The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to extend advanced manufacturing beyond prototyping to enable higher productivity throughput in digital-to-physical processes. Combinatorial algorithms, including machine learning, manage both the digital thread and the physical creation workflow of direct digital manufacturing applications, while retaining open-ended (change-friendly) design evolution and agile part creation. From entrepreneurial activities to military applications, the dynamic nature of customer/mission needs requires quick reaction in both design and end-product roll-out. Synchronizing the rapid design process with an agile manufacturing process is required to minimize response lags. The algorithms developed in this SBIR project solve the end-to-end communication inconsistencies across the digital-to-physical threshold while increasing productivity and reducing the negative impact of costly unplanned downtime. The desired result of this SBIR project is to enhance throughput and deliver the needed logistics to intelligent advanced manufacturing. This Small Business Innovation Research (SBIR) Phase I project aims to validate the combinatorial algorithm solving the inventory management and production queueing needed to ensure that the digital design evolution and on-going production via additive manufacturing remain in synchronicity. Rapid manufacturing is achieved when the counter-intuitive part creation queueing is adequately solved so activities downstream of the point of fabrication, like assembly and integration, occurs in sync with the long lead fabrication of larger parts. Essentially, workers downstream remain productive as longer lead parts are being fabricated/printed. However, the problem is compounded when the design of the product is open-ended and creates ongoing disruption to the production queueing and downstream post assembly/integration. The dynamic impact of open-ended design evolution on physical fabrication evolution creates significant management difficulties; therefore, the combinatorial algorithm and its machine learning capability is the key enabler to creating smart manufacturing. 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.
