Process Parameter Optimization of Additively Manufactured Parts Using Intelligent Manufacturing

Rizwan Ur Rehman, Uzair Khaleeq uz Zaman (), Shahid Aziz, Hamid Jabbar, Adnan Shujah, Shaheer Khaleequzzaman, Amir Hamza, Usman Qamar and Dong-Won Jung ()
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Rizwan Ur Rehman: Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Uzair Khaleeq uz Zaman: Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Shahid Aziz: Department of Mechanical Engineering, Jeju National University, Jeju-si 63243, Republic of Korea
Hamid Jabbar: Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Adnan Shujah: Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Shaheer Khaleequzzaman: School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Amir Hamza: Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Usman Qamar: Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
Dong-Won Jung: Department of Mechanical Engineering, Jeju National University, Jeju-si 63243, Republic of Korea

Sustainability, 2022, vol. 14, issue 22, 1-14

Abstract: 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.

Keywords: additive manufacturing; fused deposition modelling; machine learning; parameter optimization; stereolithography (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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