[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120959-en":3,"doc-seo-120959-105":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},120959,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Optimization of 4D/3D printing via machine learning - A systematic review","This systematic review explores the integration of 4D/3D printing technologies with machine learning, shaping a new era of manufacturing innovation. The analysis synthesizes a wide range of research papers, articles, and patents, offering a multidimensional view of progress in additive manufacturing. It highlights how machine learning supports optimization through design customization, material selection, process control, and quality assurance, enabling intelligent self-adaptive structures and predictive algorithms. Applications span aerospace, healthcare, architecture, and consumer goods, improving efficiency, reliability, and sustainability while addressing future manufacturing needs.","Hybrid Advances 6 (2024) 100242  \nContents lists available at ScienceDirect  \nHybrid Advances  \njournal [homepage: www.journals.elsevier.com/hybrid-advances](homepage: www.journals.elsevier.com/hybrid-advances)  \n| Review Article\u003Cbr>Optimization of 4D/3D printing via machine learning: A systematic review\u003Cbr>Yakubu Adekunle Allia, b, c, * , Hazleen Anuara, ** , Mohd Romainor Manshora, d , Christian Emeka Okafore, Amjad Fakhri Kamarulzamana , Nürettin Akçakalef,\u003Cbr>Fatin Nurafiqah Mohd Nazeria , Mahdi Bodaghig, Jonghwan Suhrh , Nur Aimi Mohd Nasiria Department of Manufacturing and Materials Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Jalan Gombak, 53100, Kuala Lumpur, Malaysia\u003Cbr>b CNRS, LCC (Laboratoire de Chimie de Coordination), UPR8241, Universit´e de Toulouse, UPS, INPT, Toulouse, cedex 4 F-31077, France\u003Cbr>c Department of Chemical Sciences, Faculty of Science and Computing, Ahman Pategi University, Km 3, Patigi-Kpada Road, Patigi, Kwara State, Nigeria d Food Sciences & Technology Research Center, Malaysian Agricultural Research and Development Institute, Persiaran MARDI-UPM, 43400, Serdang, Selangor, Malaysia\u003Cbr>e Department of Mechanical Engineering, Nnamdi Azikiwe University, Awka, Nigeria\u003Cbr>f Textile, Clothing, Footwear and Leather Department, Gerede Vocational School of Higher Education, Abant ˙Izzet Baysal University, Bolu, 14900, Turkey g Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK\u003Cbr>h Faculty of Mechanical Engineering, College of Engineering, Sungkyunkwan University, Natural Sciences Campus, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, South Korea\u003Cbr>i NanoVerify Sdn Bhd, e-8-6 (Suite 5.8), Block E, Megan Avenue 1, 189, Jalan Tun Razak, 54050, Kuala Lumpur, Malaysia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>4D printing Machine learning 3D printing Smart materials |  | This systematic review explores the integration of 4D/3D printing technologies with machine learning, shaping anew era of manufacturing innovation. The analysis covers a wide range of research papers, articles, and patents, presenting a multidimensional perspective on the advancements in additive manufacturing. The review underscores machine learning’s pivotal role in optimizing 4D/3D printing, addressing aspects like design customization, material selection, process control, and quality assurance. The examination reveals novel techniques enabling the fabrication of intelligent, self-adaptive structures capable of transformation over time. Additionally, the review investigates the use of predictive algorithms to enhance efficiency, reliability, and sustainability in 4D/3D printing processes. Applications span aerospace, healthcare, architecture, and consumer goods, showcasing the potential to create intricate, personalized, and once-unattainable functional products. The synergy between machine learning and 4D/3D printing is poised to unlock new manufacturing horizons, enabling rapid responses to market demands and sustainability challenges. In summary, this review provides a comprehensive overview of the current state of 4D/3D printing optimization through machine learning, highlighting the transformative potential of this interdisciplinary fusion and offering a roadmap for future research and development. It aims to inspire innovators, researchers, and industries to harness this powerful combination for accelerated evolution in manufacturing processes into the 21st century and beyond. |\n\n1. Introduction  \n3D printing, also known as one of additive manufacturing techniques, has undergone a huge advancement since its inception in 1980 [1]. 3D printing has been vastly utilized by the people in every sector whether from the consumers or manufacturers perspective in recent years [2]. It has advanced 3D printing technology to a point where users  \ncan now use it to generate their own designs ","cbCainC74SjWIAiq","https://ap.wps.com/l/cbCainC74SjWIAiq","pdf",7482867,1,21,"English","en",105,"# Introduction\n## Challenges and applications of 4D/3D printing\n# Abstract and keywords\n## Machine learning for optimization","[{\"question\":\"How does the review connect machine learning with 4D/3D printing optimization?\",\"answer\":\"It summarizes how machine learning can optimize design customization, material selection, process control, and quality assurance within 4D/3D printing workflows.\"},{\"question\":\"What kinds of technologies or outcomes are emphasized for 4D/3D printing?\",\"answer\":\"The review highlights techniques that enable fabrication of intelligent, self-adaptive structures capable of transformation over time, supported by predictive algorithms.\"},{\"question\":\"Where are the discussed applications of optimized 4D/3D printing most relevant?\",\"answer\":\"The document reports applications across aerospace, healthcare, architecture, and consumer goods, aiming at intricate, personalized, functional products.\"}]","Optimization of 4D/3D printing via machine learning - 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