arc welding(SAW), for example, current variations of approximately 10%at the sidewalls have been observed while welding in a joint consisting of a 45-degree included angle with a 5-mm root opening. with a nominal current of 580 A at the center of the joint, the current at the sidewalls is approximately 640 A. Variations of this magnitude may be used to implement robust control algorithms for joint tracking and width control Shepard [1991] presents a thorough treatment of the mechanisms that establish and influence self-regulation in GMAW. Components of a dynamic GMAw process model are identified, including the power source, joule ceating in the electrode, electrode burn-off rate, and arc voltage. A numerical simulation of the nonlinear dynamic model for self-regulation is implemented, computing current I and electrode extension in response to CTWD, voltage, and feed rate. The I/CTWD response is shown to be frequency dependent, increasing significantly at higher frequencies. The frequency at which the response increases is shown to be primarily dependent on electrode current density, occurring at lower frequencies for lower current densities. A linearized closed-form model for the I/CTWD frequency response is derived from the simulation equations and is shown to provide accurate results. The closed-form model clearly indicates the relationships between the model parameters that establish the observed characteristics of self-regulation dynamics. Initial implementations of through-the-arc seam tracking methods use simple current levels to identify the lateral limits of the weld joint, djusting the torch centerline to maintain symmetry. The dynamic model developed by Shepard provides a asis to infer actual joint geometry from position and current information acquired during cross-seam oscil lation. The relationships developed by Shepard also refine the basis for selection of welding procedures in GMAW applications, particularly for through-the-arc sensing applications. The models define the relationships to generate surfaces to facilitate selection of electrode diameter, feed rate, voltage, electrode extension, and CTWD to optimize desired characteristics such as low-frequency sensitivity, high-frequency sensitivity, and transition frequency subject to requirements on heat input and deposition rate. These interrelationships may be used as extensions to existing expert systems for selection of welding procedures Arc sensing has been proposed as a means of sensing gta pool motion after excitation from pulsations in the current. The concept of using weld pool motion as a pool geometry sensing method is based on the fluid dynamics of the constrained weld pool, which depend on the properties of the molten pool material, the surface tension, and the shape of the pool. a Another potential application of arc sensing is detection of the metal-transfer mode in GMAW. The droplet transfer mode in the GMaw process has a large effect on weld pool metallurgy, influencing penetration, solidification, heat flow, and mass input. Researchers have attempted to correlate perturbations in the electrical arc signals with droplet transfer. This work has demonstrated the ability to detect the detachment of individual droplets and to distinguish among the three transfer modes: globular, spray, and streaming, as defined by Lancaster [1986] Measurements of the incremental arc resistance by Shepard [1991] suggest that the metal-transfer mode of the gas metal arc may also be detected by the rapid transitions of the incremental arc resistance at the transition regions of metal transfer(particularly at the spray-to-streaming transition). The incremental resistance was obtained by perturbing the voltage with a 1-Vp-p 15-Hz sinusoidal variation In the arc resistance measurements, CTWD and electrode extension(and hence arc length) were held constant and data were taken over a wide range of current. A nominal CTWD of 25. 4 mm was used, with a 15-mm electrode extension. Feed rate was varied from the globular/spray transition point to the upper ranges imposed by equipment limitations. A small (1Ve-p), high-frequency(15 Hz) sinusoidal perturbation was superimposed on the power source voltage to allow measurement of the incremental resistance at each operating point. The frequency was sufficiently high that the electrode extension did not vary significantly. For each data point, an 8-s record was acquired at 1 kHz sample rate. The frequency response function(FRF)was used to compute the incremental resistance by calculating the current produced in response to the sinusoidal voltage perturbation. The FrF gives the magni tude and phase angle of a linear model of the arc V-I characteristic about the given operating point, making up the total resistance of arc plus electrode. Results of the incremental arc resistance measurements were plotted as a function of current. The most significant feature of these data was the large peak in incremental resistance in the region of the projected/streaming transition. The height of the peak is roughly twice the nominal resistance at higher currents. The incremental arc resistance increases sharply at the upper end of projected transfer mode, peaking just after the transition to decline to a relatively steady level through the upper end of the streaming transfer range e 2000 by CRC Press LLC© 2000 by CRC Press LLC arc welding (SAW), for example, current variations of approximately 10% at the sidewalls have been observed while welding in a joint consisting of a 45-degree included angle with a 5-mm root opening. With a nominal current of 580 A at the center of the joint, the current at the sidewalls is approximately 640 A. Variations of this magnitude may be used to implement robust control algorithms for joint tracking and width control. Shepard [1991] presents a thorough treatment of the mechanisms that establish and influence self-regulation in GMAW. Components of a dynamic GMAW process model are identified, including the power source, joule heating in the electrode, electrode burn-off rate, and arc voltage. A numerical simulation of the nonlinear dynamic model for self-regulation is implemented, computing current I and electrode extension in response to CTWD, voltage, and feed rate. The I/CTWD response is shown to be frequency dependent, increasing significantly at higher frequencies. The frequency at which the response increases is shown to be primarily dependent on electrode current density, occurring at lower frequencies for lower current densities. A linearized closed-form model for the I/CTWD frequency response is derived from the simulation equations and is shown to provide accurate results. The closed-form model clearly indicates the relationships between the model parameters that establish the observed characteristics of self-regulation dynamics. Initial implementations of through-the-arc seam tracking methods use simple current levels to identify the lateral limits of the weld joint, adjusting the torch centerline to maintain symmetry. The dynamic model developed by Shepard provides a basis to infer actual joint geometry from position and current information acquired during cross-seam oscil￾lation. The relationships developed by Shepard also refine the basis for selection of welding procedures in GMAW applications, particularly for through-the-arc sensing applications. The models define the relationships to generate surfaces to facilitate selection of electrode diameter, feed rate, voltage, electrode extension, and CTWD to optimize desired characteristics such as low-frequency sensitivity, high-frequency sensitivity, and transition frequency subject to requirements on heat input and deposition rate. These interrelationships may be used as extensions to existing expert systems for selection of welding procedures. Arc sensing has been proposed as a means of sensing GTA pool motion after excitation from pulsations in the current. The concept of using weld pool motion as a pool geometry sensing method is based on the fluid dynamics of the constrained weld pool, which depend on the properties of the molten pool material, the surface tension, and the shape of the pool. Another potential application of arc sensing is detection of the metal-transfer mode in GMAW. The droplet transfer mode in the GMAW process has a large effect on weld pool metallurgy, influencing penetration, solidification, heat flow, and mass input. Researchers have attempted to correlate perturbations in the electrical arc signals with droplet transfer. This work has demonstrated the ability to detect the detachment of individual droplets and to distinguish among the three transfer modes: globular, spray, and streaming, as defined by Lancaster [1986]. Measurements of the incremental arc resistance by Shepard [1991] suggest that the metal-transfer mode of the gas metal arc may also be detected by the rapid transitions of the incremental arc resistance at the transition regions of metal transfer (particularly at the spray-to-streaming transition). The incremental resistance was obtained by perturbing the voltage with a 1-Vp-p, 15-Hz sinusoidal variation. In the arc resistance measurements, CTWD and electrode extension (and hence arc length) were held constant and data were taken over a wide range of current. A nominal CTWD of 25.4 mm was used, with a 15-mm electrode extension. Feed rate was varied from the globular/spray transition point to the upper ranges imposed by equipment limitations. A small (1 Vp – p), “high-frequency” (15 Hz) sinusoidal perturbation was superimposed on the power source voltage to allow measurement of the incremental resistance at each operating point. The frequency was sufficiently high that the electrode extension did not vary significantly. For each data point, an 8-s record was acquired at 1- kHz sample rate. The frequency response function (FRF) was used to compute the incremental resistance by calculating the current produced in response to the sinusoidal voltage perturbation. The FRF gives the magni￾tude and phase angle of a linear model of the arc V-I characteristic about the given operating point, making up the total resistance of arc plus electrode. Results of the incremental arc resistance measurements were plotted as a function of current. The most significant feature of these data was the large peak in incremental resistance in the region of the projected/streaming transition. The height of the peak is roughly twice the nominal resistance at higher currents. The incremental arc resistance increases sharply at the upper end of projected transfer mode, peaking just after the transition to decline to a relatively steady level through the upper end of the streaming transfer range