Career Advancement Award: Force and Positional Data Correlation: A Step Towards Smart Deburring

9313834该项目寻求使用现有自动去毛刺设备中的力和位置数据进行毛刺识别，并预测倒角深度、刀具磨损、零件对齐以及运动学和动态引起的误差。还将研究为可用于自动去毛刺的各种微操作器建立这种关联的可行性。这种关系对微操作器设计的依赖性也将被确定。这项工作的结果将为开发智能去毛刺系统提供必要的关联模型，该系统能够通过在线调整和路径规划实时自适应地响应加工和零件变化。这种方法应该消除或最大限度地减少对视觉反馈的需要，使系统对诸如遮挡和需要事先进行数据处理等问题不那么敏感。将从这项研究中受益的领域是机械加工表面和砂型铸造表面的精加工。

9313834 Wiens The project seeks to use force and position data from avaliable automated deburring devices for burr recognition, and prediction of chamfering depth, tool wear, part alignment, and kinematic and dynamically induced errors. The feasibility of establishing such corrleations for various micromanipulators available for automated deburring will also be investigated. The dependence of such corrlwations on the micromanipulator design will also be determined. The results from the work will provide thenecessary correlation models needed for developing a smart deburring system that is capable of adaptively responding in real time to process and part variations with on line tuning and path planning. The approach should remove or minimize the need for vision feedback, making the system less sensitive to problems such as occlusions and need for prior data processing. Areas that will benefit from the research are finishing of machined surfaces and sand cast surfaces.