The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes an increase in efficiency and productivity in manufacturing supply chains, which can lead to economic growth, job creation, improved product quality, and reduced waste. The project can also enhance the U.S. industrial base, which is critical to national security by mitigating manufacturing supply chain risks. This technology can provide new learning opportunities for students, facilitate increased partnership between academia and industry, and advance scientific knowledge on precision manufacturing, leading to the development of new artificial intelligence algorithms and techniques with applications beyond manufacturing. The solution will be a step towards addressing the challenge of reshoring manufacturing given the technical skills gap crisis in the U.S. by helping increase the productivity of computer numerical control machinists and sparking greater interest in this field among new workforce entrants. The manufacturing landscape is shifting to more automation, and this solution could help train the next generation of artificial intelligence-augmented machinists. This solution has broad applicability across commerce, government, and academia, in a range of end market applications such as aerospace, defense, and MedTech.This SBIR Phase II project will result in a fully functional ?beta? prototype of an artificial intelligence-assisted, autonomous, numerical control programming software that can be tested within an operational environment and be near-ready for commercial launch. The end product will be an artificial intelligence-powered software embedded in the computer numerical control programmers? existing workflow environment. The software will provide machining strategy, cutting tool and machining parameters, and tool path recommendations across milling, drilling, and turning operations. By offering these recommendations to the end user (i.e., the numerical control programmer), the product has the potential to: 1) shorten the learning curve for new talent, 2) reduce the degree of variability across skill levels, 3) reduce the time / iterations needed to generate computer numerical control programs, and to 4) increase the probability of generating optimal (i.e., lowest overall machining cost) programs. The product has the potential to significantly increase productivity of the existing and new workforce, while also reducing the non-recurring and recurring costs for precision machining.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.
