The goal is to develop and demonstrate a physics-based, artificial intelligence (AI) empowered modeling and optimization software tool for the Air Logistics Complexes (ALCs) and U.S. Air Force (USAF) suppliers to successfully machine additively manufactured non-flight critical metal parts. Post-process machining is required to create precision features and mating surfaces, but the machining process is sensitive to material variations that could lead to unacceptable parts that must be scrapped or reworked. This SBIR will enhance commercially available machining optimization technology to meet the requirements of the ALCs to support their increasing use of metal additive manufacturing. This will reduce machinist planning time to make the first part correct, reducing part scrap and rework, to achieve drastic reductions in the time an aircraft spends waiting for one part so that it can be returned to service. In Phase I, we will identify and engage with the end-users at the ALCs regarding non-flight critical metal replacement parts and to get customer buy-in. Next, a feasibility study will be conducted using commercial machining finite element analysis software to demonstrate the sensitivity of machining to additive manufacturing material property variability. Finally, we will create the business case and development plan for the subsequent phase.
