The Fused Deposition Modeling (FDM) process is a bead deposition based additive manufacturing (AM) process that builds a product from thin layers of molten thermoplastic filaments. The ongoing goal of this research is to develop methodologies for designing and fabricating large complex parts such as complex beta testing prototypes, or sand casting patterns. The unique capabilities of the FDM process are leveraged when designing components and assemblies. Complex geometry can be readily manufactured allowing designers to incorporate non-standard component features, and consider unique solutions; however, there are size, surface finish, and accuracy limitations. Rules are developed to leverage the process characteristics and address the observed limitations. Case studies are presented to highlight the benefits and challenges when using the FDM process.

Developing a bead shape to process parameter model is challenging due to the multiparameter, nonlinear, and dynamic nature of the laser cladding (LC) environment. This introduces unique predictive modeling challenges for both single bead and overlapping bead configurations. It is essential to develop predictive models for both as the boundary conditions for overlapping beads are different from a single bead configuration. A single bead model provides insight with respect to the process characteristics. An overlapping model is relevant for process planning and travel path generation for surface cladding operations. Complementing the modeling challenges is the development of a framework and methodologies to minimize experimental data collection while maximizing the goodness of fit for the predictive models for additional experimentation and modeling. To facilitate this, it is important to understand the key process parameters, the predictive model methodologies, and data structures. Two modeling methods are employed to develop predictive models: analysis of variance (ANOVA), and a generalized reduced gradient (GRG) approach. To assist with process parameter solutions and to provide an initial value for nonlinear model seeding, data clustering is performed to identify characteristic bead shape families. This research illustrates good predictive models can be generated using multiple approaches.