Daniel A. Hartman

Experimental defect analysis and force prediction simulation of high weld pitch friction stir welding

Abstract Experimental data for AA 6061-T6 friction stir welded at rotational and travel speeds ranging from 1000 to 5000 rev min−1 and from 290 to 1600 mm min−1 (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.

Misalignment detection and enabling of seam tracking for friction stir welding

Abstract This paper describes a technique for determining the position of a friction stir welding (FSW) tool with respect to the weld seam during welding. Forces are used as a feedback signal, and a general regression neural network is trained to predict offset position given weld forces. Experimental results demonstrate the accuracy of the developed position predictor. This technique is proposed for online misalignment detection or as a position estimator for in-process tracking of the weld seam for FSW and robotic FSW.

Adaptive voltage control of gas tungsten arc welding

An adaptive control based on fuzzy logic has been implemented for Gas Tungsten Arc Welding (GTAW). This adaptive controller eliminates the problems frequently experienced with traditional Automatic Voltage Control (AVC) systems, which do not adequately perform for all operational conditions because of the non-linear relationship between the arc voltage, current and arc length. This paper examines the differences between fuzzy logic control and adaptive fuzzy logic control of gas tungsten arc welding.

Intelligent fusion control throughout varying thermal regions [arc welding]

An intelligent monitoring and control system is presented that regulates the total heat input in order to maintain constant fusion zone geometry under varying thermal regions. The integrity and quality of the final fused joint rely heavily on the geometrical properties of the fusion zone, in particular the width-to-depth ratio. Assessment of the joint geometry is performed using Rayleighs suspended droplet analogy in which the mass, and hence the volume, of the droplet is related to the natural resonant frequency of the molten region. A nonintrusive, noncontact, top-side sensor collects the arc light reflected from the oscillations of the molten metals surface. An improved approach for inducing and monitoring the oscillations of a molten weld pool is presented. A software-based phase locked loop (PLL) technique enables synchronized excitation of the molten pool. Improved locking and tracking characteristics in the presence of noise, signal distortions and harmonic conditions are achieved through the use of intelligent signal monitoring algorithms which co-exist in parallel and cooperate with the PLL. Dynamic reconfiguration of the PLLs digital filter coefficients allows superior tracking performance over a wide range of resonant frequency conditions. A fuzzy logic rule set, modeled after expert human welding knowledge, regulates the total heat input to the fusion process in order to demonstrate this unique synchronous excitation, monitoring and control technique. A detailed discussion regarding each components contribution to the overall system is provided. Finally, an in-depth comparison between various open and closed loop experiments of the joining process is discussed.