Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties
[Display omitted] •Machine learning helps establish LPBF processing window for TiCN/AlSi10Mg composite.•Within the new window, the properties of the fabricated composites are tailorable.•Increased laser power/scan speed resulted in the coarsening of Si network.•Lower yield strength and higher elonga...
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| Veröffentlicht in: | Materials letters 2022-01, Vol.307, p.131018, Article 131018 |
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| container_title | Materials letters |
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| creator | He, Peidong Liu, Qian Kruzic, Jamie J. Li, Xiaopeng |
| description | [Display omitted]
•Machine learning helps establish LPBF processing window for TiCN/AlSi10Mg composite.•Within the new window, the properties of the fabricated composites are tailorable.•Increased laser power/scan speed resulted in the coarsening of Si network.•Lower yield strength and higher elongation were caused by coarsening of Si network.
A Gaussian process regression-based machine learning approach was used to establish a processing window optimized for high density additive manufacturing of a 2 vol% TiCN reinforced AlSi10Mg composite by laser powder bed fusion. The optimized window for TiCN reinforced AlSi10Mg was found to be smaller than for AlSi10Mg. Within the optimized window, it was found that the Si eutectic cell size can be increased by raising the laser power/scanning speed at the constant energy density of 50 J/mm3 to control the tensile properties of the fabricated composites. |
| doi_str_mv | 10.1016/j.matlet.2021.131018 |
| format | Article |
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•Machine learning helps establish LPBF processing window for TiCN/AlSi10Mg composite.•Within the new window, the properties of the fabricated composites are tailorable.•Increased laser power/scan speed resulted in the coarsening of Si network.•Lower yield strength and higher elongation were caused by coarsening of Si network.
A Gaussian process regression-based machine learning approach was used to establish a processing window optimized for high density additive manufacturing of a 2 vol% TiCN reinforced AlSi10Mg composite by laser powder bed fusion. The optimized window for TiCN reinforced AlSi10Mg was found to be smaller than for AlSi10Mg. Within the optimized window, it was found that the Si eutectic cell size can be increased by raising the laser power/scanning speed at the constant energy density of 50 J/mm3 to control the tensile properties of the fabricated composites.</description><identifier>ISSN: 0167-577X</identifier><identifier>EISSN: 1873-4979</identifier><identifier>DOI: 10.1016/j.matlet.2021.131018</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Additive manufacturing ; Aluminum base alloys ; Aluminum metal matrix composites ; Flux density ; Gaussian process ; Laser powder bed fusion ; Machine learning ; Manufacturing ; Materials science ; Mechanical properties ; Powder beds ; Tensile properties ; Titanium carbonitride</subject><ispartof>Materials letters, 2022-01, Vol.307, p.131018, Article 131018</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier BV Jan 15, 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c334t-54d451880f8b4bba4f56f293dd1d3aa106780283c3d35b23ba0cc63e3940388c3</citedby><cites>FETCH-LOGICAL-c334t-54d451880f8b4bba4f56f293dd1d3aa106780283c3d35b23ba0cc63e3940388c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0167577X2101716X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>He, Peidong</creatorcontrib><creatorcontrib>Liu, Qian</creatorcontrib><creatorcontrib>Kruzic, Jamie J.</creatorcontrib><creatorcontrib>Li, Xiaopeng</creatorcontrib><title>Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties</title><title>Materials letters</title><description>[Display omitted]
•Machine learning helps establish LPBF processing window for TiCN/AlSi10Mg composite.•Within the new window, the properties of the fabricated composites are tailorable.•Increased laser power/scan speed resulted in the coarsening of Si network.•Lower yield strength and higher elongation were caused by coarsening of Si network.
A Gaussian process regression-based machine learning approach was used to establish a processing window optimized for high density additive manufacturing of a 2 vol% TiCN reinforced AlSi10Mg composite by laser powder bed fusion. The optimized window for TiCN reinforced AlSi10Mg was found to be smaller than for AlSi10Mg. Within the optimized window, it was found that the Si eutectic cell size can be increased by raising the laser power/scanning speed at the constant energy density of 50 J/mm3 to control the tensile properties of the fabricated composites.</description><subject>Additive manufacturing</subject><subject>Aluminum base alloys</subject><subject>Aluminum metal matrix composites</subject><subject>Flux density</subject><subject>Gaussian process</subject><subject>Laser powder bed fusion</subject><subject>Machine learning</subject><subject>Manufacturing</subject><subject>Materials science</subject><subject>Mechanical properties</subject><subject>Powder beds</subject><subject>Tensile properties</subject><subject>Titanium carbonitride</subject><issn>0167-577X</issn><issn>1873-4979</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1r3DAQhkVpodtt_0EPgpy9lSzZli-BsKRJIR-HptCbGEvj7CxeayNpU3rpb68W99zTwMz7wTyMfZZiI4Vsv-w3B8gT5k0tarmRqizNG7aSplOV7rv-LVsVWVc1XffzPfuQ0l4IoXuhV-zPPbgdzVhNCHGm-ZlDSpQyeg7eU6ZX5AeYTyO4fIrnexg58CfaPvCINI8huqK9mr6TFPfP3IXDMSTKyH9R3vEMNIUIw1RS0O1gJgcTP8ZwxJgJ00f2boQp4ad_c81-fL1-2t5Wd48337ZXd5VTSueq0V430hgxmkEPA-ixace6V95LrwCkaDsjaqOc8qoZajWAcK5VqHotlDFOrdnFkluqX06Yst2HU5xLpa1b2bddrWRdVHpRuRhSijjaY6QDxN9WCnsmbfd2IW3PpO1CutguFxuWD14Jo02OcC5cKKLL1gf6f8BfQk-KZg</recordid><startdate>20220115</startdate><enddate>20220115</enddate><creator>He, Peidong</creator><creator>Liu, Qian</creator><creator>Kruzic, Jamie J.</creator><creator>Li, Xiaopeng</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20220115</creationdate><title>Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties</title><author>He, Peidong ; Liu, Qian ; Kruzic, Jamie J. ; Li, Xiaopeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-54d451880f8b4bba4f56f293dd1d3aa106780283c3d35b23ba0cc63e3940388c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Additive manufacturing</topic><topic>Aluminum base alloys</topic><topic>Aluminum metal matrix composites</topic><topic>Flux density</topic><topic>Gaussian process</topic><topic>Laser powder bed fusion</topic><topic>Machine learning</topic><topic>Manufacturing</topic><topic>Materials science</topic><topic>Mechanical properties</topic><topic>Powder beds</topic><topic>Tensile properties</topic><topic>Titanium carbonitride</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Peidong</creatorcontrib><creatorcontrib>Liu, Qian</creatorcontrib><creatorcontrib>Kruzic, Jamie J.</creatorcontrib><creatorcontrib>Li, Xiaopeng</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Materials letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>He, Peidong</au><au>Liu, Qian</au><au>Kruzic, Jamie J.</au><au>Li, Xiaopeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties</atitle><jtitle>Materials letters</jtitle><date>2022-01-15</date><risdate>2022</risdate><volume>307</volume><spage>131018</spage><pages>131018-</pages><artnum>131018</artnum><issn>0167-577X</issn><eissn>1873-4979</eissn><abstract>[Display omitted]
•Machine learning helps establish LPBF processing window for TiCN/AlSi10Mg composite.•Within the new window, the properties of the fabricated composites are tailorable.•Increased laser power/scan speed resulted in the coarsening of Si network.•Lower yield strength and higher elongation were caused by coarsening of Si network.
A Gaussian process regression-based machine learning approach was used to establish a processing window optimized for high density additive manufacturing of a 2 vol% TiCN reinforced AlSi10Mg composite by laser powder bed fusion. The optimized window for TiCN reinforced AlSi10Mg was found to be smaller than for AlSi10Mg. Within the optimized window, it was found that the Si eutectic cell size can be increased by raising the laser power/scanning speed at the constant energy density of 50 J/mm3 to control the tensile properties of the fabricated composites.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.matlet.2021.131018</doi></addata></record> |
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| ispartof | Materials letters, 2022-01, Vol.307, p.131018, Article 131018 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Additive manufacturing Aluminum base alloys Aluminum metal matrix composites Flux density Gaussian process Laser powder bed fusion Machine learning Manufacturing Materials science Mechanical properties Powder beds Tensile properties Titanium carbonitride |
| title | Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties |
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