Author(s)

Qi Zhu, Ling Shen, Jianjun He^*, Weihua Gui

School of Information Science and Engineering, Central South University, Changsha, China.

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.

Prediction Model, Aluminum Alloy, Case-Based Reasoning, State Transition Algorithm, Aging Furnace

Zhu, Q. , Shen, L. , He, J. and Gui, W. (2017) Temperature Prediction of Aluminum Alloy Work-Pieces in Aging Furnaces Based on Improved Case-Based Reasoning. International Journal of Nonferrous Metallurgy, 6, 47-59. doi: 10.4236/ijnm.2017.64004.