Document Type
Article
Publication Date
6-2022
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Industrial and Managements Systems Engineering
Abstract
Fused Deposition Modeling (FDM) is a process that allows for the rapid production of functional parts through the deposition of fused material layers in a sequential manner. FDM has flexibility and the potential to create complicated parts. This study aims to optimize the FDM process parameters in terms of tensile strength, flexural strength, and longitudinal shrinkage using the Grey-Taguchi approach. The input parameters chosen to study the effects on dimension and mechanical properties are layer thickness, the raster angle, fill density, the number of contours, printing temperature, and printing speed. The Taguchi L27 orthogonal array is used as the statistical design of experiment (DOE) technique to assess how the FDM process behaves with the change in process input parameters selected. The ANOVA test was used to assess the contribution and implications of each response factor of the FDM process on the FDM process. Additionally, the optimization of multiple characteristics is done by Grey relational analysis. Optimal parameter settings that minimize longitudinal shrinkage and maximize tensile and flexural strengths concurrently are 0.2 mm of layer thickness, 90-degree raster angle, fill density of 30, 16 numbers of contours, 230℃ printing temperature, and a printing speed of 60 mm/s.
Digital Commons Citation
Syed, Md Asif Bin; Rhaman, Qausar; Shahriar, Hasan Md; and Khan, Mohammad Muhshin Aziz, "Grey-Taguchi Approach to Optimize Fused Deposition Modeling Process in Terms of Mechanical Properties and Dimensional Accuracy" (2022). Graduate Student Scholarship. 7.
https://researchrepository.wvu.edu/grad_scholarship/7
Source Citation
M. A. B. Syed, Q. Rhaman, H. M. Shahriar, and M. M. A. Khan, "Grey-Taguchi Approach to Optimize Fused Deposition Modeling Process in Terms of Mechanical Properties and Dimensional Accuracy," J. Eng. Res. Innov. Educ., vol. 4, no. 1, pp. 38-52, Jun. 2022.