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Joint tracking in Friction Stir Welding Paul Fleming Vanderbilt University Welding Automation Laboratory. Introduction. This research presents methods for monitoring of tool alignment relative to the joint-seam in Friction Stir Welding
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Joint tracking in Friction Stir Welding Paul Fleming Vanderbilt University Welding Automation Laboratory
Introduction • This research presents methods for • monitoring of tool alignment relative to the joint-seam in Friction Stir Welding • techniques for implementing automatic seam-tracking for Friction Stir Welding
Friction stir welding • Material joined by a rotating tool which traverses along joint line • Joint types include: lap, T and butt R. S. Mishra and Z. Y. Ma, Materials Science & Engineering R-Reports, 2005, 50(1-2), III 78.
Goal of this research • Develop system capable of detecting the lateral position of the FSW with respect to a desired position such as centered about the weld seam • Develop system which utilizes above estimator in a feedback control system in order to maintain a desired lateral position or alignment • This is “Through the Tool Tracking” (TTT) • Patent pending serial number 12/130,622
Lateral position of FSW tool • Lateral position refers to the location of the FSW tool relative to a desired position or path, such as the joint seam. • Effects of misalignment vary between joint types
Purpose of research • In-system quality check • misalignment can cause a number of quality flaws and in some joint-types (such as blind T-joints) it may not be possible to determine lateral position by visual inspection • Seam tracking • automated seam-tracking of linear and non-linear weld seams.
Force as a feedback signal • Forces collected during the weld are used as the feedback signal to determine lateral position • Force signals have already been used in FSW: • Discover metallurgical defects • Detect gaps in sample fit-up • Implement load-control • Estimate tensile strength
Experimental Case: Blind T-Joints • Experiment to determine ability to predict lateral offset by collected force signals • 30 welds are run with a varying lateral alignment • Forces (X,Y,Z and Mz) are recorded throughout each weld
Results: Collected Forces • Examination of recorded forces indicate that the development of lateral position estimator is very likely possible • Attempt to implement position estimator using machine learning techniques, treat forces as input data and known lateral position as target
Position estimation • Estimator which can predict offset position given gathered forces is desired • Many possible choices: linear or non-linear regression, regression tree, SVM • General regression neural network chosen
Neural Networks • Neural networks are non-linear statistical data modeling tools. • They can be used for classification and regression problems http://en.wikipedia.org/wiki/Image:Artificial_neural_network.svg#file
General Regression Neural Network • GRNN is an artificial neural network which estimates continuous variables using probability density functions • Converges to conditional mean regression surface D. F. Specht, IEEE transactions on neural networks, 1991, 2(6), 568 - 576
GRNN performance Predicted Offset Position Actual Offset Position
Continuous monitoring of weld • First learned the GRNN using training data • Then applied GRNN to new weld runs where the lateral offset was changed several times throughout each weld
Monitoring lateral position over time Void Free Region
Research into Monitoring Capabilities • Presented research demonstrates effectiveness of technique for determining lateral position in T-joints • Current research seeks to apply the same technique to lap-joints
Actuator Control Signals Estimated Lateral Position Force Data Using system for on-line tracking • The system as described could be used for quality monitoring of an FSW process • Additionally, the system could be used as a lateral position estimator in an FSW seam-tracking system FSW PLANT Lateral Position Estimator Controller
On-line seam-tracking • The system is envisioned in two-varieties • 1st case: assume it is possible for an estimator block to be developed which can determine the absolute lateral position. • a controller maintains the desired offset throughout the weld • 2nd case: a signal is maximized at a certain position (such as the axial force in this experiment around the centered position). • the system weaves back and forth to gain the center position.
Incorporating load control • Axial force control is a component of some FSW systems. • The seam tracking system, which uses force as its input signal, could be made to include load control by operating in two alternating stages: • Use seam-tracking to move tool to desired location • Use load control to obtain desired axial force at known location
Future research • Future research for both monitoring and control • Monitoring: • Improving the offset monitoring system and applying it to more joint types • Tracking: • Developing and testing systems which automatically track linear and non-linear weld seams
Thank you Questions?