Sensors: Adaptive Methods for Coordinate Metrology Using Support Vectors

该传感器和传感器网络(Sensors)研究项目的更大目标是为制造中的传感开发坚固、一致和自适应的方法。在这个框架内，主要研究的重点是使用坐标测量机进行谨慎而又准确的特征验证。零件的加工会产生表面误差，这些误差将在特征验证之前使用工艺物理进行量化。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。将基于支持向量机和学习理论的方法应用于零件验证中的抽样和区域确定。研究的重点将是开发和评估使用支持向量机进行样本缩减的搜索方法，以及量化处理过程中产生的误差。随后将采用非线性形式和轮廓计量的方法。将对全部件计量和逆向工程(RE)的扩展进行研究。还将调查拟议的传感概念对表面计量学和摩擦学的适用性。研究将被部署到教室和计算机模块，为更广泛的传播做准备。这项自适应程序研究的成功将使其适用于更广泛的传感领域，但这项研究的主要重点是零件验证的坐标计量学。成功的应用将改善产品和工艺设计以及互换性。结合学习的新一代坐标计量学软件也将产生。本研究将为检测企业制定计量标准，提出改进的解决方案。该项目中嵌入的两个主要概念是基于知识的感知和数学搜索。这些概念可以扩展到其他制造传感应用。将支持向量机引入制造业也有望导致其他应用。在制造业、计量学和运筹学研究/数据挖掘的每个领域，教育和培训都具有巨大的潜力。

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.