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.

该传感器网络（传感器）研究项目的更大目标是开发坚固，一致和适应性的方法来传感制造。 在此框架内，主要研究的重点是使用坐标测量机进行审慎而又准确的特征验证。 零件的处理会导致表面误差，这些误差将在特征验证之前使用过程物理进行量化。 感应将以零件的几何形状和这些先前的过程模型为指导。 支持向量机（SVM）代表基于统计学习理论的一种新型学习机。 基于SVM的方法和学习理论将应用于在部分验证中整合采样和区域确定。 研究将集中精力开发和评估使用SVM减少样本的搜索方法，并量化处理过程中产生的错误。 非线性形式和轮廓计量学的方法论将随后。 将研究扩展，以进行全零件计量和逆向工程（RE）。 还将研究提出的传感概念对表面计量学和摩擦学的适用性。 研究将部署到准备更广泛传播的教室和计算机模块。 这项研究对自适应程序的成功将使自己用于更广泛的传感领域，但是这项研究的主要重点是坐标是用于零件验证的坐标计量。 成功的应用将改善产品和过程设计和互换性。用于协调学习学习的新一代软件也将导致。 这项研究将导致计量标准，并目前改进了检查企业的解决方案。 该项目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，计量和运营研究/数据挖掘的每个领域的教育和培训中都存在着巨大的潜力。