The
temperature of aluminum alloy work-pieces in the aging furnace directly affects
the quality of aluminum alloy products. Since the temperature of aluminum alloy
work-pieces cannot be measured directly, a temperature prediction model based on
improved case-based reasoning (CBR) method is established to realize the online
measurement of the work-pieces temperature. More specifically, the model is
constructed by an advanced case-based reasoning method in which a state
transition algorithm (STA) is firstly used to optimize the weights of feature
attributes. In other words, STA is utilized to find the suitable attribute
weights of the CBR model that can improve the accuracy of the case retrieval
process. Finally, the CBR model based on STA (STCBR) was applied to predict the
temperature of aluminum alloy work-pieces in the aging furnace. The results of
the experiments indicated that the developed model can realize high-accuracy
prediction of work-pieces temperature and it has good application prospects in
the industrial field.
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