A hybrid model compression approach via knowledge distillation for predicting energy consumption in additive manufacturing

Yixin Li, Fu Hu, Ying Liu, Michael Ryan and Ray Wang

International Journal of Production Research, 2023, vol. 61, issue 13, 4525-4547

Abstract: Recently, additive manufacturing (AM) has received increased attention due to its high energy consumption. By extracting hidden information or highly representative features from energy-relevant data, knowledge distillation (KD) reduces predictive model complexity and computational load. By using almost predetermined and fixed models, the distillation process restricts students and teachers from learning from one model to another. To reduce computational costs while maintaining acceptable performance, a teacher assistant (TA) was added to the teacher-student architecture. Firstly, a teacher ensemble was combined with three baseline models to enhance accuracy. In the second step, a teacher ensemble (TA) was formed to bridge the capacity gap between the ensemble and the simplified model. As a result, the complexity of the student model was reduced. Using geometry-based features derived from layer-wise image data, a KD-based predictive model was developed to evaluate the feasibility and effectiveness of two independently trained student models. In comparison with independently trained student models, the performance of the proposed method has the lowest RMSE, MAE, and training time.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2022.2160501 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:61:y:2023:i:13:p:4525-4547

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2022.2160501

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().