Friction Stir Welding (FSW) is a relatively new welding technique where metals are joined through mechanical stirring. Due to its numerous advantages over older welding methods, it has been implemented in an increasing number of industries. However, there are remaining challenges to be overcome in FSW. One of the most serious is its reliance on accurate weld parameters. Additionally, faults or poor quality welds can develop from problems not easily detectible by an operator or robotic welder. In our work, we pursue automatic means of detecting fault occurrences and other quality problems. Force signals are collected from control welds run in aluminum as well as welds containing gap faults. Signal processing techniques, specifically Support Vector Machines (SVMs), are then used to correctly detect fault occurrences. Results demonstrate the ability for in-process fault detection of FSW.
Experimental data for AA 6061-T6 friction stir welded at rotational and travel speeds ranging from 1000 to 5000 rev min 21 and from 290 to 1600 mm min 21 (11-63 ipm) are presented. The present paper examines the forces and torques during friction stir welding (FSW) with respect to mechanistic defect development owing to process parameter variation. Two types of defects are observed: wormholes and weld deformation in the form of significant excess flash material. A 3D numerical model, implemented using the computational fluids dynamics package Fluent, is used to simulate and investigate the parametric relationship of the forces and torques during FSW. In order to establish a mechanistic quantification of the FSW process, two mechanical models, the Couette and the viscoplastic fluid flow models, were simulated and compared with experimental data for AA 6061-T6.
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