SBIR Phase II: An artificial intelligence system for autonomous numerical control programming for advanced manufacturing

这项小企业创新研究（SBIR）第二阶段项目的更广泛/商业影响包括提高制造业供应链的效率和生产力，从而促进经济增长，创造就业机会，提高产品质量，减少浪费。该项目还可以通过减轻制造业供应链风险来增强美国的工业基础，这对国家安全至关重要。这项技术可以为学生提供新的学习机会，促进学术界和工业界之间的合作，并推进精密制造的科学知识，从而导致新的人工智能算法和技术的发展，其应用范围超出了制造业。鉴于美国的技术技能差距危机，该解决方案将有助于提高计算机数控机械师的生产率，并激发新进入劳动力市场的人对该领域的更大兴趣，从而朝着解决制造业回流挑战迈出一步。制造业正在向更加自动化的方向转变，这种解决方案可以帮助培训下一代人工智能增强机械师。该解决方案广泛适用于商业、政府和学术界，以及一系列终端市场应用，如航空航天、国防和医疗技术。该SBIR第二阶段项目将产生一个人工智能辅助、自主、数控编程软件的全功能“beta”原型，可以在操作环境中进行测试，并接近商业发射的准备。最终产品将是一个人工智能驱动的软件，嵌入到计算机数控程序员现有的工作流程环境中。该软件将提供加工策略、切削刀具和加工参数，以及铣削、钻孔和车削操作的刀具路径建议。通过向最终用户（即数控程序员）提供这些建议，该产品具有以下潜力：1)缩短新人才的学习曲线，2)减少技能水平之间的可变性程度，3)减少生成计算机数控程序所需的时间/迭代，以及4)增加生成最佳（即最低总体加工成本）程序的概率。该产品具有显著提高现有和新劳动力生产率的潜力，同时也降低了精密加工的非经常性和经常性成本。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.