[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126705-en":3,"doc-seo-126705-105":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":4,"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},126705,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials","Additive Manufacturing (AM) enables layer-by-layer fabrication from 3D models, and Functionally Graded Materials (FGMs) are especially important because their composition varies across locations to provide mechanical and physical properties tailored to use. This review surveys published literature on Machine Learning (ML) methods applied to AM of FGMs, focusing on optimizing fabrication parameters, detecting defects, and enabling real-time monitoring. It also summarizes key challenges and outlines future research directions for ML-enabled AM process development.","Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials  \nMohammad Karimzadeh 1, Deekshith Basvoju2, Aleksandar Vakanski2, Indrajit Charit2, Fei Xu3,  \nXinchang Zhang3  \n1 Department of Computer Science, University of Idaho, Moscow, ID 83844, USA  \n2 Department of Nuclear Engineering and Industrial Management, University of Idaho, Idaho Falls, ID 83402, USA  \n3 Idaho National Laboratory, Idaho Falls, ID 83415, USA  \nAbstract  \nAdditive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-be-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in AM fabrication ofFGMs.  \n1. INTRODUCTION  \nAdditive Manufacturing (AM) refers to a collection of manufacturing processes where materials are directly joined to manufacture freeform parts layer-by-layer from a 3D computer-aided design model [1–6]. This technology has revolutionized the manufacturing of complex parts by enabling direct material printing and offers several advantages such as cost-effectiveness, manufacturing waste reduction, and opening new possibilities for manufacturing automation. AM also enables mass part customization and eliminates the need for hard-tooling of machining setups, making it suitable for on-demand manufacturing, thus mitigating supply chain challenges [2–4] . Nevertheless, AM also presents certain challenges. These encompass a lack of inherent repeatability and a shortage of widespread design knowledge and tools [3, 7, 8] . Also, another challenge in the acceptance of AM components is the lack of a streamlined process qualification methodology [8] . Additionally, the quality of AM-produced parts can be lower in comparison to conventional manufacturing methods, since defects negatively influence the structural integrity of the parts, due to the complex physics of AM processes [9, 10] .  \nTo fully leverage the benefits of AM, the design, processing, and production have become more complex in recent years. Accordingly, these complexities require significant knowledge for selecting and optimization of the AM process parameters. Consequently, although crucial for achieving high-quality products and minimizing material and time losses, the selection of process parameters can be time-consuming and expensive. Moreover, compositional inconsistencies in AM components arise due to the complex physics and the need for knowledge-supported practices to avoid defects [10–12] .  \nOne group of materials for which AM holds great potential for enhancing component properties is Functionally Graded Materials (FGMs) . FGMs are advanced composite materials that are manufactured with a gradient composition transition between dissimilar materials, resulting in location-dependent mechanical and physical material properties [13, 14] . The composition gradient in FGMs enables enhancing the material properties by combining the advantages of different materials in a single component. Such material composition differs from traditional composite materials, characterized by a sharp i","cbCaipomgkq0WMmA","https://ap.wps.com/l/cbCaipomgkq0WMmA","pdf",466123,1,23,"English","en",105,"# Introduction\n## Additive manufacturing overview\n## Challenges in AM acceptance and quality\n## Functionally graded materials (FGMs)\n## AM methods for FGMs: DED and LDED\n## ML approaches for AM of FGMs","[{\"question\":\"Why are Functionally Graded Materials (FGMs) important in additive manufacturing?\",\"answer\":\"FGMs incorporate a gradient composition transition between dissimilar materials, producing location-dependent mechanical and physical properties that improve component performance across multiple industries.\"},{\"question\":\"What challenges in additive manufacturing motivate the use of machine learning?\",\"answer\":\"AM faces issues such as limited repeatability, insufficient design knowledge/tools, lack of streamlined qualification methodology, and defect-related quality degradation due to complex process physics.\"},{\"question\":\"Which tasks does the review highlight for ML methods in AM of FGMs?\",\"answer\":\"The review emphasizes ML for process parameter optimization, defect detection, and real-time monitoring, and discusses future directions and challenges for ML-enabled fabrication.\"}]","Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials | 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are Functionally Graded Materials (FGMs) important in additive manufacturing?","Question",{"text":75,"@type":76},"FGMs incorporate a gradient composition transition between dissimilar materials, producing location-dependent mechanical and physical properties that improve component performance across multiple industries.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenges in additive manufacturing motivate the use of machine learning?",{"text":80,"@type":76},"AM faces issues such as limited repeatability, insufficient design knowledge/tools, lack of streamlined qualification methodology, and defect-related quality degradation due to complex process physics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which tasks does the review highlight for ML methods in AM of FGMs?",{"text":84,"@type":76},"The review emphasizes ML for process parameter optimization, defect detection, and real-time monitoring, and discusses future directions and challenges for ML-enabled 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