes, Measurement, 86, pp. 246-252, (2016)

[7]

Boillat E., Kolossov S., Glardon R., Loher M., Saladin D., Levy G., Finite element and neural network models for process optimization in selective laser sintering, Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf., 218, 6, pp. 607-614, (2004)

[8]

Campanelli S., Contuzzi N., Ludovico A., Caiazzo F., Cardaropoli F., Sergi V., Manufacturing and characterization of Ti6Al4V lattice components manufactured by selective laser melting, Materials, 7, pp. 4803-4822, (2014)

[9]

Casalino G., Campanelli S.L., Contuzzi N., Ludovico A.D., Experimental investigation and statistical optimisation of the selective laser melting process of a maraging steel, Opt. Laser Technol., 65, pp. 151-158, (2015)

[10]

Cortez P., (2020)

← 1 2 3 4 5 →