CONCLUSION Machine vision is an efficient sensing method for monitoring the molten pool in the GTAW process. In order to control the welding process of GTAW with a wire filler for the need of the hybrid RP&T technique based on welding and CNC milling, the area of the molten pool was used as feedback to control the GTAW process. The image of the molten pool and its surrounding area was acquired by a CCD camera coaxially integrated with the tungsten electrode. The boundary of the molten pool is extracted alte the image processing. The boundary is incomplete because the molten pool is partially hidden by the electrode. A two-layer neural network is established using MLP to calculate the area of the molten pool with its incomplete boundary. The neural network was trained by back propagation learning with a momentum term using molten pool images acquired for different welding conditions. The testing results by another group of the molten pool images show that the neural network provides a good result. ACKNOWLEDGEMENTS This work was financially supported by THECB, Grant No. 003613-0022001999, NSF Grant DM1-9809198, and by the US Department of Education, Grant No. P200A-80806-98. The authors would like to express the gratitude to Weldware Inc. Columbus, OH for providing free of charge the GTAW torch with coaxial machine vision system for the need of this project. Assistance of Mr. I. S. Kmecko during the experiment is also gratefully acknowledged. REFERENCES 1. Kruth, J. P.; Leu, M. C.; and Nakagawa, T. 1999. Progress in adaptive manufacturing and rapid prototyping. CIRP Annuals 47 (2): 525-540. 2. Beardsley, H. E.; and Kovacevic, R. 1999. New rapid prototyping technique based on 3-D welding. Technical Paper - Society of Manufacturing Engineers. PE, No. PE99-126, SME, Dearborn, MI, USA: PE99-126-1 - PE99-126-6. 3. Spencer, J. D.; Dickens, P. M.; and Wykes, C. M. 1998. Rapid prototyping of metal parts by three-dimensional welding. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 212 (B3): 175-182. 4. Kovacevic, R. 2001. Rapid manufacturing of functional parts based on deposition by welding and 3D laser cladding. Proceedings of the Mold Making 2001 Conference, presented by Mold Making Technology Magazine: 263-276. 5. Richardson, R. W.; Gutow, D. A.; Anderson, R. A.; and Farson, D. F. 1984. Coaxial arc weld pool viewing for process monitoring and control. Welding Journal 63 (3): 43-50. 6. Kovacevic, R.; and Zhang, Y. M. 1995. Machine vision recognition of weld pool in gas tungsten arc welding. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 209 (B2): 141-152. MONITORING STABILITY OF KEYHOLE FORMATION IN VPPAW G. Tao, H. Wang and R. Kovacevic* ABSTRACT There are a number of factors that influence the quality of the weld formation in variable polarity plasma arc welding (VPPAW). Stability control of weld formation in VPPAW is required. One of the control strategies is to apply feedback control. The key for the successful application of feedback control is to acquire a characteristic signal that reflect the status of the controlled object. Airborne sound generated in the keyhole mode of VPPA welding process is used to characterize the presence of the keyhole and its size. Short-time spectrum analysis is used to study the frequency characteristics of the sound signal during the welding process. Short-time spectra are integrated along the frequency domain to obtain the power at different frequency intervals. The power is then used as the input to an artificial neural network (ANN) model to classify the keyhole diameter. KEYWORDS Sound signal, Time-frequency analysis, ANN, VPPAW INTRODUCTION Variable polarity plasma arc welding (VPPAW) is a promising welding technique for joining aluminum alloys. It has been used successfully in production, such as in the fabrication of the space shuttle external tanks (Ref. 1-3). VPPAW can generate high welding quality and high productivity at relatively low cost. However, the keyhole molten pool is very dynamic, and it can collapse and generate burn-through holes during welding, especially when welding plates with thicknesses above 4.0 mm. Thus, the selection of the welding parameters and the implementation of controller to ensure the stability of the weld formation in real-time remain a challenge. Recently, it was found that the presence or absence of a keyhole could be determined by measuring the ratio of hydrogen to argon in the plasma arc column with an optical spectrometer (Ref.4). However, the size of the keyhole cannot be determined and the welding process cannot be distinguished from the cutting process according to that signal. At present, two problems are associated with sensing of the keyhole weld pool in VPPAW: 1) Inaccessibility from the frontside of the weld pool because of the limited torch standoff-distance and the interference produced by the strong arc light. 2) Detection of the keyhole from the back-side of the workpiece is related to the complexity of the weld structure, for instance, in the welding of pressure vessels. Research Center for Advanced Manufacturing, Southern Methodist University 1500 International Pkwy, Suite 100, Richardson, Texas 75081 * corresponding author Tel: 1-214-768-4865, Fax: 1-214-768-0812, Email: kovacevi@seas.smu.edu Previous work shows that keyhole size captured by a machine vision system can be used as a feature signal to the feedback control of the weld formation in VPPAW (Ref.5). However, it is difficult to precisely monitor the keyhole size by machine vision when the keyhole is very small or very large. A new approach to characterize the presence and the size of the keyhole is needed. One of the potential solutions is to relate the signature of the airborne sound signal during welding to the geometrical characters of the molten pool. The acoustic signal has been used successfully in the monitoring of different welding processes. For example, the time-frequency characteristics of airborne signals were investigated in laser welding (Ref.6). It was found that the acoustic spectrum of good-quality, full-penetration welds could be differentiated from the spectra of poor-quality welds, defined as either partially penetrated welds or welds having a gap between the sheets being joined. Welding sound signature in GTAW was also studied to reveal the conditions that generate weld defects (Ref.7). It was found that the sound signature produced by GMAW contained information about the behavior of the arc column, the oscillation of metal in the molten pool, and the metal transfer mode. The analysis of the acoustic signal from arc welding, oxy-flame cutting, and water jet cutting were carried out also (Ref.8). In addition, acoustic emission monitoring of laser beam welding was investigated to detect laser misfiring, loss of power, and improper focus (Ref.9). In this paper, the sound generated in the VPPAW is studied. The investigation is focused on the following: 1) Frequency and time-frequency analyses of the sound signal generated in VPPAW to study the welding status. 2) Establishment of the model to describe the relationship between the sound signal and the keyhole diameter of the weld pool. backside of the workpiece as a reference. The image acquisition and sound signal acquisition is synchronized. The frequency of image acquisition is 2 Hz while the frequency of the sound signal acquisition is 50 kHz. |