Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference

Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited la...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced manufacturing technology 2021-07, Vol.115 (5-6), p.1547-1564
Main Authors: Velázquez, Daniel René Tasé, Helleno, André Luís, Fals, Hipólito Carvajal, dos Santos, Raphael Galdino
Format: Article
Language:English
Subjects:
Adequacy
Aerospace industry
CAE) and Design
Computer-Aided Engineering (CAD
Design of experiments
Dilution
Engineering
Factorial analysis
Industrial and Production Engineering
Inference
Interaction parameters
Laser deposition
Lasers
Mechanical Engineering
Media Management
Optimization
Original Article
Process parameters
Stainless steels
Substrates
Variance analysis
Wetting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643
cites cdi_FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643
container_end_page 1564
container_issue 5-6
container_start_page 1547
container_title International journal of advanced manufacturing technology
container_volume 115
creator Velázquez, Daniel René Tasé
Helleno, André Luís
Fals, Hipólito Carvajal
dos Santos, Raphael Galdino
description Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and for optimizing the LMD process parameters. LMD was performed using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each, and the factor Pf was used with two operating levels. An analysis of variance allowed identifying that the Pf affects the Bh, Bh/Bw ratio, d and wa, as well as the increase in Lp showed an increasing of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (error up to 8.43%) for assessing the experimental conditions. The second FIS showed through the output defuzzified index (ODI) measured the best possible process parameters interaction, given the studied operating conditions and the output variables assessed.
doi_str_mv 10.1007/s00170-021-07269-y
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2548296264</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2548296264</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643</originalsourceid><addsrcrecordid>eNp9kEtPwzAQhC0EEqXwBzhZ4hzwI7GTY1XxqIQEEnC2HGdTXKVxsJ1De-d_YxoQN04rzX4zqx2ELim5poTIm0AIlSQjjGZEMlFluyM0oznnGSe0OEYzwkSZcSnKU3QWwibhgopyhj6fPTTWROt67Fq8BreF6K3RHTbv2msTwdsQrQlY9w0evDMQAh7SKoHgsRui3dq9_k3odEhqA4ML9qAtVi8rzKnAIQJ0eAy2X-N23O932PYteOgNnKOTVncBLn7mHL3d3b4uH7LHp_vVcvGYGS54zCSvCTUtVLKqKsHyRspSiLJmteakLkxrWM7ASAmFSFVQzqTMeVG02iRE5HyOrqbc9MfHCCGqjRt9n04qVuQlS6EHik2U8S4ED60avN1qv1OUqO-61VS3SjfUoW61SyY-mUKC-zX4v-h_XF_ARoUZ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2548296264</pqid></control><display><type>article</type><title>Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference</title><source>Springer Link</source><creator>Velázquez, Daniel René Tasé ; Helleno, André Luís ; Fals, Hipólito Carvajal ; dos Santos, Raphael Galdino</creator><creatorcontrib>Velázquez, Daniel René Tasé ; Helleno, André Luís ; Fals, Hipólito Carvajal ; dos Santos, Raphael Galdino</creatorcontrib><description>Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and for optimizing the LMD process parameters. LMD was performed using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each, and the factor Pf was used with two operating levels. An analysis of variance allowed identifying that the Pf affects the Bh, Bh/Bw ratio, d and wa, as well as the increase in Lp showed an increasing of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (error up to 8.43%) for assessing the experimental conditions. The second FIS showed through the output defuzzified index (ODI) measured the best possible process parameters interaction, given the studied operating conditions and the output variables assessed.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-021-07269-y</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Adequacy ; Aerospace industry ; CAE) and Design ; Computer-Aided Engineering (CAD ; Design of experiments ; Dilution ; Engineering ; Factorial analysis ; Industrial and Production Engineering ; Inference ; Interaction parameters ; Laser deposition ; Lasers ; Mechanical Engineering ; Media Management ; Optimization ; Original Article ; Process parameters ; Stainless steels ; Substrates ; Variance analysis ; Wetting</subject><ispartof>International journal of advanced manufacturing technology, 2021-07, Vol.115 (5-6), p.1547-1564</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643</citedby><cites>FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>Velázquez, Daniel René Tasé</creatorcontrib><creatorcontrib>Helleno, André Luís</creatorcontrib><creatorcontrib>Fals, Hipólito Carvajal</creatorcontrib><creatorcontrib>dos Santos, Raphael Galdino</creatorcontrib><title>Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and for optimizing the LMD process parameters. LMD was performed using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each, and the factor Pf was used with two operating levels. An analysis of variance allowed identifying that the Pf affects the Bh, Bh/Bw ratio, d and wa, as well as the increase in Lp showed an increasing of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (error up to 8.43%) for assessing the experimental conditions. The second FIS showed through the output defuzzified index (ODI) measured the best possible process parameters interaction, given the studied operating conditions and the output variables assessed.</description><subject>Adequacy</subject><subject>Aerospace industry</subject><subject>CAE) and Design</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Design of experiments</subject><subject>Dilution</subject><subject>Engineering</subject><subject>Factorial analysis</subject><subject>Industrial and Production Engineering</subject><subject>Inference</subject><subject>Interaction parameters</subject><subject>Laser deposition</subject><subject>Lasers</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Process parameters</subject><subject>Stainless steels</subject><subject>Substrates</subject><subject>Variance analysis</subject><subject>Wetting</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEqXwBzhZ4hzwI7GTY1XxqIQEEnC2HGdTXKVxsJ1De-d_YxoQN04rzX4zqx2ELim5poTIm0AIlSQjjGZEMlFluyM0oznnGSe0OEYzwkSZcSnKU3QWwibhgopyhj6fPTTWROt67Fq8BreF6K3RHTbv2msTwdsQrQlY9w0evDMQAh7SKoHgsRui3dq9_k3odEhqA4ML9qAtVi8rzKnAIQJ0eAy2X-N23O932PYteOgNnKOTVncBLn7mHL3d3b4uH7LHp_vVcvGYGS54zCSvCTUtVLKqKsHyRspSiLJmteakLkxrWM7ASAmFSFVQzqTMeVG02iRE5HyOrqbc9MfHCCGqjRt9n04qVuQlS6EHik2U8S4ED60avN1qv1OUqO-61VS3SjfUoW61SyY-mUKC-zX4v-h_XF_ARoUZ</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Velázquez, Daniel René Tasé</creator><creator>Helleno, André Luís</creator><creator>Fals, Hipólito Carvajal</creator><creator>dos Santos, Raphael Galdino</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20210701</creationdate><title>Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference</title><author>Velázquez, Daniel René Tasé ; Helleno, André Luís ; Fals, Hipólito Carvajal ; dos Santos, Raphael Galdino</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adequacy</topic><topic>Aerospace industry</topic><topic>CAE) and Design</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Design of experiments</topic><topic>Dilution</topic><topic>Engineering</topic><topic>Factorial analysis</topic><topic>Industrial and Production Engineering</topic><topic>Inference</topic><topic>Interaction parameters</topic><topic>Laser deposition</topic><topic>Lasers</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Process parameters</topic><topic>Stainless steels</topic><topic>Substrates</topic><topic>Variance analysis</topic><topic>Wetting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Velázquez, Daniel René Tasé</creatorcontrib><creatorcontrib>Helleno, André Luís</creatorcontrib><creatorcontrib>Fals, Hipólito Carvajal</creatorcontrib><creatorcontrib>dos Santos, Raphael Galdino</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Velázquez, Daniel René Tasé</au><au>Helleno, André Luís</au><au>Fals, Hipólito Carvajal</au><au>dos Santos, Raphael Galdino</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2021-07-01</date><risdate>2021</risdate><volume>115</volume><issue>5-6</issue><spage>1547</spage><epage>1564</epage><pages>1547-1564</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Laser metal deposition (LMD) process is an additive manufacturing technique that has attracted the interest of the automotive and aerospace industries due to its ability to manufacture parts with complex geometries and different types of metallic materials. However, the structure of the deposited layers and the geometrical characteristics of the manufactured parts are influenced by the interaction among the deposition process parameters. In this paper, fuzzy inference (FIS) technique was used to develop two models for predicting the geometrical characteristics and for optimizing the LMD process parameters. LMD was performed using AISI 316 stainless steel powder and substrate. An experimental design, based on factorial analysis, was used to correlate the influence of selected deposition process parameters, laser power (Lp), powder flow (Pf) and focal length (Fl) with the process geometrical characteristics bead height (Bh), bead width (Bw), depth of penetration (Dp), dilution (d) and wetting angle (wa). The factors Lp and Fl were used with three operating levels each, and the factor Pf was used with two operating levels. An analysis of variance allowed identifying that the Pf affects the Bh, Bh/Bw ratio, d and wa, as well as the increase in Lp showed an increasing of the geometric characteristics Bw and Dp. The first FIS, for predicting the bead’s geometrical characteristics, presented high adequacy (error up to 8.43%) for assessing the experimental conditions. The second FIS showed through the output defuzzified index (ODI) measured the best possible process parameters interaction, given the studied operating conditions and the output variables assessed.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-021-07269-y</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0268-3768
ispartof International journal of advanced manufacturing technology, 2021-07, Vol.115 (5-6), p.1547-1564
issn 0268-3768
1433-3015
language eng
recordid cdi_proquest_journals_2548296264
source Springer Link
subjects Adequacy
Aerospace industry
CAE) and Design
Computer-Aided Engineering (CAD
Design of experiments
Dilution
Engineering
Factorial analysis
Industrial and Production Engineering
Inference
Interaction parameters
Laser deposition
Lasers
Mechanical Engineering
Media Management
Optimization
Original Article
Process parameters
Stainless steels
Substrates
Variance analysis
Wetting
title Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T10%3A31%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20geometrical%20characteristics%20and%20process%20parameter%20optimization%20of%20laser%20deposition%20AISI%20316%20steel%20using%20fuzzy%20inference&rft.jtitle=International%20journal%20of%20advanced%20manufacturing%20technology&rft.au=Vel%C3%A1zquez,%20Daniel%20Ren%C3%A9%20Tas%C3%A9&rft.date=2021-07-01&rft.volume=115&rft.issue=5-6&rft.spage=1547&rft.epage=1564&rft.pages=1547-1564&rft.issn=0268-3768&rft.eissn=1433-3015&rft_id=info:doi/10.1007/s00170-021-07269-y&rft_dat=%3Cproquest_cross%3E2548296264%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c363t-73b01cfe97999624d778668b2ba30b5cfc242ec77e56021132774355fac2ba643%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2548296264&rft_id=info:pmid/&rfr_iscdi=true