Degree Type

Thesis

Date of Award

2018

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

Major

Industrial and Manufacturing Systems Engineering

First Advisor

G�l Kremer

Abstract

Additive Manufacturing (AM) is known for its ability to manufacture complex parts layer by layer using 3D design data. AM brings significant freedom in design, yet it can get hard to produce the same parts with identical dimensional tolerances, a.k.a. reproducibility problem. Reproducibility, the ability to produce the same part again under same conditions, is one of the major challenges in AM as it plays an important role in the replacement of worn-out/damaged parts in an assembly. Ceramics, metals, alloys, and plastics are being used for the biomedical implants in which the concept of reproducibility is crucial. To obtain quality products and maintain consistency, this study is conducted to analyze the effects of most common and critical factors – layer thickness, printing speed, orientation angle on dimensional accuracy and surface roughness of AM parts. A full-factorial Design of Experiment (DOE) involving these factors with three levels each is implemented to determine their effect on overall length, height, width, middle height, and surface roughness, which are the response parameters. A dog-bone shaped tensile testing specimen is printed with Poly Lactic Acid (PLA) polymer using Fused Filament Fabrication (FFF) technology. Dimensional features and surface roughness of parts are then measured to determine the variability in output for different levels of input. The results of ANOVA analysis are used to conclude about the significant factors and their levels. The ANOVA results show that the response parameters are affected by main effects, 2-way interactions, and 3-way interactions in different combinations. The optimal conditions obtained from ANOVA analysis are used to print some more parts to plot control charts and conduct capability analysis. Control charts are used to monitor the process variability and capability analysis is conducted to check if the process is in statistical control and can produce parts within specifications. The small size of the parts allows the results of this study to be applicable in biomedical and industrial sectors. This study containing three input parameters with three levels each considers main effects along with interaction effects which have not been considered previously in our literature review. Also, the combination of factors is unique, and their effect combined has not been focused in previous studies.

Copyright Owner

Anusha Velineni

Language

en

File Format

application/pdf

File Size

92 pages

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