Research Notes

At the Machining Research Centre of the School of Engineering Systems and Design at London South Bank University (London, England, UK), a project entitled Threading of Aerospace Materials is investigating the effect of thread pitch and helix angle on overall performance when threading aerospace steels using cemented carbide (coated and uncoated) and HSS threading inserts (UNJ type). The effect of cutting speed on surface finish generated, threading accuracy, residual stress/surface abuse, and pulling strength is also considered with a view to identifying the optimum cutting speed, best inserts and threads, as well as suitable cutting fluids. Generic machining data are also used to predict the behavior of threading inserts when machining difficult-to-cut materials using Artificial Neural Networks (ANNs).
At the National Center for Manufacturing Sciences (Ann Arboi; MI) the Springback Predictability Project (SPP) is a spinoff from a completed five-year SPP NIST Advanced Technology Program (ATP) joint venture project seeking to accurately predict springback in metalforming for new high-strength steel and aluminum sheet materials intended for use in future lightweight car programs. A software code designated LS-DYNA (commercialized by LSTC, Livermore, CA), was chosen to demonstrate the project's advances. The objective of the SPP Forum is to further evaluate the ATP developments and share expertise. The forum is aimed at reducing costs and time associated with the current, largely trial-and-error methods for design, construction, and correction of the shapes and tooling.

Program manager for the forum is Manish Mehta. Contact Mehta at 734-995-4938, or at manishm@ncms.org. Or find more information at http://spring-back.ncms.org.

The goal of a program called Visually Guided Grasping and Manipulation that's now underway at the Robotics Group of Columbia University (New York City, NY) is to visually monitor and control the fingers of a robotic hand as it performs grasping and manipulation tasks. The motivation behind the project is the general lack of fast, accurate feedback from most robotic hands. Many grippers lack sensing, particularly at contact points with objects, and rely on open-loop control to perform grasping and manipulation tasks. This research seeks to use vision to provide the compliance and robustness that assembly operations require without the need for extensive analysis of grasping or a detailed knowledge of the environment to control a complex grasping and manipulation task.