[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128028-en":3,"doc-seo-128028-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128028,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Data-Driven Non-Intrusive Surrogate Modeling and Optimization of Selective Laser Melting Using Machine Learning Methods","The thesis develops data-driven non-intrusive surrogate modeling for accelerating and optimizing selective laser melting (SLM), a key process in laser-based additive manufacturing. Traditional experimentation and simulation workflows are costly and time-intensive, motivating the use of machine learning methods to predict outcomes more efficiently. A numerical SLM model is built in ANSYS and assessed through comparisons with multiple experimental cases. Parameter calibration, sensitivity and uncertainty propagation are supported by a fast surrogate model, while finite-element modeling in ANSYS Workbench Additive enables public-data validation.","Data-Driven Non-Intrusive Surrogate Modeling and Optimization of Selective Laser Melting Using Machine  \nLearning Methods  \nby  \nSHUBHAM CHAUDHRY  \nMANUSCRIPT-BASED THESIS PRESENTED TO ÉCOLE DE TECHNOLOGIE SUPÉRIEURE IN PARTIAL FULFILLMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY  \nPh.D.  \nMONTREAL, AUGUST 2, 2023  \nÉCOLE DE TECHNOLOGIE SUPÉRIEURE UNIVERSITÉ DU QUÉBEC  \n Shubham Chaudhry, 2023  \nThis Creative Commons license allows readers to download this work and share it with others as long as the author is credited. The content of this work cannot be modified in any way or used commercially.  \nBOARD OF EXAMINERS  \nTHIS THESIS HAS BEEN EVALUATED  \nBY THE FOLLOWING BOARD OF EXAMINERS  \nMr. Azzeddine Soulaïmani, Thesis supervisor  \nDepartment of Mechanical Engineering, École de technologie supérieure  \nMr. Simon Joncas , Chair, Board of Examiners  \nDepartment of Systems Engineering, École de technologie superieure  \nMr. Hakim Bouzid, Member of the Jury  \nDepartment of Mechanical Engineering, École de technologie superieure  \nMr. Mohammad Jahazi, Member of the Jury  \nDepartment of Mechanical Engineering, École de technologie superieure  \nMr. Francisco Chinesta, External Examiner  \nDepartment of Mechanical Engineering, ENSAM, Paris  \nTHIS THESIS WAS PRESENTED AND DEFENDED  \nIN THE PRESENCE OF A BOARD OF EXAMINERS AND THE PUBLIC  \nON \"JULY 7, 2023\"  \nAT ÉCOLE DE TECHNOLOGIE SUPÉRIEURE  \nACKNOWLEDGEMENTS  \nI would wish to convey my earnest gratitude to my supervisor, Prof. Azzeddine Soulaïmani, whose expertise, counsel, and patience added considerably to my research experience. I appreciate all his efforts, especially in transferring his knowledge to me, adjusting my writing and supporting me financially during my Ph.D. I am thankful to him for believing in my accomplishments and providing me with every opportunity to assimilate. His encouragement helped me throughout my research study. I would also like to thank my colleagues Gullnaz Shahzadi, and Azzedine Abdedou for their constant support and motivation during my Ph.D. journey. Also, special thanks to my jury members Prof. Mohammad Jahazi, Prof. Hakim Bouzid, Prof. Francisco Chinesta, and Prof. Simon Joncas for evaluating my Ph.D. thesis, valuable feedback and suggestions.  \nLast but not least, I want to thank my parents, Mr. Siya nand, Ms. Urmila and my siblings Navneet Kumar, Dr. Shikha Choudhary, for their outstanding support, kindness, love and constant motivation throughout this journey. Their belief in me has been an endless source of motivation, and I owe much of my success to their presence in my life. I am really lucky to have you all.  \nTo my parents, who have been my pillars of strength from the very beginning, I cannot thank you enough for your constant encouragement, sacrifices, and belief in my abilities. Your guidance and unwavering faith in me have been instrumental in shaping me into the person I am today. Iam blessed to have you as my parents.  \nTo my dear sister and brother, you have been my closest confidants and best friends. Your support, understanding, and encouragement have meant the world to me. Your willingness to lend a helping hand and provide much-needed breaks during stressful times have made this journey more manageable and enjoyable.  \nTo myjiju (DSP. Prince Singhal) and bahabi (Dr. Damini), your presence in my life has been a wonderful addition to our family. Thank you for embracing me as your own and always being  \nVI  \nthere with your kind words and support. Your belief in my abilities has boosted my confidence, and I am grateful for the warmth and love you bring into our family.  \nI would also like to acknowledge the extended family members and friends who have been a source of inspiration and motivation during this journey. Your constant encouragement and well wishes have kept me going, and I am thankful for your presence in my life.  \nTo everyone who has played a role in my journey, whether big or small, thank you from the bottom of my heart. Your ","cbCainbwUvi9m2i8","https://ap.wps.com/l/cbCainbwUvi9m2i8","pdf",8994845,2,1,202,"English","en",105,"# Abstract\n## Thesis objective and motivation\n## Modeling approach and tools (ANSYS)\n## Calibration and validation with experimental cases\n## Fast surrogate modeling, sensitivity, and uncertainty analysis\n## Finite-element modeling and public-data application","[{\"question\":\"What problem does the thesis address in selective laser melting?\",\"answer\":\"It targets the high cost and long time of trial-and-error experiments and time-consuming numerical simulations used to understand and predict SLM processes.\"},{\"question\":\"What non-intrusive modeling strategy is proposed?\",\"answer\":\"The work builds a data-driven surrogate framework that combines machine learning with high-fidelity numerical simulation to speed up process prediction and optimization.\"},{\"question\":\"How is the proposed approach evaluated?\",\"answer\":\"The thesis compares model performance against multiple experimental test cases (including vertical and horizontal prism and an L-shaped structure) and also uses finite-element modeling with ANSYS Workbench Additive based on public-domain data.\"}]","Data-Driven Non-Intrusive Surrogate Modeling and Optimization of Selective Laser Melting Using Machine Learning Methods | 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problem does the thesis address in selective laser melting?","Question",{"text":76,"@type":77},"It targets the high cost and long time of trial-and-error experiments and time-consuming numerical simulations used to understand and predict SLM processes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What non-intrusive modeling strategy is proposed?",{"text":81,"@type":77},"The work builds a data-driven surrogate framework that combines machine learning with high-fidelity numerical simulation to speed up process prediction and optimization.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the proposed approach evaluated?",{"text":85,"@type":77},"The thesis compares model performance against multiple experimental test cases (including vertical and horizontal prism and an L-shaped structure) and also uses finite-element modeling with ANSYS Workbench Additive based on public-domain 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