【摘要】 建筑钢结构不同管材的弯曲曲率变化较大,施工时需制作大量胎架,即费时费料又占用场地,故设计了一种冷弯机结构,基于该冷弯机,利用Midas7.8软件对建筑钢结构用管材进行冷弯成形模拟,基于神经网络建立3个输入参数的回弹量数据模型,选择1 000组模拟数据作为训练数据训练神经网络,500组模拟数据作为测试数据测试网络,将预测结果和样本结果进行比较和分析,结果表明,所建立的神经网络预测模型满足误差要求,可以用来预测大管径厚管壁管材冷弯成形后的回弹量。该项研究为开发具有自适应回弹量补偿性能的数控弯管系统提供理论基础。更多还原

【Abstract】 Construction steel tubes curvatures differ greatly,when constructing,need to make a lot of tire racks,time and material consuming and space occupied. Design a tube bending structure,based on this structure,with Midas7. 8 software,simulate and analyze steel tube cold roll forming,set up springback data model with three input parameters based on neural network,select 1 000 set of simulated data as training data to train the neural network,500 sets of analog data as the test data to test the network,predicted results and the sample results are compared and analyzed,the results showed that nerve network prediction model meet the error requirement,it can be used to predict springback of large diameter thick wall tubes after roll forming. This research provides a theoretical basis for the development of the adaptive the CNC tube bending with springback compensation performance.更多还原