Investigation of Effect of Processing Parameters for Direct Energy Deposition Additive Manufacturing Technologies

13Citations
Citations of this article
50Readers
Mendeley users who have this article in their library.

Abstract

In order to capitalize on the cost-effectiveness of additive manufacturing (AM), it is critical to understand how to build components with consistency and high quality. Directed energy deposition (DED) is an AM method for creating parts layer by layer through the use of a moving heat source and powder material inserted into the melt pool generated on the substrate. DED, like most AM processes, is highly complex due to the rapid thermal gradients experienced during processing. These thermal gradients are determined by a variety of processing parameters, which include laser power, powder feed rate, travel speed, layer height hatch spacing, etc. A lot of effort has been carried out in the additive manufacturing community to understand what these critical parameters are and how they influence the thermal gradients. Despite all these efforts, AM industries rely on a trial-and-error-based approach to find the right set of parameters to produce a quality part. This is time-consuming and not a cost-effective use of AM technology. The aim of our research is to reduce the amount of experimental data in combination with numerical analysis to optimize this relationship. Physics-based two-dimensional melt-pool modeling and experimental results from an OPTOMEC 850M LENS will be utilized to investigate the effects of processing parameters on melt-pool geometry, and the results from this study will provide key processing guidelines to achieve desirable clad geometry and powder efficiency for the DED method.

References Powered by Scopus

Simulation of melt pool behaviour during additive manufacturing: Underlying physics and progress

239Citations
N/AReaders
Get full text

Review on thermal analysis in laser-based additive manufacturing

195Citations
N/AReaders
Get full text

On thermal modeling of Additive Manufacturing processes

175Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

17Citations
N/AReaders
Get full text

Application of Additive Manufacturing in the Automobile Industry: A Mini Review

12Citations
N/AReaders
Get full text

Use of Machine Learning to Improve Additive Manufacturing Processes

4Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Cho, K. T., Nunez, L., Shelton, J., & Sciammarella, F. (2023). Investigation of Effect of Processing Parameters for Direct Energy Deposition Additive Manufacturing Technologies. Journal of Manufacturing and Materials Processing, 7(3). https://doi.org/10.3390/jmmp7030105

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 10

59%

Researcher 4

24%

Professor / Associate Prof. 2

12%

Lecturer / Post doc 1

6%

Readers' Discipline

Tooltip

Engineering 13

68%

Materials Science 3

16%

Chemical Engineering 2

11%

Design 1

5%

Article Metrics

Tooltip
Mentions
Blog Mentions: 1
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free