[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120912-en":3,"doc-seo-120912-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120912,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V","Quantitatively linking laser powder bed fusion (LPBF) process parameters to microstructures in additively manufactured Ti-6Al-4V remains a key challenge, since conventional optimization relies on time-consuming, experience-guided trial-and-error experiments. The study proposes an image-driven conditional GAN (cGAN) machine learning model to reconstruct and quantitatively predict key microstructural features, including martensite morphology and the size of primary and secondary martensite, across varying LPBF parameters such as laser power and scan speed, enabling efficient prediction beyond the training dataset.","A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V  \nAuthor:  \nCao , Z; Liu , Q; Liu , Q; Yu , X; Kruzic , JJ; Li , X  \nPublication details:  \nnpj Computational Materials v. 9  \nChapter No. 1 2057-3960 (ISSN)  \nPublication Date:  \n2023-12-01  \nPublisher DOI:  \n[https://doi.org/10.1038/s41524-023-01152-y](https://doi.org/10.1038/s41524-023-01152-y)  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/unsworks_84867](http://hdl.handle. net/1959.4/unsworks_84867) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2024-05-18  \n[www.nature.com/npjcompumats](www.nature.com/npjcompumats)  \nARTICLE OPEN   \nA machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V  \nZhuohan Cao 1, Qian Liu1, Qianchu Liu2, Xiaobo Yu2, Jamie J. Kruzic 1 and Xiaopeng Li1 ✉  \n\n|  | Quantitatively defining the relationship between laser powder bed fusion (LPBF) process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges. To date, achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience. Here, we develop an approach whereby an image-driven conditional generative adversarial network (cGAN) machine learning model is used to reconstruct and quantitatively predict the key microstructural features (e.g., the morphology of martensite and the size of primary and secondary martensite) for LPBF fabricated Ti-6Al-4V. The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters (i.e., laser power and laser scan speed) . This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model, which can be readily extended to other metal alloy systems, thus offering great potential in applications related to process optimisation, material |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| design, and microstructure control in the additive manufacturing field. |  |  |\n|  | npj Computational Materials (2023)9:195; [https://doi.org/10.1038/s41524-023-01](https://doi.org/10.1038/s41524-023-01)152-y |  |\n|  |  |  |\n\nINTRODUCTION  \nTi-6Al-4V is a widely used alloy in the aerospace, automotive, chemical, and biomedical industries 1,2 due to its excellent mechanical properties, high corrosion resistance, and biocompatibility. Laser powder bed fusion (LPBF) additive manufacturing isan emerging manufacturing technique wherein the product is fabricated by selectively melting successive layers of metal powders using a high-energy laser beam according to a computer-aided design (CAD) model3,4. LPBF is providing opportunities to design and manufacture complex near-netshape Ti-6Al-4V components with tailorable microstructures and desired properties, which has the potential to further expand applications in many key industries5–11.  \nA distinctive feature of LPBF is the thermal history with rapid solidification (cooling rates around 103 to 108 °C s−1) and cyclic reheating, which produces a Ti-6Al-4V microstructure different from that produced by conventional manufacturing methods 12. During the LPBF process, the Ti-6Al-4V alloy is usually transformed into a predominant metastable α’ martensite phase through a phase transformation13 because of the large thermal grad","cbCaivhQGKeUSBoC","https://ap.wps.com/l/cbCaivhQGKeUSBoC","pdf",21771819,1,16,"English","en",105,"# Introduction\n## Motivation: LPBF process–microstructure challenge\n## Distinct LPBF thermal history and martensite formation\n## Importance of martensite morphology for properties\n## Limitations of existing statistical and multiphysics approaches\n## Rationale for machine learning in materials science","[{\"question\":\"What microstructural features does the proposed cGAN model predict for LPBF Ti-6Al-4V?\",\"answer\":\"It reconstructs micrographs and predicts key features such as martensite morphology and the size of primary and secondary martensite.\"},{\"question\":\"Why is establishing a process–microstructure relationship in LPBF Ti-6Al-4V difficult?\",\"answer\":\"Desired microstructures and properties are typically achieved through costly, time-consuming trial-and-error experiments guided by human experience.\"},{\"question\":\"What LPBF parameters are considered when testing prediction beyond the training dataset?\",\"answer\":\"Predictions are evaluated under different LPBF parameters, particularly laser power and laser scan speed.\"}]","A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V | 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microstructural features does the proposed cGAN model predict for LPBF Ti-6Al-4V?","Question",{"text":75,"@type":76},"It reconstructs micrographs and predicts key features such as martensite morphology and the size of primary and secondary martensite.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is establishing a process–microstructure relationship in LPBF Ti-6Al-4V difficult?",{"text":80,"@type":76},"Desired microstructures and properties are typically achieved through costly, time-consuming trial-and-error experiments guided by human experience.",{"name":82,"@type":73,"acceptedAnswer":83},"What LPBF parameters are considered when testing prediction beyond the training dataset?",{"text":84,"@type":76},"Predictions are evaluated under different LPBF parameters, particularly laser power and laser scan 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