人工神经网络在预测斜轧穿孔毛管偏差中的应用

Application of artificial neural networks on predicting deviation of tube in cross piercing process

斜轧穿孔中毛管质量与许多工艺参数 ,如辊型、送进角、顶头前伸量及温度 ,以及设备性能参数如穿孔机刚度、加工精度和顶杆振动等有关。传统的轧制理论难以解决其质量问题 ,应用人工神经网络则能较好地解决毛管质量的预测问题。应用实测的工艺参数与其对应的毛管精度参数 ,训练和学习网络的权值和阈值 ,建立起模拟穿孔机生产的数学模型 ,即网络模型。

The quality of tube hollow in cross piercing process is concerned with complicated factors, such as technical parameters including roller shape, feed angle, plug advance and temperature, and the piercing mill properties including stiffness and precision of the mill manufactured, vibration of the plug and driven systems. It is difficult to solve further problems on qualities using traditional rolling theory, and the prediction of tube hollow qualities is even more difficult. The artificial neural networks were used to solve the above problems easily. Weights and thresholds of the networks were learnt by experimental data and the model has been established in production. Technical parameters optimized and deviation of tube have been predicted.