PreprintCommunicationVersion 1Preserved in Portico This version is not peer-reviewed
Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L
Version 1
: Received: 23 April 2024 / Approved: 24 April 2024 / Online: 25 April 2024 (02:35:50 CEST)
How to cite:
Gnaase, S.; Walter, A.; Rohling, R.; Tröster, T. Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L. Preprints2024, 2024041587. https://doi.org/10.20944/preprints202404.1587.v1
Gnaase, S.; Walter, A.; Rohling, R.; Tröster, T. Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L. Preprints 2024, 2024041587. https://doi.org/10.20944/preprints202404.1587.v1
Gnaase, S.; Walter, A.; Rohling, R.; Tröster, T. Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L. Preprints2024, 2024041587. https://doi.org/10.20944/preprints202404.1587.v1
APA Style
Gnaase, S., Walter, A., Rohling, R., & Tröster, T. (2024). Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L. Preprints. https://doi.org/10.20944/preprints202404.1587.v1
Chicago/Turabian Style
Gnaase, S., Robin Rohling and Thoas Tröster. 2024 "Effect of Substrate Temperature on Weld Track Geometry in Directed Energy Deposition: A Comprehensive Investigation and Regression Modeling of processing 316L" Preprints. https://doi.org/10.20944/preprints202404.1587.v1
Abstract
This paper investigates the effects of various process parameters, specifically the substrate temperature, on weld track geometry in Directed Energy Deposition (DED) processes. A specialized experimental setup integrated within a DED machine facilitates controlled thermal conditioning of sample sheets. Through Design of Experiments (DoE) methods, individual weld marks are generated and analysed to assess geometric characteristics. Regression models are constructed to predict machine parameters for desired weld geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the regression models. Results show a consistent trend towards target geometric features across a broad range of substrate temperatures. Despite deviations in measured values, successful fabrication is achieved, demonstrating robust bonding between weld and substrate. Contact angle predictions exhibit precision within a partial temperature range for proper deposition. The developed model offers insights for optimizing DED process parameters to achieve desired weld characteristics, advancing the capabilities and reliability of additive manufacturing technology. Future work aims to refine the regression model and explore additional mathematical relationships for enhanced accuracy. An implication of this work is the potential to vary the local mechanical properties of parts by controlling the temperature profile while maintaining consistent geometric characteristics. By manipulating the substrate temperature, it may be possible to tailor the microstructure and mechanical properties of fabricated components in the future to meet specific requirements in different regions of the part.
Keywords
additive manufacturing; direct energy deposition; laser metal deposition; DED; LMD; 316L; 1.4404
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
