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

ABSTRACT: 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.

SUBMITTER: Lim JS

PROVIDER: S-EPMC8683500 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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

Lim Jong-Sup JS Oh Won-Jung WJ Lee Choon-Man CM Kim Dong-Hyeon DH

Scientific reports 20211217 1

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 accordin ...[more]

PMID: 34921196

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Dataset Information

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

Publications

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

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