期刊
SUSTAINABILITY
卷 14, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/su142215475
关键词
additive manufacturing; fused deposition modelling; machine learning; parameter optimization; stereolithography
资金
- ICT through the National Research Foundation of Korea [2021H1D3A2A01100014]
- National Research Foundation of Korea [2021H1D3A2A01100014] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper explores the use of Taguchi DOE and linear regression to optimize the input parameters for maximizing the tensile strength of a part in additive manufacturing. Experimental data is collected and analyzed to determine the optimal parameter settings, and a machine learning model is used to predict the tensile strength for various parameter combinations.
Additive manufacturing is the technique of combining materials layer by layer and process parameter optimization is a method used popularly for achieving the desired quality of a part. In this paper, four input parameters (layer height, infill density, infill pattern, and number of perimeter walls) along with their settings were chosen to maximize the tensile strength for a given part. Taguchi DOE was used to generate an L-27 orthogonal array which helped to fabricate 27 parts on the Ender 3 V2 fused deposition modeling (FDM) printer. The ultimate testing machine was used to test all 27 samples to generate the respective tensile strength values. Next, the Microsoft Azure ML database was used to predict the values of the tensile strength for various input parameters by using the data obtained from Taguchi DOE as the input. Linear regression was applied to the dataset and a web service was deployed through which an API key was generated to find the optimal values for both the input and output parameters. The optimum value of tensile strength was 22.69 MPa at a layer height of 0.28 mm, infill density of 100%, infill pattern of honeycomb, and the number of perimeter walls as 4. The paper ends with the conclusions drawn and future research directions.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据