{"678335":{"#nid":"678335","#data":{"type":"event","title":"PhD Proposal by Ayush Jain","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAyush Jain\u003C\/strong\u003E\u003Cbr\u003EAdvisor: Prof. Ramprasad\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cem\u003Ewill propose a doctoral thesis entitled\u003C\/em\u003E,\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EMultiscale Machine Intelligence Tools to Accelerate Polymer Additive Manufacturing Design\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cem\u003EOn\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EWednesday, November 13th at\u0026nbsp; 1 p.m.\u003Cbr\u003EMRDC Room 4404\u003C\/p\u003E\u003Cp\u003Eand\/or Virtual via Teams\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_ODUxNjA5M2MtYTg5Yy00ZmViLThmZDgtMjliNDk0NDkzYzNi%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%229d323b6a-6017-4a5e-b47c-b276df7b5e4e%22%7d\u0022 title=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_ODUxNjA5M2MtYTg5Yy00ZmViLThmZDgtMjliNDk0NDkzYzNi%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%229d323b6a-6017-4a5e-b47c-b276df7b5e4e%22%7d\u0022\u003Ehttps:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_ODUxNjA5M2MtYTg5Yy00ZmViLThmZDgtMjliNDk0NDkzYzNi%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%229d323b6a-6017-4a5e-b47c-b276df7b5e4e%22%7d\u003C\/a\u003E\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EProf. Rampi Ramprasad (Advisor) \u2013 School of Materials Science and Engineering, School of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003EProf. H Jerry Qi \u2013 School of Mechanical Engineering\u003C\/p\u003E\u003Cp\u003EProf. Aaron Stebner \u2013 School of Materials Science and Engineering, School of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003EProf. Victor Fung \u2013 School of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003EDr. Ehsan Haghighat \u2013 Head of Machine Learning, C-infinity; Software Research Scientist, Carbon3D\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr\u003EThe modern manufacturing landscape is transforming, driven by the need for mass customization and sustainability in producing complex, multi-material structures. Traditional manufacturing methods struggle to meet these demands, especially for intricate designs. Additive Manufacturing (AM), particularly polymeric 3D printing, offers a promising solution by building products layer by layer, allowing for reduced material waste and greater design freedom. However, the vast design space in AM\u2014including ranges of material chemistries, processing conditions, and component design\u2014poses a significant optimization challenge.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EI propose that we can use computational tools that leverage machine learning (ML) and materials informatics to accelerate the AM design process in a hierarchical manner, across the many length scales of design. In the first stage, active learning algorithms, integrated with molecular dynamics simulations, and graph neural network diffusion model surrogates, can help explore the vast combinatorial space of thermoset photopolymer acrylates. This framework will be deployed as an autonomous decision-making system for the experimental lab. The next stage is using domain-informed ML algorithms to model polymeric materials, namely the melt viscosity, across chemical and processing domains. I demonstrate that domain information in machine intelligence is crucial to model unseen spaces. Finally, the optimization of lattice structures in component design is addressed by introducing an ML surrogate that predicts mechanical responses in AM components based on finite-element simulations.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ECollectively, these tools lay a foundation for multi-scale informatics-driven AM design, enabling faster and more informed decision-making. This increases AM\u0027s adaptability and scalability, paving the way for innovative, customized products in a sustainable manufacturing ecosystem.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EMultiscale Machine Intelligence Tools to Accelerate Polymer Additive Manufacturing Design\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Multiscale Machine Intelligence Tools to Accelerate Polymer Additive Manufacturing Design"}],"uid":"27707","created_gmt":"2024-11-11 20:56:03","changed_gmt":"2024-11-11 20:56:30","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-13T13:00:00-05:00","event_time_end":"2024-11-13T15:00:00-05:00","event_time_end_last":"2024-11-13T15:00:00-05:00","gmt_time_start":"2024-11-13 18:00:00","gmt_time_end":"2024-11-13 20:00:00","gmt_time_end_last":"2024-11-13 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"MRDC Room 4404 and\/or Virtual via Teams","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}