It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.
A FEM simulation study was carried out to investigate the influence of the roll speed on the rolling process. Using a commercial FEM code, ABAQUS, a number of cases were studied. The angular velocity of the rigid rolls ranged from 30 to 480 revolutions per minute (r.p.m.) and the initial feeding speed of the plate was kept constant, thus causing a slipping between the plate and the roll surfaces. The results indicate that for an elastic-plastic hardening plate under the same thickness reduction, a higher rotating speed of rolls will help to reduce the roll separating force and the maximum value of the residual stress in the plate. Though this is a purely numerical simulation and more studies are required to look at the thermo-mechanical and frictional aspects, it does pose to be an inspiration for more tests to be carried out.
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