SBIR Phase I: Computational Synthesis of 3D Printed Composite and Infill Layouts

This Small Business Innovation Research (SBIR) Phase I project expedites the growth of additive manufacturing for end-use parts through automated design software. Additive manufacturing has emerged as an appealing alternative to traditional subtractive manufacturing to fabricate end-use parts with tailored stiffness and strength. A critical production stage required to unlock the potential of additive manufacturing for end-use parts is the design process, which currently requires extensive engineering experience and high engineering design time. The new technology will allow a paradigm shift from the current slow, tedious, and failure-prone design process to an automated design process. The algorithm utilizes high-performance computing and designs components based on strength, specific material properties, and manufacturing constraints. The technology is expected to reduce engineering design time and, as a result, the production cost and time, which will enable the industry to scale production. Additionally, by using the automated design process, the material distribution can be tailored to achieve the desired structural responses, and lightweight structures can be fabricated. The reduction in weight results in a reduction in fuel consumption in aviation and auto industries, which will provide both ecological and economic benefits. This Small Business Innovation Research (SBIR) Phase I project advances the state of the art by (a) developing novel approaches to optimize layout, fiber paths, and plastic infill distribution, (b) generating additive manufacturing toolpaths based on structural performance and efficiency, and (c) implementing multiple failure criteria to understand failure loads in composite additive manufacturing. Due to the significant cost difference between continuous fiber filament and plastic infill, it is crucial to consider the design of fiber paths, carbon fiber reinforced regions, and plastic infill layout. Another challenge is that current design processes do not include a single failure criterion that can predict failure under different loading scenarios. To address these two challenges, the team is investigating a stiffness and strength-based topology optimization for composite additive manufacturing that will enable the design of the geometric layout and toolpath for 3D printing simultaneously. Anisotropic material properties for stiffness and strength in different directions are implemented to utilize the full potential of composite parts. Toolpath constraints such as curvature, minimum length, and width are also implemented in the optimization process to prevent print failure. Finally, an intelligent slicing program will be developed to control the movement of the 3D printer nozzle and eliminate part failure due to stress concentration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.