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
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
收藏
页码:148 / 156
页数:8
相关论文
共 44 条
[11]  
Chen Z., Wu X., Tomus D., Davies C.H.J., Surface roughness of Selective Laser Melted Ti-6Al-4V alloy components, Addit. Manuf., 21, pp. 91-103, (2018)
[12]  
Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 2, pp. 182-197, (2002)
[13]  
Delgado J., Ciurana J., Rodriguez C.A., Influence of process parameters on part quality and mechanical properties for DMLS and SLM with iron-based materials, Int. J. Adv. Manuf. Technol., 60, 5-8, pp. 601-610, (2012)
[14]  
Deng Y., Mao Z., Yang N., Niu X., Lu X., Collaborative optimization of density and surface roughness of 316L stainless steel in selective laser melting, Materials, 13, (2020)
[15]  
Elsayed M., Ghazy M., Youssef Y., Essa K., Optimization of SLM process parameters for Ti6Al4V medical implants, Rapid Prototyping J., 25, 3, pp. 433-447, (2019)
[16]  
El-Tamimi A.M., Evaluation of the implementation of business practices and advanced manufacturing technology (AMT) in Saudi Industry, J. King Saud Univ. – Eng. Sci., 22, 2, pp. 139-151, (2010)
[17]  
Forrest S., Mitchell M., Adaptive computation: The multidisciplinary legacy of John H Holland, Commun. ACM, 59, 8, pp. 58-63, (2016)
[18]  
Frazier W.E., Metal additive manufacturing: a review, J. Mater. Eng. Perform., 23, 6, pp. 1917-1928, (2014)
[19]  
Garg A., Tai K., Savalani M.M., State-of-the-art in empirical modelling of rapid prototyping processes, Rapid Prototyping J., 20, pp. 164-178, (2014)
[20]  
Ghorbani J., Li J., Srivastava A.K., Application of optimized laser surface re-melting process on selective laser melted 316L stainless steel inclined parts, J. Manuf. Process., 56, pp. 726-734, (2020)