Optimizing the Design for Additive Manufacturing Project in the Manufacturing Processes Lab Course Using the Taguchi Orthogonal Arrays

Omar Ahmed Al-Shebeeb

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Conference: 2024 ASEE North Central Section Conference
Location: Kalamazoo, Michigan
Publication Date: March 22, 2024
Start Date: March 22, 2024
End Date: March 23, 2024
Page Count: 9
DOI: 10.18260/1-2--45629
Permanent URL: https://peer.asee.org/45629
Download Count: 34

Paper Authors

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Omar Ahmed Al-Shebeeb West Virginia University

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Dr. Omar Al-Shebeeb is a Teaching Assistant Professor in the Industrial and Management Systems Engineering (IMSE), WVU since January 2020. He finished his Ph.D. in the IMSE Department at WVU (2019). Then, he started his job as an Academic Program Director at Greenville Technical College. While Dr. Al-Shebeeb was pursuing his Ph.D. degree at West Virginia University, he was working as a Graduate Teaching Assistant in the IMSE Department for four years. Dr. Al-Shebeeb obtained his M.S. and B.S. degrees in Production (Manufacturing) Engineering from the Production and Metallurgy Engineering Department at the University of Technology, Iraq. Dr. Al-Shebeeb was working as an Assistant Professor (2011-2013) and Instructor (2007-2009) at the University of Diyala, Iraq. He had taught several courses in the mechanical, production, and manufacturing engineering fields. His areas of research interest are Design for Manufacturing and Assembly (DFMA) and Design Efficiency, productivity improvement, advanced manufacturing, and technologies, Subtractive and Additive Manufacturing, and CAD/CAM/CIM/CIE systems and applications. Dr. Al-Shebeeb has been teaching more several graduate and undergraduate courses at WVU. He has several publications in journals, conferences, and book chapters. He is an active member of American Society for Engineering Education (ASEE), American Society of Mechanical Engineers (ASME), Society of Manufacturing Engineers (SME), Society of Automotive Engineering (SAE) International, Institute of Industrial and Systems Engineers (IISE), Industrial Engineering and Operations Management (IEOM), and WVU IE Leaders.

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Abstract

As 3D printing technology becomes more extensively used and more users have access to its immense potential, questions regarding which machine parameters affect the performance of the produced object arise. One of the primary projects taught and implemented in the Manufacturing Processes Lab course is the Design for Additive Manufacturing (DfAM). One of the key challenges we had when executing this procedure was determining how to optimize the 3D printing parameters and increase the quality of the manufactured items. In the other course, Design for Manufacturability (DfM), that I am teaching, I was presenting Taguchi Orthogonal Arrays and Quality Loss Functions (QLF) as tools for Design for Quality projects in the DfM course. In the Manufacturing Processes Lab course, I opted to use Taguchi Orthogonal Arrays to investigate the performance of the DfAM project in the Manufacturing processes course. This report seeks to address some of these 3D printing difficulties. The Taguchi Quality research was performed in two different ways on two distinct 3D printing systems to assess the effects of 3D printer settings on part quality.

In the first evaluation, the performance of the pieces was initially evaluated by applying a tensile load to a hook style model till failure. Part thickness, infill pattern, number of perimeters, infill ratio, layer height, nozzle temperature, and printer speed were all changed in this experiment. Each component has two alternative values, and each experiment was designed using a L8 (27) array within the Taguchi method. A total of 8 experiments were designed with two tests per experiment for a total of 16 data points collected. Our testing concluded the most important factors of the seven investigated were the number of perimeters, infill ratio, and layer height. An optimized set of factor values was established based on the Taguchi method, with an estimated strength value of 44.64 kg under tensile loading.

In the second evaluation, six factors were investigated in this experiment, as well as one interaction between two factors. These variables comprised the part's width, thickness, fillet radius, nozzle temperature, layer cross-sectional direction, and layer height. The width and layer height interaction were investigated. Three trials of eight distinct tensile strength experiments were performed to test the factors. The Taguchi orthogonal array was used to calculate the factors applied to each specimen, and each factor was examined. The second evaluation revealed that the width and thickness of the pieces were the most critical criteria. Except for nozzle temperature, all lower values for the six parameters were shown to have a higher strength-to-weight ratio. The best signal-to-noise ratio was found in Experiment 1. When both parameters were at Level 1 (lower values), the interaction between width and layer height was shown to be the best. Based on desirable qualities, the optimal strength-to-weight ratio was determined to be 35.09 MPa/g.

Citation
Format

Al-Shebeeb, O. A. (2024, March), Optimizing the Design for Additive Manufacturing Project in the Manufacturing Processes Lab Course Using the Taguchi Orthogonal Arrays Paper presented at 2024 ASEE North Central Section Conference, Kalamazoo, Michigan. 10.18260/1-2--45629

TY - CPAPER
AB - As 3D printing technology becomes more extensively used and more users have access to its immense potential, questions regarding which machine parameters affect the performance of the produced object arise. One of the primary projects taught and implemented in the Manufacturing Processes Lab course is the Design for Additive Manufacturing (DfAM). One of the key challenges we had when executing this procedure was determining how to optimize the 3D printing parameters and increase the quality of the manufactured items. In the other course, Design for Manufacturability (DfM), that I am teaching, I was presenting Taguchi Orthogonal Arrays and Quality Loss Functions (QLF) as tools for Design for Quality projects in the DfM course. In the Manufacturing Processes Lab course, I opted to use Taguchi Orthogonal Arrays to investigate the performance of the DfAM project in the Manufacturing processes course. This report seeks to address some of these 3D printing difficulties. The Taguchi Quality research was performed in two different ways on two distinct 3D printing systems to assess the effects of 3D printer settings on part quality.

In the first evaluation, the performance of the pieces was initially evaluated by applying a tensile load to a hook style model till failure. Part thickness, infill pattern, number of perimeters, infill ratio, layer height, nozzle temperature, and printer speed were all changed in this experiment. Each component has two alternative values, and each experiment was designed using a L8 (27) array within the Taguchi method. A total of 8 experiments were designed with two tests per experiment for a total of 16 data points collected. Our testing concluded the most important factors of the seven investigated were the number of perimeters, infill ratio, and layer height. An optimized set of factor values was established based on the Taguchi method, with an estimated strength value of 44.64 kg under tensile loading.

In the second evaluation, six factors were investigated in this experiment, as well as one interaction between two factors. These variables comprised the part&#39;s width, thickness, fillet radius, nozzle temperature, layer cross-sectional direction, and layer height. The width and layer height interaction were investigated. Three trials of eight distinct tensile strength experiments were performed to test the factors. The Taguchi orthogonal array was used to calculate the factors applied to each specimen, and each factor was examined. The second evaluation revealed that the width and thickness of the pieces were the most critical criteria. Except for nozzle temperature, all lower values for the six parameters were shown to have a higher strength-to-weight ratio. The best signal-to-noise ratio was found in Experiment 1. When both parameters were at Level 1 (lower values), the interaction between width and layer height was shown to be the best. Based on desirable qualities, the optimal strength-to-weight ratio was determined to be 35.09 MPa/g.

AU - Omar Ahmed Al-Shebeeb
CY - Kalamazoo, Michigan
DA - 2024/03/22
PB - ASEE Conferences
TI - Optimizing the Design for Additive Manufacturing Project in the Manufacturing Processes Lab Course Using the Taguchi Orthogonal Arrays
UR - https://peer.asee.org/45629
DO - 10.18260/1-2--45629
ER -