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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是通过帮助提高精密机械师的生产力和引发新劳动力进入者对精密制造领域的更大兴趣，解决美国技术技能差距危机下制造业回流的挑战。计算机数字控制（CNC）是对机床操作的计算机自动化管理，是精密制造的关键过程。数控机床编程需要具有广泛和专业技术技能的专业人员。随着贸易学校的毕业生越来越少，CNC编程技术已经成为制造业运营的瓶颈，迫切需要一个从根本上简化和自动化的CNC编程工作流程，以提高精密制造的可及性。该项目旨在开发一种人工智能工具来自动化CNC编程过程，从而帮助缓解CNC编程瓶颈，并在减少制造时间和成本的情况下填补技能缺口。该项目将扩大在精密制造中使用人工智能（AI）进行生成式CNC编程的研究。人工智能有可能自动化CNC编程工作流程中的各个步骤;然而，这项研究仅限于受约束的加工条件，并没有专注于优化程序，从而限制了其在现实世界中的适用性。该项目扩展了这项研究，为CNC编程的最佳刀具和加工参数选择阶段开发了一种商业上可行的解决方案。简而言之，该项目将解决算法开发和优化的技术挑战，以提高刀具和加工参数的准确选择。结果将在CNC机床上进行验证。在第一阶段结束时，将开发一个概念验证解决方案，以自动化CNC钻削和铣削的最佳切削刀具和加工参数选择。该奖项反映了NSF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 impact of this Small Business Innovation Research (SBIR) Phase I project is to address the challenge of reshoring manufacturing given the technical skills gap crisis in the U.S. by helping increase the productivity of precision machinists and sparking greater interest in the field of precision manufacturing among new workforce entrants. Computer Numerical Control (CNC), the computer-automated management of machining tools’ operations, is a key process in precision manufacturing. Programming CNC machines requires professionals with vast and specialized technical skills. With fewer graduates of trade schools, CNC programming know-how has become a bottleneck to manufacturing operations, creating an urgent unmet need for a radically simplified and automated CNC programming workflow to improve accessibility to precision manufacturing. This project aims to develop an artificial intelligence tool to automate the CNC programming process, thereby helping ease the CNC programming bottleneck and fill the skills gap at reduced manufacturing time and costs. This project will expand research on the use of Artificial Intelligence (AI) for generative CNC programming in precision manufacturing. AI has the potential to automate various steps in the CNC programming workflow; however, this research has been limited to constrained machining conditions and has not focused on optimization procedures, thereby limiting its real-world applicability. This project expands this research to develop a commercially viable solution for the optimal cutting tool and machining parameter selection stage of CNC programming. Briefly, this project will address technical challenges of algorithm development and optimization to improve accurate selection of tool and machining parameters. The results will be validated on CNC machines. At the end of Phase I, a proof-of-concept solution will be developed to automate the optimal cutting tool and machining parameters selection for CNC drilling and milling.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.