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Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing

文献类型：期刊论文

作者Tamir, Tariku Sinshaw1,2; Xiong, Gang3,4; Shen, Zhen3,5; Leng, Jiewu1
刊名IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
出版日期2024-06-28
页码20
关键词3-D printing experimental data physicsinformed machine learning (PIML) simulation data social manufacturing warpage analysis
ISSN号2329-924X
DOI10.1109/TCSS.2024.3407823
通讯作者Leng, Jiewu(jwleng@gdut.edu.cn)
英文摘要Additive manufacturing (AM), also called 3-D printing, is a supporting technology in social manufacturing that has gained significant attention recently. As the AM industry grows, collecting and analyzing data are essential to ensure product quality, process efficiency, and cost-effectiveness. However, obtaining experimental data is challenging owing to cost and time constraints. Therefore, cost-effective and time-efficient strategies for collecting AM data are urgently required. This study proposes a novel data-collection approach that integrates the concept of finite element analysis (FEA) and physics-informed machine learning (PIML). We begin by discussing the importance of data collection in AM and the associated challenges. We then present various types of data that can be collected in AM, including the 3-D models and end-to-end data. End-to-end data comprise experimental data (i.e., sensors and images) and simulation data. Moreover, we present a case study that demonstrates the generation of simulation data and provides a detailed analysis of warpage. The STereoLithography (STL) file format of the BeltClip object from the Thingiverse possesses slicing through the Ultimaker (c) Cura software. The resulting G-code file is input to the Digimat-AM platform for virtual simulation of the BeltClip printing process. Digimat-AM, as a FEA simulation tool, then generates observational sample data. These data function as a roadmap for understanding the application of physical information for learning, which constitutes the observational bias aspect of PIML. The observational data obtained from the Digimat-AM is suggested for building a machine-learning model. Finally, we conclude with a discussion of inductive and learning biases in the prediction, control, and optimization aspects of AM.
WOS关键词OPTIMIZATION ; FRAMEWORK
资助项目National Key Research and Development Program of China[2022YFF0606005] ; National Natural Science Foundation of China[92267103] ; National Natural Science Foundation of China[92360307] ; National Natural Science Foundation of China[22108041] ; Beijing Natural Science Foundation[L233005]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001258783500001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/59166]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Leng, Jiewu
作者单位1.Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipment, Guangzhou 510006, Peoples R China
2.Debremarkos Univ, Inst Technol, Sch Elect & Comp Engn, Debremarkos 269, Debre Marqos, Ethiopia
3.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Guangdong Engn Res Ctr Printing & Intelligent Mfg, Cloud Comp Ctr, Dongguan 523808, Peoples R China
5.Qingdao Acad Intelligent Ind, Intelligent Mfg Ctr, Qingdao 266109, Peoples R China
推荐引用方式
GB/T 7714		 Tamir, Tariku Sinshaw,Xiong, Gang,Shen, Zhen,et al. Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2024:20.
APA Tamir, Tariku Sinshaw,Xiong, Gang,Shen, Zhen,&Leng, Jiewu.(2024).Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,20.
MLA Tamir, Tariku Sinshaw,et al."Physics-Driven Data Collection in 3-D Printing: Traversing the Realm of Social Manufacturing".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2024):20.

入库方式： OAI收割

来源：自动化研究所

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