Effect of substrate temperature on bead track geometry of 316L in directed energy deposition: investigation and regression modeling

The optimization of process parameters in powder Directed Energy Deposition (DED) is essential for achieving consistent, high-quality bead geometries, which directly influence the performance and structural integrity of fabricated components. As a subset of additive manufacturing(AM), the DED process, also referred to as laser metal deposition (LMD), enables precise, layer-by-layer material deposition, making it highly suitable for complex geometries and part repairapplications. Critical parameters, such as the laser power, feed rate, powder mass flow, and substrate temperature govern the deposition process, impacting the bead height, width, contact angle, and dilution. Inconsistent control over these variables can lead to defects, such as poor bonding,dimensional inaccuracies, and material weaknesses, ultimately compromising the final product. This paper investigates the effects of various process parameters, specifically the substrate temperature, on bead track geometry in DED processes for stainless steel (1.4404). A specialized experimental setup, integrated within a DED machine, facilitates the controlled thermal conditioning of sample sheets. Using Design of Experiments (DoE) methods, individual bead marks are generated and analyzed toassess geometric characteristics. Regression models, including both linear and quadratic approaches,are constructed to predict machine parameters for achieving the desired bead geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the models,particularly in predicting the bead height, bead width, and contact angle across a broad range of substrate temperatures. However, the models demonstrated limitations in accurately predicting dilution, indicating the need for further refinement. Despite some deviations in measured values,successful fabrication is achieved, demonstrating robust bonding between the bead and substrate.The developed ...

Publication Details

Journal
Amtliche Mitteilungen (Universitätsbibliothek Paderborn)
Published
2024-12-13
DOI
https://doi.org/10.17619/unipb/1-2147
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effect of substrate temperature on bead track geometry of 316L in directed energy deposition: investigation and regression modeling

Amtliche Mitteilungen (Universitätsbibliothek Paderborn)
Additive Manufacturing Materials and Processes
article

Effect of substrate temperature on bead track geometry of 316L in directed energy deposition: investigation and regression modeling

article en

Abstract

The optimization of process parameters in powder Directed Energy Deposition (DED) is essential for achieving consistent, high-quality bead geometries, which directly influence the performance and structural integrity of fabricated components. As a subset of additive manufacturing(AM), the DED process, also referred to as laser metal deposition (LMD), enables precise, layer-by-layer material deposition, making it highly suitable for complex geometries and part repairapplications. Critical parameters, such as the laser power, feed rate, powder mass flow, and substrate temperature govern the deposition process, impacting the bead height, width, contact angle, and dilution. Inconsistent control over these variables can lead to defects, such as poor bonding,dimensional inaccuracies, and material weaknesses, ultimately compromising the final product. This paper investigates the effects of various process parameters, specifically the substrate temperature, on bead track geometry in DED processes for stainless steel (1.4404). A specialized experimental setup, integrated within a DED machine, facilitates the controlled thermal conditioning of sample sheets. Using Design of Experiments (DoE) methods, individual bead marks are generated and analyzed toassess geometric characteristics. Regression models, including both linear and quadratic approaches,are constructed to predict machine parameters for achieving the desired bead geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the models,particularly in predicting the bead height, bead width, and contact angle across a broad range of substrate temperatures. However, the models demonstrated limitations in accurately predicting dilution, indicating the need for further refinement. Despite some deviations in measured values,successful fabrication is achieved, demonstrating robust bonding between the bead and substrate.The developed ...

Amtliche Mitteilungen (Universitätsbibliothek Paderborn)
Deutsche Forschungsgemeinschaft, Universität Paderborn
Openalex Percentile: Top 67%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.