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

小行星9313834 该项目旨在使用来自现有自动去毛刺设备的力和位置数据进行毛刺识别，并预测倒角深度，刀具磨损，零件对齐以及运动和动态引起的误差。 建立这种相关性的各种显微操作器可用于自动去毛刺的可行性也将进行调查。 这种corrlwations上的显微操作器的设计的依赖性也将被确定。 从工作的结果将提供必要的相关模型需要开发一个智能去毛刺系统，能够自适应地响应真实的时间过程和部分的变化与在线调整和路径规划。 该方法应消除或最大限度地减少对视觉反馈的需求，使系统对遮挡等问题不太敏感，并需要事先进行数据处理。 将从研究中受益的领域是机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.