The influence of key process parameters on melt pool geometry in direct energy deposition additive manufacturing systems

Sampson, Robert E.; Lancaster, Robert; Sutcliffe, Mark; Carswell, David; Hauser, Carl; Barras, Josh

doi:10.1016/j.optlastec.2020.106609

Cited by 39 publications

(11 citation statements)

References 20 publications

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“…Thus, studies have been conducted on the correlation between process parameters and output quality. Sampson et al studied the effects of the powder mass flow rate and path velocity changes on the molten pool 26 The increase in the height and width of the single track with an increase in the powder feed rate is affected by the laser power; it cannot be simply assumed that the melt pool width increases as the powder feed rate increases. In this experiment, the powder feed rate was set to 14 g/min, considering the laser power and conservation of powder.…”

Section: Resultsmentioning

confidence: 99%

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

Lim

Lee

et al. 2021

Sci Rep

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In 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.

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Section: Resultsmentioning

confidence: 99%

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

Lim

Lee

et al. 2021

Sci Rep

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“…Among the other process parameters, at the present time, it is consolidated that the process parameters that mostly affect the temperature distribution and consequently the melt pool dimensions [12] are the powder feed rate (Q), the travel speed (v) and the laser power (P) [9,12,82]. In the following paragraphs the effect of these parameters on the width, on the height and on the depth of the melt pool has been described.…”

Section: Thermal Behaviourmentioning

confidence: 99%

“…Conversely, Hu, et al [84] and Peyre, et al [72] observed that the melt pool width slightly increased with the powder feed rate. Consequently, the effect of powder feed rate on melt pool width is highly dependent on the value of laser power and it could not be studied independently [82].…”

Section: Effect Of Powder Feed Ratementioning

confidence: 99%

An Overview of Physical Phenomena Involved in the Laser Powder Directed Energy Deposition Process

Piscopo¹,

Atzeni²,

Saboori³

et al. 2022

Preprint

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Laser Powder Directed Energy Deposition (LP-DED) is an Additive Manufacturing process in which a laser beam is used to generate a melt pool onto a substrate. The additional material, in form of powder material, is fed into the generated melt pool and a raised track is obtained. The LP-DED process is very powerful for different applications such as the repair operations and the production of functionally graded material, however the application is still limited and one of the main reasons is related to the lack of knowledge in the physics of the process mechanisms. The process is influenced by a huge number of parameters and three main mechanisms can be identi-fied. These mechanisms are the powder flow, the generation of the molten pool and the solidifica-tion process and for each of these mechanisms the process parameters have a different relevance. In this paper, a review of the main mechanisms and the relationship between the process parame-ters and the outcome of each step of the process is presented.

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“…Then, for the rest of the AM process, it is subjected to cooling-heating cycles in the solid-state i.e., solid-state thermal cycling (SSTC), also known as intrinsic heat treatment, at varying temperature amplitudes and rates. Current experiment [1][2][3][4][5][6][7][8][9][10][11] and modeling [12][13][14][15][16][17] efforts are overwhelmingly focused on understanding microstructure formation due to melt-pool dynamics and rapid solidification. While these phenomena are important to understand microstructure genesis during AM, however, they are insufficient to understand the formation of the final microstructure of as-built parts.…”

Section: Introductionmentioning

confidence: 99%

Insight on precipitate evolution during additive manufacturing of stainless steels via in-situ heating-cooling experiments in a transmission electron microscope

et al. 2022

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The influence of key process parameters on melt pool geometry in direct energy deposition additive manufacturing systems

Cited by 39 publications

References 20 publications

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

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

An Overview of Physical Phenomena Involved in the Laser Powder Directed Energy Deposition Process

Insight on precipitate evolution during additive manufacturing of stainless steels via in-situ heating-cooling experiments in a transmission electron microscope

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