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A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning

文献类型：期刊论文

作者Fang, Qihang1,2; Xiong, Gang3; Wang, Weixing1; Shen, Zhen3; Dong, Xisong1; Wang, Fei-Yue1
刊名JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
出版日期2024-08-01
卷号24期号:8页码:15
关键词additive manufacturing database process planning defect detection laser powder bed fusion vat photopolymerization
ISSN号1530-9827
DOI10.1115/1.4065344
通讯作者Shen, Zhen(zhen.shen@ia.ac.cn)
英文摘要Additive manufacturing (AM) is gaining prominence across numerous fields, which involves the generation of extensive data at each process stage. A relational database is a useful tool to store such AM data and streamline data retrieval. Users can specify the value of one AM variable or attribute and retrieve the corresponding record values of another attribute. This establishes the correlations between AM variables, and supports applications such as process planning. Nonetheless, such an operation is a "hard" query, which lacks reasoning capabilities and fails to provide useful information when required records are missing. It is urgent to develop a more powerful AM database to handle AM data better, which should support "soft" queries, be scalable to high-dimensional data, and maintain flexible query functionality among multiple attributes. In this paper, we construct an AM database with probabilistic modeling and transformation-invariant feature learning, which is termed as a probabilistic AM database (PAMDB). The PAMDB allows the selection of any AM attribute as a query attribute, or even multiple attributes as query attributes, to retrieve the values of other attributes, which is adapted to unseen, high-dimensional, and multimodal AM data. Two case studies were conducted for laser powder bed fusion (LPBF) and vat photopolymerization (VP). Compared with existing methods, experimental results underscore the efficacy of the PAMDBs, both qualitatively and quantitatively, in tasks that includes melt pool size prediction and scan parameter estimation in LPBF, and defect detection for the resin deposition process in VP.
资助项目Guangdong Basic and Applied Basic Research Foundation[2021B1515140034] ; National Natural Science Foundation of China[92267103] ; National Natural Science Foundation of China[92360307] ; Beijing Natural Science Foundation[L233005]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001271424000007
出版者ASME
资助机构Guangdong Basic and Applied Basic Research Foundation ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/59254]  
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Shen, Zhen
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
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
推荐引用方式
GB/T 7714		 Fang, Qihang,Xiong, Gang,Wang, Weixing,et al. A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning[J]. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING,2024,24(8):15.
APA Fang, Qihang,Xiong, Gang,Wang, Weixing,Shen, Zhen,Dong, Xisong,&Wang, Fei-Yue.(2024).A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning.JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING,24(8),15.
MLA Fang, Qihang,et al."A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning".JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 24.8(2024):15.

入库方式： OAI收割

来源：自动化研究所

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