DETC2005-84802 THE USE OF ARTIFICIAL INTELLIGENCE IN THE MODELLING AND HEAT TREATMENT PARAMETERS IDENTIFICATION FOR ALLOY-STEEL RE-HEATING PROCESS

ABSTRACT This paper describes the work undertaken by the University of Glamorgan and CORUS Rotherham UK to apply artificial neural networks to model the cold alloy-steel bars and the heat treatment parameters with their end-product quality characteristics. Standard multi-layered feed forward artificial neural networks (ANNs) were employed to represent the functional mapping of inputs such as physical dimension, material composition and the parameters of the heat treatment cycles to the Brinell Hardness (HB) and the Ultimate Tensile Strength (UTS). The HB and UTS networks were validated with new data sets and demonstrated a satisfactory level of predictions over a range of conditions. These neural networks were then integrated into a Genetic Algorithm (GA) search strategy to identify the best material characteristics and furnace operating parameters in order that both the HB and UTS values are maximised. The results demonstrated that the hybrid strategy of combining the neural network based models with GA can deliver sensible results

An investigation of the generation of Acoustic Emission from the flow of particulate solids in pipelines

This paper is concerned with the generation of the Acoustic Emission (AE) from particulate flow and an investigation of the potential of implementing AE for flow parameters, namely the solid mass flow rate, particle velocity and size, monitoring. A series of experiments has been conducted to gather AE signals from a laboratory scale single flow-loop pneumatic conveying system. Initially, AE sensors were attached to two steel meshes which were placed with a fixed axial distance in the pipeline to study the generation of the AE and subsequently the possibility of using those generated AE to determine particle velocity in the pipeline. Particle velocities measured from this approach were compared with theoretical predictions. The results indicated that this approach could measure the mean particle velocity with reasonable accuracy. The generation of AE on five different sensor mounting locations was also studied. The results showed that sensors mounted on all those locations were able to respond to changes in the flow parameters. However, only two sensor locations (outer bend and Mesh) were chosen for further investigation. The final experimental results indicated that the AE features, namely Root-Mean-Square (RMS) and energy of the AE, are related to the changes in the flow parameters and good correlations were found. Good correlations between the RMS and energy of the AE with the momentum and kinetic energy of the particles, respectively, were also found. Overall, the studies indicated that features of AE have great potential in gas-solid two phase flow parameter monitoring. However, the studies also show that the applicability of the AE techniques to measure solid mass flow rates in practice would require tedious calibration. © 2013 Elsevier B.V