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.

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There is an increasing interest in exploring the use of conductive polymers for protecting metals from corrosion. The corrosion resistance of steel coated with the conductive polymer polypheneylenevinylene (PPV) is studied. The corrodant used is deaerated 1% w/w NaCl(aq). / The PPV coating is deposited by a homemade spinning coater. The effect of substrate microstructure and surface treatment on PPV coating morphology is investigated. The morphology and surface roughness of the coating are measured by Atomic Force Microscopy (AFM). The PPV coating thickness and substrate surface roughness are measured by profilometer. / Open Circuit Potential (OCP), electrochemical impedance spectroscopy (EIS), linear potentiodynamic voltammetry (Tafel) and optical microscopy were used to characterize the structure and properties of the coatings. The correlation between the coating thickness, the surface treatment of the steel substrate prior to coating, and the corrosion behaviour of PPV coated steel was investigated. / Under the condition of our studies, the level of pH in the solution used for corrosion testing has the strongest effect on the corrosion behaviour of PPV coated steel.

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The use of magnesium for degradable implants can fill the need for temporary, load bearing, metallic orthopaedic implants without the risks and expense of further surgeries once the bone has healed. Mg is non toxic and biocompatible, but the corrosion rate in the body is too high. The rate will need to be moderated if these implants are to be made clinically useful. A review of common orthopaedic coatings found that the biomimetic calcium phosphate coating process meets the criteria for a good coating. This process was designed for permanent implants, and its corrosion protection properties were unknown on Mg. The research presented here evaluates and optimizes aspects of the corrosion protection of biomimetic coatings in vitro. To accurately identify the corrosion mechanisms of such coatings, the in vitro behaviour of several common simulated body fluids and buffer systems was evaluated. The deposition of biomimetic coatings on Mg was compared to Ti. The effect of common surface treatments on the deposition, composition, and ultimate corrosion protection was identified in order to understand the corrosion properties of these coatings. Following the results, the biomimetic method was modified to optimize the protection by reducing the defects. The corrosion properties of these modified coatings were assessed in vitro. The limitation of the biomimetic coatings was found to be in all cases sensitive to the defects present in the coating. While these could be minimized, they were not eliminated. This led to unfavourable corrosion properties. To solve this problem, a novel treatment was developed to give the biomimetic coatings self-healing properties. This treatment promoted local repair in the coating at the defects, greatly improving the corrosion properties. The in vitro model was increased in complexity by adding first amino acids, then proteins. The corrosion behaviour of the coatings was compared in these solutions to understand the effects of these molecules. The data gathered will help to build a better model of in vivo corrosion, and allow better prediction of the performance of biomimetic coatings for corrosion resistance.