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刘立鹏, 王伟, 董培欣, 魏艳红. 基于遗传神经网络的焊接接头力学性能预测系统[J]. 焊接学报, 2011, (7): 105-108.
引用本文: 刘立鹏, 王伟, 董培欣, 魏艳红. 基于遗传神经网络的焊接接头力学性能预测系统[J]. 焊接学报, 2011, (7): 105-108.
LIU Lipeng, WANG Wei, DONG Peixin, WEI Yanhong. Mechanical properties predication system for welded joints based on neural network optimized by genetic algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (7): 105-108.
Citation: LIU Lipeng, WANG Wei, DONG Peixin, WEI Yanhong. Mechanical properties predication system for welded joints based on neural network optimized by genetic algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (7): 105-108.

基于遗传神经网络的焊接接头力学性能预测系统

Mechanical properties predication system for welded joints based on neural network optimized by genetic algorithm

  • 摘要: 文中广泛收集和整理企业第一线的焊接工艺和焊接接头力学性能数据,并建立起相关数据库.应用遗传算法优化BP神经网络,建立焊接接头力学性能预测模型,实现碳钢、低合金高强钢以及不锈钢的抗拉强度、屈服强度、断后伸长率以及断面收缩率等力学性能指标预测.结果表明,材料成分和焊接工艺为影响接头力学性能的主要参数,应用遗传算法优化BP神经网络建立焊接头力学性能预测模型,可以达到较理想的预测精度.

     

    Abstract: In this paper, A large number of production line data was collectd and collated on welding technology and mechanical properties of welded joints. A relevant database was established based on these data, then BP neural networks optimized by genetic algorithm was used to mock up these mechanical properties of welding joint for prediction. By which mechanical properties of tensile strength, yield strength and elongation of carbon steel, low alloy high strength steel and stainless steel can be predicted. The results show that the material composition and welding technology as the main parameters of joints can be determined by this system and staisfied prediction accuracy can be obtained as well.

     

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