journal article·2020-04-22Open access

Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

Ahmed Elkaseer, Stella Schneider, Steffen G. Scholz

Applied Sciences· Volume 10· Issue 8

Abstract

This article reports on the investigation of the effects of process parameters and their interactions on as-built part quality and resource-efficiency of the fused filament fabrication 3D printing process. In particular, the influence of five process parameters: infill percentage, layer thickness, printing speed, printing temperature, and surface inclination angle on dimensional accuracy, surface roughness of the built part, energy consumption, and productivity of the process was examined using Taguchi orthogonal array (L50) design of experiment. The experimental results were analyzed using ANOVA and statistical analysis, and the parameters for optimal responses were identified. Regression models were developed to predict different process responses in terms of the five process parameters experimentally examined in this study. It was found that dimensional accuracy is negatively influenced by high values of layer thickness and printing speed, since thick layers of printed material tend to spread out and high printing speeds hinder accurate deposition of the printed material. In addition, the printing temperature, which regulates the viscosity of the used material, plays a significant role and helps to minimize the dimensional error caused by thick layers and high printing speeds, whereas the surface roughness depends very much on surface inclination angle and layer thickness, which together determine the influence of the staircase effect. Energy consumption and productivity are primarily affected by printing speed and layer thickness, due to their high correlation with build time.
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Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization · Additive Manufacturing Materials and Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany

Faculty of Engineering, Port Said University, Port Fuad 42526, Egypt

Future Manufacturing Research Institute, College of Engineering, Swansea University, Bay Campus, Crymlyn Burrows, Swansea SA1 8EN, UK

Karlsruhe Nano Micro Facility, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

Funding

Helmholtz Research Infrastructure (KNMF) at Karlsruhe Institute of Technology · Helmholtz Research Programme STN (Science and Technology of Nanosystems) at KIT. · Deutsche Forschungsgemeinschaft and open access publishing fund of Karlsruhe Institute of Technology.

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