以TMCP（Thermo Mechanical Control Process）实验获得的低成本热轧FB（Ferrite/Bainite）钢为研究对象,通过扩孔实验评价FB钢板的延伸凸缘性能,分析显微组织对延伸凸缘性能的影响,并对扩孔过程中FB钢的裂纹扩展行为进行探讨。结果表明：当贝氏体体积分数在19～25%时,热轧FB钢的抗拉强度不低于551 MPa、扩孔率不小于77%,力学性能和延伸凸缘性能综合起来比较良好;裂纹通过孔洞和微裂纹相互扩展、连接的方式进行;当裂纹扩展到硬相的贝氏体时,裂纹沿着铁素体和贝氏体的相界面绕过贝氏体,并切断铁素体继续扩展。
In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.
To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.
