Quality Prediction of Fused Deposition Molding Parts Based on Improved Deep Belief Network
Quality Prediction of Fused Deposition Molding Parts Based on Improved Deep Belief Network
About this item
Full title
Author / Creator
Dong, Hai , Gao, Xiuxiu and Wei, Mingqi
Publisher
United States: Hindawi
Journal title
Language
English
Formats
Publication information
Publisher
United States: Hindawi
Subjects
More information
Scope and Contents
Contents
Tensile strength, warping degree, and surface roughness are important indicators to evaluate the quality of fused deposition modeling (FDM) parts, and their accurate and stable prediction is helpful to the development of FDM technology. Thus, a quality prediction method of FDM parts based on an optimized deep belief network was proposed. To determi...
Alternative Titles
Full title
Quality Prediction of Fused Deposition Molding Parts Based on Improved Deep Belief Network
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8670973
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8670973
Other Identifiers
ISSN
1687-5265,1687-5273
E-ISSN
1687-5273
DOI
10.1155/2021/8100371
