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Prediction of Tensile Behaviors of L-Ded 316 Stainless Steel Parts Using Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prediction of Tensile Behaviors of L-Ded 316 Stainless Steel Parts Using Machine Learning./
作者:	Era, Israt Zarin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	69 p.
附註:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
標題:	Fluid dynamics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28811830
ISBN:	9798494450555

Prediction of Tensile Behaviors of L-Ded 316 Stainless Steel Parts Using Machine Learning.
Era, Israt Zarin.

Prediction of Tensile Behaviors of L-Ded 316 Stainless Steel Parts Using Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 69 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (M.Sc.)--West Virginia University, 2021.

This item must not be sold to any third party vendors.

Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed and specimen height, play a great deal in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, a data driven machine learning model XGBoost has been built and applied to predict the tensile behaviors of the stainless steel 316 parts by DED with variation in process parameters i.e. laser power, scanning speed, layer height and energy density. For the validation purpose, molten pool temperature data has been provided to the model and it was able to predict the molten pool temperature successfully with a very high accuracy. After the tensile testing, the model was able to predict the tensile properties i.e. yield strength, elongation (%) and ultimate tensile strength of the fabricated parts with a limited size of training data and to compute the significance of the factors affecting the part quality. Performance of the model was then compared with ridge regression and XGBoost outperformed ridge regression.

ISBN: 9798494450555Subjects--Topical Terms:

545210
Fluid dynamics.

Prediction of Tensile Behaviors of L-Ded 316 Stainless Steel Parts Using Machine Learning.
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