Process modelling and control of pulse gas metal arc welding of aluminum
Posinasetti, Praveen (2007) Process modelling and control of pulse gas metal arc welding of aluminum. PhD thesis, Queensland University of Technology.
|Praveen Posinasetti Thesis (PDF 2MB) |
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Recent developments in materials and material joining [specifically Aluminum and Pulse Gas Metal Arc Welding (GMAW-P) technology] have increased the scope and extent of their areas of application. However, stern market demand for the improved weld quality necessitates the need for automation of the welding processes. As a result, improvements in the process parameter feedback, sensing and control, are necessary to successfully develop the automated control technology for the welding processes. Hence, several aspects of the GMAW-P process have been investigated in this study in order to improve its control techniques.
Welding was conducted on 6XXX aluminium, using 1.2 mm diameter 4047 aluminum electrode and argon shielding gas. An extensive collection of high speed camera pictures were taken over a wide range of pulse parameters and wire feed rates using a xenon shadowgraph setup to improve understanding of the physics of GMAW-P process. Current and voltage signals were recorded concurrently too.
This investigation explores the effects of different process parameters namely pulsing parameters (Peak current (IP), Base Current (IB), Peak time (TP), Base Time (TB)) and wire feed rate on metal transfer phenomena in GMAW-P. Number of drops per pulse, arc length and droplet diameter were measured for aluminium electrodes by high speed videography. The pulsing parameters and wire feed rate were varied to investigate their effect on the metal transfer behaviour. Analysis showed that transition between the different metal transfer modes is strongly influenced by the electrode extension. Lower electrode extension reduced the number of droplets detached per pulse, while at higher electrode extension, spray mode is observed due to increased influence of the resistance heating.
Analysis of the current and voltage signals were correlated with the high speed films. A simple derivative filter was used to detect the sudden changes in voltage difference associated with metal transfer during GMAW-P. The chosen feature for detection is the mean value of the weld current and voltage. A new algorithm for the real time monitoring and classification of different metal transfer modes in GMAW-P has been developed using voltage and current signals. The performance of the algorithm is assessed using experimental data. The results obtained from the algorithm show that it is possible to detect changes in metal transfer modes automatically and on-line.
Arc stability in the GMAW-P has a close relationship with the regularity of metal transfer, which depends on several physical quantities (like voltage, current, materials, etc.) related to the growth and transfer of the metal droplet. Arc state in GMAW-P can be assessed quantitatively in terms of number of drops per pulse, droplet diameter and arc length. In order to assess the arc state in GMAW-P quantitatively, statistical and neural network models for number of drops/pulse, droplet diameter and arc length were developed using different waveform factors extracted from the current waveform of GMAW-P. To validate the models, estimated results were compared to the actual values of the number of drops per pulse, droplet diameter and arc length, observed during several welding conditions.
Determination of stable one drop per pulse (ODPP) parametric zone containing all the combinations of peak current (IP), base current (IB), peak time (TP), and base time (TB) that results in stable operation of GMAW-P, is one of the biggest challenges in GMAW-P. A new parametric model to identify the stable ODPP condition in aluminium which also considers the influence of the background conditions and wire feed has been proposed.
Finally, a synergic control algorithm for GMAW-P process has been proposed. Synergic algorithm proposed in this work uses the sensing and prediction techniques to analyse state of the arc and correct the pulsing parameters for achieving the stable ODPP. First arc state is estimated using the signal processing techniques and statistical methods to detect the occurrence of short circuit, unstable ODPP or multiple drops per pulse (MDPP) in GMAW-P system. If the arc state is not stable ODPP, then parametric model and genetic algorithm (GA) is used to assess the deviation of the existing pulsing parameters from the stable operation of GMAW-P process and automatically adjust pulsing parameters to achieve stable ODPP.
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Yarlagadda, Prasad, Turner, Ian, & Kang, Mun|
|Keywords:||aluminum, GMAW-P, statistical modelling, genetic algorithm|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Department:||Faculty of Built Environment and Engineering|
|Institution:||Queensland University of Technology|
|Copyright Owner:||Copyright Praveen Posinasetti|
|Deposited On:||03 Dec 2008 14:05|
|Last Modified:||29 Oct 2011 05:49|
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