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Wayne State University
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
Additive Manufacturing AM Fused Deposition Modeling FDM 3D Point Cloud Central Composite Design DoE Parametric and Non parametric Analysis