Doctoral Dissertations

Date of Award

12-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Industrial Engineering

Major Professor

Andrew Yu

Committee Members

Zhongshun Shi, James Simonton, Feng Yuan Zhang

Abstract

Fused Deposition Modeling (FDM) can be purchased for under five hundred dollars. The availability of these inexpensive systems has created a large hobbyist (or maker) community. For makers, FDM printing is used numerous uses.

With the onset of the COVID-19 pandemic, the needs for Personal Protective Equipment (PPE) skyrocketed. COVID-19 mitigation strategies such as social distancing, businesses closures, and shipping delays created significant supply shortfalls. The maker community stepped in to fill gaps in PPE supplies.

In the case of 3DP, optimization remains the domain of commercial entities. Optimization is, at best, ad-hoc for makers. With the need to PPE supplies and COVID-19 related supply delays, optimization techniques would be of great value to makers.

The objective functions in this research is throughput and cost with quality factored into both. There are several parameters common to both throughput and surface roughness, including layer thickness, print speed, infill density, raster width, and wall thickness.

This research will utilize a 2-level fractional factorial design, in which process parameter had a specified upper (+1) and lower (-1) level. By using the upper and lower limits, this study will more closely align with the common maker workflow. The design will have a total of 16 trials, no main effect or 2-factor interactions are confounded with any other main effect or 2-factor interactions, this will allow the parameters to be estimated separately from one another without the requirement for conducting a full factorial (32 trials).

Least Squares Regression (OLS) will be completed on throughput and cost independently. Quality will be considered a component of both. For example, an OLS will be completed for the throughput to determine the respective effects of the process parameters on throughput. Using a 95% confidence interval, a process parameter with a P-value smaller that .05 will show that the process parameter has a significant effect on the throughput. Upon completion of each OLS model -Contraint methodology will be used to jointly optimize the process parameters. Validation trials will be completed to test the optimized process parameters. The results will be documented and discussed.

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