IMPACT OF PROCESS PARAMETERS AND DEGRADATION ON PRODUCT QUALITY IN FUSED DEPOSITION MODELING
Date
2024-04-30
Authors
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Publisher
Wayne State University
Abstract
Fused Deposition Modeling (FDM) emerged as a popular additive manufacturing technique,
finding applications across various industries to produce functional parts and industrial
tooling. The key advantage of FDM lies in its ability to fabricate complex and free-form
shapes that are challenging to create using conventional manufacturing methods. However,
the optimal utilization of FDM relies on a comprehensive understanding of how (1)
process parameters and (2) degradation factors influence the product quality metrics such
as dimensional accuracy, mechanical properties, power consumption, fabrication duration,
and material utilization. This constitutes a significant barrier to the industrial adoption of
FDM processes, and limits lifetime of equipment. For this reason, there has been a growing
interest in understanding the process control factors and machine degradation factors that
influence the characteristics of produced parts. To date, state-of-the-models either focus
on a limited number of FDM process parameters or concentrates on a restricted set of response
characteristics related to the key performance indicators (KPIs) of produced parts or reporting a number of monitoring failures techniques for AM processes. In addition,
many of these studies also assume linear relationships between FDM process parameters.
This study presents an investigation of the impact of FDM process parameters (i.e.,
nozzle temperature, infill pattern, infill percentage, build orientation, layer thickness, and
printing speed) on seven key performance indicators (KPIs) of parts produced: (1) energy
consumption, (2) fabrication duration, (3) material used, (4) mechanical properties, and
geometric accuracy (i.e., (5) flatness, (6) thickness deviation, and (7) root mean square
(RMS)); using a comprehensive Design of Experiments methodology to characterize the
relationships across these highly interdependent KPIs. Our experiments use a non-contact
type measurement technique to evaluate the geometric accuracy of the 3D printed parts
by extracting the 3D point cloud using CT scan. We examine the inter- action among
these process parameters factors through a design of experiments approach, considering
the non-linear relationships between FDM process parameters by estimating curvature to
identify any underlying patterns or trends.
In this study, we also present a design of experiments (DOE) framework to investigate
the influence of machine degradation conditions (i.e. nozzle blockages, gear wear) on
FDM printing quality metrics (i.e. material used, mechanical properties, and geometric
accuracy (i.e. flatness and root mean square)). We used an analysis procedure composed
of two steps. The first steps uses parametric methods, specifically the analysis of variance
(ANOVA), to identify which factors and their interactions are significant in influencing
the KPIs and their level of performance. In cases where the assumptions of ANOVA or the
model accuracy are not satisfied, the second step, a non-parametric approach, is employed.
The second step uses association rules analysis, which involves extracting rules based on
predictions made from a random forest model. These methodologies are used to analyze
the data collected from the experiments and obtain meaningful insights without leading
to biased or inaccurate results.
The findings from this study can provide guidance for operators interesting in maintaining
printing quality throughout the lifetime of the machines; by carefully orchestrating
a balance between machine degradation severity and adjustments made to the process
parameters. The proposed approach will extend the lifespan of FDM machines, optimize
production quality, and promote sustainability in additive manufacturing
Description
Keywords
Additive Manufacturing AM Fused Deposition Modeling FDM 3D Point Cloud Central Composite Design DoE Parametric and Non parametric Analysis