Analysis, Modeling, and Prediction of the Thermal and Mechanical Behavior of Polylactic Acid under Fused Deposition Modeling

Fused Deposition Modeling (FDM) is an extrusion based additive manufacturing methodology. During the manufacturing process, a thread of thermoplastic material is melted through the extruder and solidified on the building platform to form a specific shape. Affordability and feasibility promote the development of FDM technology, nevertheless, the product quality problem hinders the future growth of this advanced manufacturing technique. Therefore, the focus of this dissertation is to realize FDM product and process development by establishing the relationship between manufacturing conditions and product quality and seeking an approach to optimize the process conditions with the lowest cost. To accomplish that, a hybrid experimental/numerical approach is proposed to model, predict, and optimize the thermal and mechanical behavior of the FDM process and the manufactured product. The proposed hybrid model had three major components: experimental, numerical, and prediction models. For the investigation of thermal behavior, both experimental and numerical models were used to analyze how extrusion temperature, platform temperature, printing speed and layer thickness affect the cooling time of the filament during the manufacturing process. After the accuracy of the numerical model was validated, a prediction model was developed to predict the dimensional accuracy and the residual stress of the fabricated part. For the investigation of mechanical behavior, experimental and numerical models were used to examine how the infill topology impacts the modulus of elasticity for several FDM products. Then a prediction model was developed to predict the tensile behavior of parts given filament structure settings. For investigating process optimization, the numerical model provides an approximate representation of the original optimization problem. Then, the approximate solution can be iteratively updated by evaluation using the experimental model which is more expensive, but also more accurate. This process allows an optimum condition be predicted. The investigation of thermal behavior revealed that reducing extrusion temperature, slowing printing speed, and decreasing layer thickness could help lessen the vertical distortion and residual thermal stress, while the high platform temperature might have opposing effects on deformation and residual stress. The results from mechanical behavior analysis revealed that minimize the air gap, and triangular infill pattern would be beneficial to UTS/weight ratio. In addition, the finite element model developed in this study could be used to predict the product breakage location under high load, facilitated the redesign process to increase the strength of the products. Finally, it is demonstrated the optimization algorithm developed in this study is superior to traditional optimization algorithms in the area of additive manufacturing applications, reduced the cost by at least 72.4% when compared with experimental-only method, and costs less than half of the fellow surrogate-based method. The future directions of this study would be focused on increasing the accuracy of the predictive model and reduce the computation cost of the optimization algorithm