A survey of machine learning in additive manufacturing technologies

被引:50
作者
Jiang, Jingchao [1 ]
机构
[1] Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China
关键词
Additive manufacturing; machine learning; review; POWDER BED FUSION; CONVOLUTIONAL NEURAL-NETWORK; DIMENSIONAL ACCURACY; LASER; PREDICTION; POROSITY; OPTIMIZATION; FABRICATION; TRENDS; PARTS;
D O I
10.1080/0951192X.2023.2177740
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Thirty years into its development, additive manufacturing has become a mainstream manufacturing process. Additive manufacturing fabricates products by adding materials layer-by-layer directly based on a 3D model. It is able to manufacture complex parts and allows more freedom of design optimization compared with traditional manufacturing techniques. Machine learning is now a hot technology that has been used in medical diagnosis, image processing, prediction, classification, learning association, regression, etc. Currently, focuses are increasingly given to using machine learning in the manufacturing industry, including additive manufacturing. Due to the rapid development of machine learning in additive manufacturing, a special issue 'Machine Learning in Additive Manufacturing' in International Journal of Computer Integrated Manufacturing is organized. This paper gives a comprehensive understanding of the current status of machine learning enhanced additive manufacturing technologies for this special issue. Discussions and future perspectives are also provided.
引用
收藏
页码:1258 / 1280
页数:23
相关论文
共 121 条
[1]   Vat photopolymerization 3D printing of acid-cleavable PEG-methacrylate networks for biomaterial applications [J].
Aduba, Donald C., Jr. ;
Margaretta, Evan D. ;
Marnot, Alexandra E. C. ;
Heifferon, Katherine V. ;
Surbey, Wyatt R. ;
Chartrain, Nicholas A. ;
Whittington, Abby R. ;
Long, Timothy E. ;
Williams, Christopher B. .
MATERIALS TODAY COMMUNICATIONS, 2019, 19 :204-211
[2]   Predicting the compressive strength of additively manufactured PLA-based orthopedic bone screws: A machine learning framework [J].
Agarwal, Raj ;
Singh, Jaskaran ;
Gupta, Vishal .
POLYMER COMPOSITES, 2022, 43 (08) :5663-5674
[3]  
[Anonymous], 2017, Stratasys
[4]  
[Anonymous], 2021, ISO/ASTM52900
[5]  
[Anonymous], 2020, MACH LEARN
[6]   Simple method to construct process maps for additive manufacturing using a support vector machine [J].
Aoyagi, Kenta ;
Wang, Hao ;
Sudo, Hideki ;
Chiba, Akihiko .
ADDITIVE MANUFACTURING, 2019, 27 :353-362
[7]   Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing [J].
Baturynska, Ivanna .
APPLIED SCIENCES-BASEL, 2019, 9 (06)
[8]   Application of Machine Learning Methods to Improve Dimensional Accuracy in Additive Manufacturing [J].
Baturynska, Ivanna ;
Semeniuta, Oleksandr ;
Wang, Kesheng .
ADVANCED MANUFACTURING AND AUTOMATION VIII, 2019, 484 :245-252
[9]  
Beaman J.J., 1991, Selective laser sintering with assisted powder handling (US4938816 A)
[10]   Ceramic components manufacturing by selective laser sintering [J].
Bertrand, Ph. ;
Bayle, F. ;
Combe, C. ;
Goeuriot, P. ;
Smurov, I. .
APPLIED SURFACE SCIENCE, 2007, 254 (04) :989-992