KLI

Machine learning-based optimization of process parameters in selective laser melting for biomedical applications

Metadata Downloads
Abstract
Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications,
owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti?6Al?4V
SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization
methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser
scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique
(density ratio and surface roughness). A supervised learning deep neural network based on the backpropagation algorithm
was applied to optimize input parameters for a given set of quality part outputs. Several methods were utilized to solve
undesired problems occurring during neural network training to increase the model accuracy. The model’s performance was
proven with a value of R2
of 99% for both density ratio and surface roughness. A selection system was then built, allowing
users to choose the optimal process parameters for fabricated products whose properties meet a specific user requirement.
Experiments performed with the optimal process parameters recommended by the optimization system strongly confirmed
its reliability by providing the ultimate part qualities nearly identical to those defined by the user with only about 0.9?4.4%
of errors at the maximum. Finally, a graphical user interface was developed to facilitate the choice of the optimum process
parameters for the desired density ratio and surface roughness.
Author(s)
박홍석Dinh Son NguyenThai Le?HongXuan Van Tran
Issued Date
2021
Type
Article
Keyword
Selective laser meltingTitanium-based alloysProcess parameter optimizationArtificial neural networkAdditive manufacturingMachine learning
DOI
10.1007/s10845-021-01773-4
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8806
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9770366&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,%EC%9D%B4%EB%8F%99%EC%8B%9D%EC%82%AC%EB%8B%A4%EB%A6%AC%20%EC%A4%91%EB%8C%80%EC%9E%AC%ED%95%B4%20%ED%86%B5%EA%B3%84%20%EB%B6%84%EC%84%9D%20%EB%B0%8F%20%EC%9D%B4%EB%8F%99%EC%8B%9D%EC%82%AC%EB%8B%A4%EB%A6%AC%EC%99%80%20%EC%95%88%EC%A0%84%EB%AA%A8%20%EC%8B%A4%EC%8B%9C%EA%B0%84%20%ED%83%90%EC%A7%80%20%EA%B8%B0%EA%B3%84%ED%95%99%EC%8A%B5%20%EB%AA%A8%EB%8D%B8%20%EA%B0%9C%EB%B0%9C&offset=0&pcAvailability=true
Publisher
JOURNAL OF INTELLIGENT MANUFACTURING
Location
네덜란드
Language
영어
ISSN
0956-5515
Citation Volume
32
Citation Number
4
Citation Start Page
142
Citation End Page
142
Appears in Collections:
Engineering > Aerospace Engineering
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.