The complex non-linear nature of problem as well as varying delays, make the use of NN.
Arc furnace: Neural networks for closed loop welding process control: Welding in a manufacturing context is much more complex than the familiar concept of the fusion process for joining materials. Generally, the planning and design begins with a specification of the weld joint geometry and environment and final required characteristics from selection of a suitable power source can be made. In order to make good welds consistently, consideration must be given to mentioning and controlling machine performance continuously.
One of the most impressive industrial applications of neuro-control is the intelligent arc furnace designed by Neural Application Corporation in Coralville. Electric arc furnace uses electric power to melt steel. A typical furnace is 15-30 ft in diameter and melts 55-150 tonnes of steel in about an hour. The amount of scrap power delivered to scrap metal is controlled by positioning three large electrodes each having 12-24 ft in diameter.
Neural networks for closed loop welding process control: Welding in a manufacturing context is much more complex than the familiar concept of the fusion process for joining materials. Generally, the planning and design begins with a specification of the weld joint geometry and environment and final required characteristics from selection of a suitable power source can be made. In order to make good welds consistently, consideration must be given to mentioning and controlling machine performance continuously.
The gas metal arc welding process, (GMAW) formerly called metal inter gas (MIG) welding, is extensively used in metal works industry to weld a variety of ferrous and non-ferrous metals. The process uses a continuous consumable wire electrode. The molten weld paddle is completely covered with a sealed gas, which is also fed through a welding gun. The GMAW process has been widely used as a metal joining technique because of two facts,
a. Its potential for increasing the productivity and quality of the weld,
b. Its use by most arc welding robots.
High performance automated welding offers the potential for excellent consistency, increased level of productivity and reduced cost. To achieve a high level of performance and quality at high gun travel speed, the weld path must be well defined and appropriate welding process variables must be correctly adjusted along the weld path. The multivariate environment suggests the need for an intelligent control system that can evaluate the process and determine the best adjustments and do so in real time. The full potential for an intelligent control system to evaluate and control the welding process goes beyond the shop-floor. Such technology makes it possible to integrate the weldment design and process planning activities more completely.
Arata and colleagues did a series of studies to investigate the characteristics of the welding arc sound. They concluded that welding arc sound is a good information signal to express the welding condition. Yu conducted a set of experiments to study the feasibility of using the welding arc sound as a means of a feedback to control the welding process. The results clearly indicate the excellent repeatability of arc sound signal and characteristic response of various process conditions - the potential for using the sound as the basis of the feedback control. In order to measure the weld condition and control the weld process in real time, a special pattern classification technique is required to classify welding arc sound in order to assess the condition of weld. In past few years, neural networks have been recognised and accepted as a powerful tool for correlating data without making strong assumptions about the problem. One of the most widely used neural network configuration is a multi-layer feed forward network structure, which is normally trained through an optimisation technique such as back-propagation.
Methodology
Architecture: Figure shows the neural network structure adopted in investigation for closed loop welding process control. The first neural network, the signal classification network (SCN) is used to classify the welding arc sound signal in order to assess the quality of weld. The second neural network, feedback control signal (FCN) provide the feedback control signal to adjust and control the welding process.
The closed loop control system starts setting the controllable variables of the welding system. The sound signal corresponding to this setting is then collected. The collected sound signal is fed into SCN (signal classification network). The output of the network indicates the present quality of weld, which is determined by measures: penetration, spatter, weld bead width and weld bead height. After comparing the present weld quality with base line weld quality, a quality difference is obtained and fed into FCN (feedback control network) signal. The FCN, then in turn, provides the feedback control signal to adjust the controllable variables and forms a closed loop control system.
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| Posted : 10/27/2005 |
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