金属表面的激光强化及强化类别的BP神经网络控制研究  

Research on controlling method for different classifications of laser surface strengthening process by using artificial neural network

作　　者：符永宏[1] 王大承[2] 袁润[1] 蔡兰[1] 张永康[1] 

机构地区：[1]江苏大学机械工程学院,镇江212301 [2]五邑大学机电工程系,广东江门529020

出　　处：《农业工程学报》2004年第5期82-85,共4页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省自然科学基金资助项目(32364);江苏省高校自然科学研究计划重点项目(03KJA460020)

摘　　要：通过试验分析,表明使用不同的激光工作参数,对金属材料进行激光强化处理,可使材料表面产生4种结果,即:未相变硬化、相变硬化、表面微熔及表面熔凝。建立了激光工艺参数与材料表面强化结果之间关系的BP神经网络模型,并应用该模型,对常用于制造农业机械和发动机齿轮、凸轮轴、链轮、曲轴等零件的材料HT300进行激光强化处理试验。结果表明,BP神经网络模型可方便、准确地选择激光工艺参数,控制材料表面强化类别及工作性能。Experiments show that metal surface properties can be more or less modified by laser surface strengthening treatment. In this paper four different strengthening classifications of structure and characteristic of phase layer: non-transformation hardening, transformation hardening, shallow melting and melting were analyzed and the relationship between the four strengthening classifications and laser processing parameters: laser power(P), laser processing beam diameter(D), laser scanning velocity(v) were established by using BP neural network. HT300, as a kind of main high strength cast iron, was widely used for making gears, camshafts, chain wheel, etc. The study results, using HT300 as experimental material, show that laser processing parameters can be chosen conveniently and material surface quality is controlled effectively.

关 键 词：激光表面强化处理 表面强化类别 BP神经网络 控制 

分 类 号：TG156.99[金属学及工艺—热处理] TP183[金属学及工艺—金属学]