Study of dislocations from continuous flattening anneal and its effect on magnetic properties of grain oriented electrical steel

Deformation mechanism and dislocation dynamics in grain oriented electrical steel (GOES) is not well established during the continuous flattening anneal process. This work deals with the study of deformation mechanisms during the process and the effect of lattice defects created during the process on the final magnetic properties of GOES. A heat transfer model of the continuous flattening anneal furnace was developed to calculate the temperature profile of the strip throughout the process. The heat transfer model showed the stability of peak strip temperature at 850oC when the line speed was varied from 60-90m/min. A deformation mechanism map was constructed for two varieties of GOES. The main implication of this model is the knowledge of the effect of process parameters like stress, temperature and strain rate on the formation of dislocation structure in GOES during continuous flattening anneal process. LAFFAS (Lab Annealing Furnace for Flattening Anneal Simulation) was constructed to simulate the continuous flattening anneal process and produce samples for dislocation study and magnetic testing. The high temperature mechanical behaviour of Conventional Grain Oriented+ ® (CGO+ - new GOES product) and its effect on magnetic properties were also analysed. Polygonization in GOES and the factors affecting polygonization were studied in detail. Initial orientation of the grains was determined to be a rate controlling factor for degree of polygonization along with temperature and annealing time. The degree of polygonization was shown to be an important parameter affecting the change in domain width. A localised decrease in specific total loss was observed at 1.5T and 50Hz in grains where polygonization was complete. An increase in specific total loss of about 10-35% at 1.5T and 50Hz in bulk polycrystalline material was observed due to the sensitivity of polygonization to initial texture resulting in incomplete polygonization in a high percentage of grains

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals