Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process-Reference-Cited by-同舟云学术

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Published:2024-05-27 Issue:1 Volume:19 Page:
ISSN:1745-2759
Container-title:Virtual and Physical Prototyping
language:en
Short-container-title:Virtual and Physical Prototyping

Author:

Pandiyan Vigneashwara¹²^ORCID,Baganis Antonios¹³^ORCID,Axel Richter Roland¹,Wróbel Rafał¹⁴,Leinenbach Christian¹³

Affiliation:

1. Laboratory for Advanced Materials Processing (LAMP), Swiss Federal Laboratories for Materials Science and Technology (Empa), Thun, Switzerland

2. Additive Manufacturing Group, Advanced Materials Research Center, Technology Innovation Institute (TII), Masdar City, United Arab Emirates

3. Laboratory for Photonic Materials and Characterization, Ecole Polytechnique Federale de Lausanne Lausanne, Switzerland

4. Department of Materials, Laboratory for Nanometallurgy, ETH Zürich, Zürich, Switzerland

Funder

Strategic Focus Area Advanced Manufacturing

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17452759.2024.2356080

Reference77 articles.

1. Process parameter selection and optimization of laser powder bed fusion for 316L stainless steel: A review

2. Dependence of mechanical properties on crystallographic orientation in nickel-based superalloy Hastelloy X fabricated by laser powder bed fusion

3. Developing ductile and isotropic Ti alloy with tailored composition for laser powder bed fusion

4. Review of high-strength aluminium alloys for additive manufacturing by laser powder bed fusion

5. Fabrication of a strong and ductile CuCrZr alloy using laser powder bed fusion