Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-021-01845-5
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Mojtaba Khanzadeh & Sudipta Chowdhury & Mark A. Tschopp & Haley R. Doude & Mohammad Marufuzzaman & Linkan Bian, 2019. "In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes," IISE Transactions, Taylor & Francis Journals, vol. 51(5), pages 437-455, May.
- Biranchi Panda & K. Shankhwar & Akhil Garg & M. M. Savalani, 2019. "Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 809-820, February.
- A. Garg & Jasmine Siu Lee Lam & M. M. Savalani, 2018. "Laser power based surface characteristics models for 3-D printing process," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1191-1202, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
- Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.
- Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
- Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
- Jyothi Padmaja Koduru & T. Vijay Kumar & Kedar Mallik Mantrala, 2024. "A review of wire and arc additive manufacturing using different property characterization, challenges and future trends," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4563-4581, September.
- A. Chabot & N. Laroche & E. Carcreff & M. Rauch & J.-Y. Hascoët, 2020. "Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1191-1201, June.
- Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
- Hongjian Wu & Shaowu Liu & Xinliang Xie & Yicha Zhang & Hanlin Liao & Sihao Deng, 2022. "A framework for a knowledge based cold spray repairing system," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1639-1647, August.
- Yingjie Zhang & Wentao Yan, 2023. "Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2557-2580, August.
More about this item
Keywords
Selective melting laser; Bead geometry; Single-track morphology; Machine learning; Artificial neural network; Bayesian optimization;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01845-5. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.