基于改进的模拟退火人工神经网络的冷轧螺纹钢筋质量控制研究  

Based on Improvement of Simulated Annealing Neural Network of Cold-rolled Ribbed Bars Quality Control Study

作　　者：邢邦圣[1] 

机构地区：[1]徐州师范大学机电工程学院,江苏徐州221116

出　　处：《煤矿机械》2011年第1期125-127,共3页Coal Mine Machinery

基　　金：江苏省高校自然科学研究计划项目(08KJB460006);江苏省"青蓝工程"资助(08QLD002);徐州市科技计划项目(XJ09079)

摘　　要：论述了模拟退火算法和改进的模拟退火人工神经网络,利用改进的模拟退火人工神经网络建立了冷轧螺纹钢筋产品抗拉强度、延伸率与其工艺参数之间关系的数学模型,对影响冷轧螺纹钢筋质量的主要工艺参数进行了优化,实验证明使用优化后的工艺参数进行生产,可大大提高产品质量和生产效率,降低生产成本,对企业提高经济效益和社会效益具有重要作用。The simulated annealing algorithm and the improvement of simulated annealing neural network were discussed, simulated annealing neural network established cold-rolled steel rebar product tensile strength and elongation of the relationship between process parameters and mathematical model of cold-rolled ribbed bars, affect the quality of the main process parameters have been optimized, and proved the optimized parameters used for production, the product can greatly improve the quality and production efficiency, reduce the production cost, improve economic efficiency and the social efficiency, plays an important role.

关 键 词：模拟退火 神经网络 螺纹钢筋 

分 类 号：TG156.2[金属学及工艺—热处理] TG142[金属学及工艺—金属学]