Surface roughness R prediction in Selective Laser Melting of 316L stainless steel by means of artificial intelligence inference-Reference-Cited by-同舟云学术

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

Published:2021-03 Issue: Volume: Page:
ISSN:1018-3639
Container-title:Journal of King Saud University - Engineering Sciences
language:en
Short-container-title:Journal of King Saud University - Engineering Sciences

Author:

La Fé-Perdomo Iván,Ramos-Grez Jorge,Mujica Rafael,Rivas Marcelino

Publisher

Elsevier BV

Subject

General Engineering

Reference44 articles.

1. Analysis and optimisation of vertical surface roughness in micro selective laser melting;Abele;Surf. Topogr.: Metrol. Prop.,2015

2. Reducing porosity in AlSi10Mg parts processed by selective laser melting;Aboulkhair;Addit. Manuf.,2014

3. Direct Digital Manufacturing;Al-Ahmari;J. King Saud Univ. – Eng. Sci..,2017

4. On optimization of surface roughness of selective laser melted stainless steel parts: a statistical study;Alrbaey;J. Mater. Eng. Perform.,2014

5. Effects of process parameters on the surface roughness of stainless steel 316L parts produced by selective laser melting;Aqilah;J. Test. Eval.,2018

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2. Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and Machine Learning Approach for Pore Recognition on Pipes Surfaces;Sensors;2024-07-31

3. Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression;The International Journal of Advanced Manufacturing Technology;2024-07-05

4. Steel surface roughness parameter prediction from laser reflection data using machine learning models;The International Journal of Advanced Manufacturing Technology;2024-04-24

5. Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning;Computers in Industry;2024-04

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