Set Of Output Patterns Research Articles

Predicting The Wear Resistance Of Wc-Co Coatings Using Neural Networks

In the field of mechanical engineering the prediction of rates of wear has proven to be difficult. The wear process is very complicated and many variables are involved including hardness, toughness, yield strength, fatigue properties, as well as geometry, contact sliding distance, relative velocity, and surface finish. Many theoretical wear equations have been developed, but they always contain factors/parameters which have to be determined by experiments and/or are not widely accepted. Neural networks have been developed in the hope of finding solutions for problems with unknown or complex internal relationships as they can be trained to map a set of input patterns onto a corresponding set of output patterns, by simple exposure to examples of the mapping. After training, the neural network can be used to predict output not encountered in training. In the present work, the abrasive wear resistance of VVC-Co coatings on AISI-D3 tool steel substrates is tested. The High Velocity Oxy Fuel (HVOF) thermal spray technique is used with different substrate pre-heating temperatures to produce coatings having various thicknesses. Wear tests are carried out using a novel test rig designed and built at Dublin City University. Volume loss is recorded at regular intervals for each experiment. Experimental data are used to train a neural network (multi-layer perceptron) using the back-propagation algorithm. The neural network is then used to predict volume loss for experiments not used in training. A comparison with a multi-regressive prediction model is also performed. The results obtained show a 16% average prediction error for the neural network and a 30% average for the multi-regressive model. The lowest prediction errors are 6.5% and 6.7%, respectively. Accuracy is found to be particularly high. It is felt that this new computational device can be useful in several industrial application requiring components wear prediction.

Mutually linked HASPs: A solution for constraint-satisfaction problems by associative processing

A model of a human associative processor (HASP) is applied to a class of constraint-satisfaction problems which can be seen as a formal representation of cognitive tasks such as memory retrieval and interpretation of scenes and sentences. A problem consists of a set of nodes, a set of labels, and relational constraints. A relational constraint specifies a set of compatible node-label pairs, and a solution consists of a set of labels of all nodes which satisfy the constraints. The structure of the problem is represented by the associational structures defined on mutually linked HASPs, where a node corresponds to a single HASP and a solution consists of a set of output patterns from respective HASPs, all of which satisfy the relationships defined between them. Relational structure defined between HASPs are divided into three classes according to the difficulty in solving problems. A series of simulation studies has shown that mutually linked HASPs can solve problems very efficiently by associative and parallel processing capabilities.