Sensors: Adaptive Methods for Coordinate Metrology Using Support Vectors

传感器：使用支持向量进行坐标计量的自适应方法

• 批准号: 0427966
• 负责人: Shivakumar Raman
• 金额: --
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427966&HistoricalAwards=false
• 关键词: Sensors; Adaptive; Methods; Coordinate; Metrology

The larger objective of this Sensors and Sensors Networks (Sensors) research project is to develop sturdy, consistent and adaptive methods for sensing in manufacturing. Within this framework, the focus of primary research is in prudent and yet accurate feature verification using coordinate measuring machines. The processing of parts results in surface errors that will be quantified using process physics prior to feature verification. Sensing will be guided by the geometry of the part and these prior process models. Support Vector Machine (SVM) represents a new type of learning machine based on statistical learning theory. Methodologies based on the SVMs and learning theory will be applied to integrate sampling and zone determination in part verification. Research will focus efforts on developing and evaluating search methods for sample reduction using SVM, and on quantifying errors generated during processing. Methodologies for non-linear forms and profile metrology will follow. Extensions will be investigated for full-part metrology and Reverse Engineering (RE). Suitability of proposed sensing concepts to surface metrology and tribology will also be investigated. Research will be deployed to classrooms and computer modules prepared for wider dissemination. The success of this research for an adaptive procedure will lend itself for use to a wider domain of sensing, but the principal focus for this research is in coordinate metrology for part verification. Successful application will improve product and process designs and interchangeability. New generation software for coordinate metrology incorporating learning will also result. This research will lead to metrology standards as well as present improved solutions to the inspection enterprise. The two principal concepts embedded in this project are knowledge-based sensing and mathematical search. These concepts can be extended to other manufacturing sensing applications. Introduction of SVMs to manufacturing is also expected to lead to other applications. Significant potential exists in education and training in each area of manufacturing, metrology, and operations research/data mining.

这个传感器和传感器网络（传感器）研究项目的更大目标是开发坚固，一致和自适应的制造传感方法。 在此框架内，主要研究的重点是谨慎，但准确的功能验证使用坐标测量机。 零件的加工会导致表面误差，在特征验证之前，将使用工艺物理学对这些误差进行量化。 传感将由零件的几何形状和这些先前的工艺模型来指导。 支持向量机是一种基于统计学习理论的新型学习机。 基于支持向量机和学习理论的方法将被应用于集成采样和区域确定的部分验证。 研究工作将集中于开发和评估使用SVM减少样本的搜索方法，以及量化处理过程中产生的错误。 非线性形状和轮廓计量的方法将遵循。 将研究全部件计量和逆向工程（RE）的扩展。 还将研究拟议的传感概念对表面计量学和摩擦学的适用性。 研究将部署到教室，并准备计算机模块供更广泛地传播。 这项研究的成功的自适应程序将有助于使用更广泛的领域的传感，但这项研究的主要重点是在坐标计量的部分验证。 成功的应用将改善产品和工艺设计以及可重复性。新一代的软件，坐标计量纳入学习也将产生。 这项研究将导致计量标准，以及目前改进的解决方案，以检查企业。 在这个项目中嵌入的两个主要概念是基于知识的传感和数学搜索。 这些概念可以扩展到其他制造传感应用。 将SVM引入制造业也有望导致其他应用。 在制造、计量和业务研究/数据挖掘的每个领域的教育和培训方面都存在巨大的潜力。