Research On The Temperature Of Energetic Materials In Twin-Screw Extrusion Process

Twin-screw extrusion process is a new kind of energetic materials production process with characteristics of continuity, flexibility and high safety, which has been paid attention by countries all over the world. By contrast, the research of energetic materials in twin-screw extrusion process in our country is far behind the developed countries in Europe and America, which is mainly due to the problems in guaranteeing the intrinsic safety of extrusion process. Therefore the temperature of energetic materials in twin-screw extrusion process has been discussed in this paper.1. The data acquisition platform was built based on Siemens PLC and WinCC, which could be used to monitor the technological parameter such as the temperature and pressure of energetic materials, twin-screw speed, torque, feeding amount, heat and the cooling signal. The data could be exported and saved at the end of the process.2. The temperature model of extruder was analyzed systematically in theory and simulated by Simulink, which has drawn a conclusion that the temperature of energetic materials in extrusion process is fluctuant and rising. Then the experiments with the alternative materials of modified double base propellant were carried out to verify the validity of the theoretical analysis.3. The Empirical Mode Decomposition (EMD) was imported to analyze the inherent link between the temperature of energetic materials and temperature-control mechanism. It has been demonstrated that EMD is suitable for analyzing the temperature signal of extrusion process. The decomposed Intrinsic Mode Functions (IMFs) could be used to trace the temperature fluctuation caused by heating and cooling nicely, and the trend residual could be used to indicate temperature rise caused by shear of screws intuitively.4. The BP neural network model was built to identify the temperature rise system, which of the inputs were twin-screw speed, feeding amount, rheological behavior of materials and the output was temperature rise under different conditions. The temperature rise of materials under unknown extrusion process conditions could be predicted well by the trained network model.