Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods
NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introduci...
Gespeichert in:
Veröffentlicht in: | Virtual and physical prototyping 2024-12, Vol.19 (1) |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 1 |
container_start_page | |
container_title | Virtual and physical prototyping |
container_volume | 19 |
creator | Li, Zhicheng Zhong, Jing Jiang, Xingsong Wang, Zhangxi Zhang, Lijun |
description | NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783
$\pm$
±
8 MPa, an elongation of 13.7
$\pm$
±
0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes. |
doi_str_mv | 10.1080/17452759.2024.2364221 |
format | Article |
fullrecord | <record><control><sourceid>doaj_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_17452759_2024_2364221</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_153a26384adc4e678d4b0df7acd6bd75</doaj_id><sourcerecordid>oai_doaj_org_article_153a26384adc4e678d4b0df7acd6bd75</sourcerecordid><originalsourceid>FETCH-LOGICAL-c371t-4aaccdda9fdb55c8e23c94fa58968fb5ca04ca673d6ec60df032ae35640c99113</originalsourceid><addsrcrecordid>eNp9kd1u1DAQhSMEEqXwCEh-gV0c_yW5o1QUKlXARbm2JuPJxisnjmyXal-HJyVhSy-5mtGMzjeac6rqfc33NW_5h7pRWjS62wsu1F5Io4SoX1QX23wnGtO8fO5197p6k_ORcyW5rC-q31eIFChB8fOBhR-fbtiSIlLOLC7FTz6vmzizISb2zd97lkdYiE00xXRiEEI8Zfboy8hoHmFGcgxmxzDOJcUQoA-0ARdKxVNmZUzx4TCyCXD0M7FAkObt8iaaHkLxu9gfCYv_tR0pY3T5bfVqgJDp3VO9rH7efL6__rq7-_7l9vrqboeyqctOASA6B93geq2xJSGxUwPotjPt0GsErhBMI50hNNwNXAogqY3i2HV1LS-r2zPXRTjaJfkJ0slG8PbvIKaDhfULDGRrLUEY2SpwqMg0rVP9SmwAneldo1eWPrMwxZwTDc-8mtstNPsvNLuFZp9CW3Ufzzo_r45P8BhTcLbAKcQ0pNVfn638P-IPMOajzQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods</title><source>Taylor & Francis Open Access</source><source>DOAJ Directory of Open Access Journals</source><creator>Li, Zhicheng ; Zhong, Jing ; Jiang, Xingsong ; Wang, Zhangxi ; Zhang, Lijun</creator><creatorcontrib>Li, Zhicheng ; Zhong, Jing ; Jiang, Xingsong ; Wang, Zhangxi ; Zhang, Lijun</creatorcontrib><description>NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783
$\pm$
±
8 MPa, an elongation of 13.7
$\pm$
±
0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes.</description><identifier>ISSN: 1745-2759</identifier><identifier>EISSN: 1745-2767</identifier><identifier>DOI: 10.1080/17452759.2024.2364221</identifier><language>eng</language><publisher>Taylor & Francis</publisher><subject>Laser powder bed fusion ; machine learning ; multi-objective optimisation ; NiTi shape memory alloys ; process</subject><ispartof>Virtual and physical prototyping, 2024-12, Vol.19 (1)</ispartof><rights>2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c371t-4aaccdda9fdb55c8e23c94fa58968fb5ca04ca673d6ec60df032ae35640c99113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/17452759.2024.2364221$$EPDF$$P50$$Ginformaworld$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/17452759.2024.2364221$$EHTML$$P50$$Ginformaworld$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2096,27479,27901,27902,59116,59117</link.rule.ids></links><search><creatorcontrib>Li, Zhicheng</creatorcontrib><creatorcontrib>Zhong, Jing</creatorcontrib><creatorcontrib>Jiang, Xingsong</creatorcontrib><creatorcontrib>Wang, Zhangxi</creatorcontrib><creatorcontrib>Zhang, Lijun</creatorcontrib><title>Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods</title><title>Virtual and physical prototyping</title><description>NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783
$\pm$
±
8 MPa, an elongation of 13.7
$\pm$
±
0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes.</description><subject>Laser powder bed fusion</subject><subject>machine learning</subject><subject>multi-objective optimisation</subject><subject>NiTi shape memory alloys</subject><subject>process</subject><issn>1745-2759</issn><issn>1745-2767</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>DOA</sourceid><recordid>eNp9kd1u1DAQhSMEEqXwCEh-gV0c_yW5o1QUKlXARbm2JuPJxisnjmyXal-HJyVhSy-5mtGMzjeac6rqfc33NW_5h7pRWjS62wsu1F5Io4SoX1QX23wnGtO8fO5197p6k_ORcyW5rC-q31eIFChB8fOBhR-fbtiSIlLOLC7FTz6vmzizISb2zd97lkdYiE00xXRiEEI8Zfboy8hoHmFGcgxmxzDOJcUQoA-0ARdKxVNmZUzx4TCyCXD0M7FAkObt8iaaHkLxu9gfCYv_tR0pY3T5bfVqgJDp3VO9rH7efL6__rq7-_7l9vrqboeyqctOASA6B93geq2xJSGxUwPotjPt0GsErhBMI50hNNwNXAogqY3i2HV1LS-r2zPXRTjaJfkJ0slG8PbvIKaDhfULDGRrLUEY2SpwqMg0rVP9SmwAneldo1eWPrMwxZwTDc-8mtstNPsvNLuFZp9CW3Ufzzo_r45P8BhTcLbAKcQ0pNVfn638P-IPMOajzQ</recordid><startdate>20241231</startdate><enddate>20241231</enddate><creator>Li, Zhicheng</creator><creator>Zhong, Jing</creator><creator>Jiang, Xingsong</creator><creator>Wang, Zhangxi</creator><creator>Zhang, Lijun</creator><general>Taylor & Francis</general><general>Taylor & Francis Group</general><scope>0YH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>20241231</creationdate><title>Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods</title><author>Li, Zhicheng ; Zhong, Jing ; Jiang, Xingsong ; Wang, Zhangxi ; Zhang, Lijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-4aaccdda9fdb55c8e23c94fa58968fb5ca04ca673d6ec60df032ae35640c99113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Laser powder bed fusion</topic><topic>machine learning</topic><topic>multi-objective optimisation</topic><topic>NiTi shape memory alloys</topic><topic>process</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhicheng</creatorcontrib><creatorcontrib>Zhong, Jing</creatorcontrib><creatorcontrib>Jiang, Xingsong</creatorcontrib><creatorcontrib>Wang, Zhangxi</creatorcontrib><creatorcontrib>Zhang, Lijun</creatorcontrib><collection>Taylor & Francis Open Access</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Virtual and physical prototyping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhicheng</au><au>Zhong, Jing</au><au>Jiang, Xingsong</au><au>Wang, Zhangxi</au><au>Zhang, Lijun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods</atitle><jtitle>Virtual and physical prototyping</jtitle><date>2024-12-31</date><risdate>2024</risdate><volume>19</volume><issue>1</issue><issn>1745-2759</issn><eissn>1745-2767</eissn><abstract>NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783
$\pm$
±
8 MPa, an elongation of 13.7
$\pm$
±
0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes.</abstract><pub>Taylor & Francis</pub><doi>10.1080/17452759.2024.2364221</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1745-2759 |
ispartof | Virtual and physical prototyping, 2024-12, Vol.19 (1) |
issn | 1745-2759 1745-2767 |
language | eng |
recordid | cdi_crossref_primary_10_1080_17452759_2024_2364221 |
source | Taylor & Francis Open Access; DOAJ Directory of Open Access Journals |
subjects | Laser powder bed fusion machine learning multi-objective optimisation NiTi shape memory alloys process |
title | Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T10%3A18%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Accelerating%20lPBF%20process%20optimisation%20for%20NiTi%20shape%20memory%20alloys%20with%20enhanced%20and%20controllable%20properties%20through%20machine%20learning%20and%20multi-objective%20methods&rft.jtitle=Virtual%20and%20physical%20prototyping&rft.au=Li,%20Zhicheng&rft.date=2024-12-31&rft.volume=19&rft.issue=1&rft.issn=1745-2759&rft.eissn=1745-2767&rft_id=info:doi/10.1080/17452759.2024.2364221&rft_dat=%3Cdoaj_cross%3Eoai_doaj_org_article_153a26384adc4e678d4b0df7acd6bd75%3C/doaj_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_doaj_id=oai_doaj_org_article_153a26384adc4e678d4b0df7acd6bd75&rfr_iscdi=true |