An integrated approach to friction surfacing process optimisation.

Abstract

This paper discusses the procedures for data collection, management and optimisation of the friction surfacing process. Experimental set-up and characteristics of measuring equipment are found to match the requirements for accurate and unbiased data signals. The main friction surfacing parameters are identified and the first stage of the optimisation process is achieved by visually assessing the coatings and introducing the substrate speed vs. force map. The optimum values from this first stage forms a region around the middle of a trapezium-shaped area whose borders are found experimentally. Data collected for the second stage were analysed using the least squares method which were applied to find the coefficients of a second order regression model. Advantages of applying artificial intelligence methods to friction surfacing modelling are also described and the higher accuracy achieved using neural networks demonstrated.