Process parameter study for enhancement of directed energy deposition powder efficiency based on single-track geometry evaluation

2021 ◽  
Vol 33 (4) ◽  
pp. 042023
Author(s):  
Zoé Jardon ◽  
Julien Ertveldt ◽  
Michaël Hinderdael ◽  
Patrick Guillaume
Keyword(s):  
Energy Deposition ◽  
Process Parameter ◽  
Parameter Study ◽  
Single Track ◽  
Track Geometry ◽  
Directed Energy Deposition ◽  
Directed Energy

scholarly journals Directed energy deposition of AlSi10Mg: Single track nonscalability and bulk properties

2020 ◽  
Vol 194 ◽  
pp. 108847 ◽  
Author(s):  
Parnian Kiani ◽  
Alexander D. Dupuy ◽  
Kaka Ma ◽  
Julie M. Schoenung
Keyword(s):  
Energy Deposition ◽  
Single Track ◽  
Bulk Properties ◽  
Directed Energy Deposition ◽  
Directed Energy

Effect of Process Parameters on Laser Directed Energy Deposition of Copper

2019 ◽  
Author(s):  
Sunil Yadav ◽  
Christ P. Paul ◽  
Arackal N. Jinoop ◽  
Saurav K. Nayak ◽  
Arun K. Rai ◽  
...  
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Laser Power ◽  
Energy Deposition ◽  
Process Conditions ◽  
Powder Feed Rate ◽  
Track Geometry ◽  
Powder Feed ◽  
Directed Energy Deposition ◽  
Directed Energy

Abstract Laser Additive Manufacturing (LAM) is an advanced manufacturing processes for fabricating engineering components directly from CAD Model by depositing material in a layer by layer fashion using lasers. LAM is being widely deployed in various sectors such as power, aerospace, automotive etc. for fabricating complex shaped and customized components. One of the most commonly used LAM process is Directed Energy Deposition (LAM-DED) which is used for manufacturing near net shaped components with tailored microstructure, multi-materials (direct and graded) and complex geometry. This paper reports experimental investigation of LAM of Copper (Cu) tracks on Stainless Steel 304 L (SS 304L) using an indigenously developed LAM-DED system. Cu-SS304L joints find wider applications in tooling, automotive and aerospace sectors due to its combination of higher strength, thermal conductivity and corrosion resistance. However, laying Cu layers on SS304L is not trivial due to large difference in the thermo-physical properties. Thus, a comprehensive experiments using full factorial design are carried out and a number of Cu tracks were laid on SS304L substrate by varying laser power, scan speed and powder feed rate. The laid tracks are characterized for track geometry and porosity and the quality of the tracks are analyzed. Lower values of laser power and higher powder feed rate results in discontinuous deposition, while higher laser power and lower powder feed rate results in cracked deposits. Porosity is observed to vary from 6–45 % at different process conditions. Analysis of Variance (ANOVA) of deposition rate and track geometry is performed to estimate the major contributing process parameters. This study paves a way to understand effect of process parameters on LAM-DED for fabricating bimetallic joints and graded structures of Copper and SS304L.

scholarly journals Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jong-Sup Lim ◽  
Won-Jung Oh ◽  
Choon-Man Lee ◽  
Dong-Hyeon Kim
Keyword(s):  
Process Parameters ◽  
Energy Deposition ◽  
Classification Models ◽  
Single Track ◽  
Surface Color ◽  
Directed Energy Deposition ◽  
Color Data ◽  
Directed Energy ◽  
Multi Classification ◽  
Validation Experiments

AbstractIn the directed energy deposition (DED) process, significant empirical testing is required to select the optimal process parameters. In this study, single-track experiments were conducted using laser power and scan speed as parameters in the DED process for titanium alloys. The results of the experiment confirmed that the deposited surface color appeared differently depending on the process parameters. Cross-sectional view, hardness, microstructure, and component analyses were performed according to the color data, and a color suitable for additive manufacturing was selected. Random forest (RF) and support vector machine multi-classification models were constructed by collecting surface color data from a titanium alloy deposited on a single track; the accuracies of the multi-classification models were compared. Validation experiments were performed under conditions that each model predicted differently. According to the results of the validation experiments, the RF multi-classification model was the most accurate.

scholarly journals Correction to: Characterization and Optimization of Process Parameters for Directed Energy Deposition Powder-Fed Laser System

2021 ◽  
Author(s):  
Daniel Andres Rojas Perilla ◽  
Johan Grass Nuñez ◽  
German Alberto Barragan De Los Rios ◽  
Fabio Edson Mariani ◽  
Reginaldo Teixeira Coelho
Keyword(s):  
Process Parameters ◽  
Energy Deposition ◽  
Laser System ◽  
Directed Energy Deposition ◽  
Optimization Of Process Parameters ◽  
Directed Energy
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