The friction surfacing process is a derivative of friction welding and retains all the benefits of that welding process - solid phase, forged microstructures and excellent metallurgical bonds. This work is aimed at the development of mathematical and statistical models for the optimisation of the significant process parameters in order to allow rapid development of new applications using standard CNC equipment. Also the possibility of implementing real-time control systems have been investigated and developed. A friction surfacing database has been configured to allow continuos recording and storage of the useful machine outputs. Later, an infrared pyrometer and thermocouples have also been connected to the data acquisition set-up establishing fully automated information flow from the process. A conversion procedure has been developed to ensure that the experimental results are applicable in industrial environments. Response surface map and the method of visual optimisation have been developed. They are an essential part of the methodology for experimental optimisation of the friction surfacing process. The problem of modelling and optimisation has also been approached using accurate statistical methods. Artificial intelligence in the form of neural networks has been used to improve the accuracy of the derived friction surfacing analytical relationships. For the first time dynamic study of the process has been carried out and CARIMA models have been derived using a modified version of the recursive least squares, to ensure high sensitivity and stability of the identification procedure. New conversion technique has been developed, allowing the use of existing models for materials that have not been used for friction surfacing before, reducing significantly the number of experiments. The idea of using indicator parameters has been introduced for the first time in this research. Such parameters are the force, the torque and the interface temperature and they can be measured on-line. It has been shown that variations of these parameters reflect in the quality of the coating characteristics that cannot be measured on-line. Real-time control has also been considered. An algorithm involving fuzzy logic and self-tuning extremum controller has been developed to continuously monitor and compensate in real-time against the variations in the coating characteristics, and respectively in the indicator parameters. The proposed methodology has been used to design a control system that is capable of maintaining optimal process characteristics. The value of this work is also in reducing the lead-time and hence the cost for determining the optimum parameters for a given coating material on a given substrate geometry. This is an important feature when developing new applications for the friction surfacing process. On the basis of this research a range of new commercial applications have emerged including the manufacture of machine knives for the food, pharmaceutical and packaging industries, repair of car engine valve seats, turbine blades, reclamation of shafts, etc.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:326995 |
| Date | January 2000 |
| Creators | Voutchkov, Ivan I. |
| Publisher | University of Portsmouth |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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