SBIR Phase I: Artificial Intelligence (AI)-Aided Part Identification for Coordinate Measuring Machines

SBIR 第一阶段：三坐标测量机的人工智能 (AI) 辅助零件识别

• 批准号: 2222967
• 负责人: BING ZHAO
• 金额: $ 27.45万
• 依托单位: OPTIC FRINGE CORP.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222967&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; AI

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a new generation of smart machines used in the measurement of parts and assemblies. The team has demonstrated that this technology can convert existing coordinate measuring machines to self-driving autonomous machines. The ability to automatically measure parts is an important feedback link in the process chain that will enable fully automated manufacturing of the future. Specifically, this automation will reduce the specialized skill required to use a Coordinate Measuring Machine (CMM). The innovation will enable workers to operate a CMM and get a precise part measurement. This device is especially helpful as the skilled manufacturing/metrology workforce is retiring as it gives new employees the ability to provide accurate information with little/no training. This innovation also gives the manufacturing companies an option to buy a new machine or upgrade their existing coordinate measuring machine. While the focus of this proposal is part identification, this technology has ready applications in Computer Numerical Control (CNC) machining, robotics, and automated assembly lines. This capability will make the US manufacturing sector stronger and more technologically advanced.The objective of this proposal is to develop a new technology to identify machined parts and assemblies. This technology will be implemented on coordinate measuring machines (CMM), which are used widely in the manufacturing sector to measure the shape and size of parts. The proposed technology will enable autonomous measurements of parts allowing a higher level of automation. In this identification technology, the team will use live images from a camera, multiple solid model/Computer Aided Design (CAD)-generated images, and advanced image processing. Applying Artificial Intelligence (AI)/Machine Learning (ML) to the image processing of part images will ensure correct part identification. Correct identification of parts as seen by the camera is the remaining unsolved challenge to achieving self-driven automatic measurements of parts. Most machined parts are textureless and most of the information is contained in the edges. Current image processing techniques work well with texture-rich parts but are unreliable with textureless machined parts. AI/ML enhanced image processing using edge and shape information is a promising approach, solving this problem will lead to the birth of a new generation of CMMs that can measure parts automatically.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响是开发用于测量零件和装配的新一代智能机器。该团队已经证明，这项技术可以将现有的坐标测量机转换为自动驾驶的自动机器。自动测量零件的能力是工艺链中重要的反馈环节，将使未来的全自动制造成为可能。具体来说，这种自动化将减少使用坐标测量机（CMM）所需的专业技能。这项创新将使工人能够操作CMM并获得精确的零件测量。在熟练的制造/计量劳动力即将退休的情况下，该设备特别有帮助，因为它使新员工能够在很少/没有培训的情况下提供准确的信息。这一创新也为制造公司提供了购买新机器或升级现有坐标测量机的选择。虽然该提案的重点是零件识别，但该技术已在计算机数控（CNC）加工，机器人和自动装配线中得到应用。这一能力将使美国制造业更强大，技术更先进。本提案的目标是开发一种新技术，以识别机加工零件和组件。该技术将在坐标测量机（CMM）上实施，坐标测量机广泛用于制造业测量零件的形状和尺寸。拟议的技术将实现零件的自主测量，从而实现更高水平的自动化。在这种识别技术中，该团队将使用来自相机的实时图像，多个实体模型/计算机辅助设计（CAD）生成的图像以及高级图像处理。将人工智能（AI）/机器学习（ML）应用于零件图像的图像处理将确保正确的零件识别。正确识别相机所看到的零件是实现零件自驱动自动测量的剩余未解决的挑战。大多数加工零件是无纹理的，大部分信息包含在边缘中。当前的图像处理技术对于纹理丰富的零件工作良好，但是对于无纹理的加工零件是不可靠的。利用边缘和形状信息的AI/ML增强图像处理是一种很有前途的方法，解决这一问题将导致新一代可自动测量零件的坐标测量机的诞生。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。