Artificial neural network prediction of mechanical properties of hot rolled low carbon steel strip

Conventionally,direct tensile tests are employed to measure mechanical properties of industrially produced products. In mass production,the cost of sampling and labor is high,which leads to an increase of total production cost and a decrease of production efficiency. The main purpose of this paper is to develop an intelligent program based on artificial neural network(ANN) to predict the mechanical properties of a commercial grade hot rolled low carbon steel strip,SPHC. A neural network model was developed by using 7×5×1 back-propagation(BP)neural network structure to determine the multiple relationships among chemical composition,product process and mechanical properties. Industrial on-line application of the model indicated that prediction results were in good agreement with measured values. It showed that 99.2 % of the products’ tensile strength was accurately predicted within an error margin of ±10 %,compared to measured values. Based on the model,the effects of chemical composition and hot rolling process on mechanical properties were derived and the relative importance of each input parameter was evaluated by sensitivity analysis. All the results demonstrate that the developed ANN models are capable of accurate predictions under real-time industrial conditions. The developed model can be used to substitute mechanical property measurement and therefore reduce cost of production. It can also be used to control and optimize mechanical properties of the investigated steel.