Abstract
50CrVA cold-rolled spring steel strip was used to fabricate the diaphragm of the automotive horn. The material parameters which were taken into account in the design of the diaphragm involve elongation, elastic limit, Young’s modulus, yield strength and tensile strength. The tempering process was carried out in order to enable the diaphragm to possess the excellent mechanical properties, such as high elastic limit, high fatigue strength and perfect stress relaxation resistance. As a nonlinear information processing system, the backpropagation artificial neural network (BPANN) was applied to predict and simulate the relationship between the mechanical properties of the diaphragm and the tempering process parameters. Experimental results show that a BPANN with 3-8-5 architecture is capable of predicting the relationship between the mechanical properties of the diaphragm and the tempering temperature successfully and lays the profound foundations for optimizing the design of the diaphragm. BPANN simulation results show that the tempering temperature ranging from 380 to 420 °C contributes to enhancing the comprehensive mechanical properties of the diaphragm including high Young’s modulus, high elastic limit and high fatigue strength.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call