锻压技术

基于TS模糊神经网络和田口法的热径向锻造端部凹坑预测

英文标题：Prediction of end surface concave in hot radial forging process based on Taguchi method and T-S FNN

单位：（1.上海交通大学国家模具CAD工程研究中心上海 200030 2.甘肃省金属塑性成形装备智能控制重点实验室甘肃兰州 730314 3.甘肃省大型快锻液压设备工程技术研究中心甘肃兰州 730314 4.兰州兰石能源装备工程研究院有限公司甘肃兰州 730314）

关键词：径向锻造不均匀变形田口法 T-S模糊神经网络端部凹坑

分类号：TG316

出版年,卷(期):页码：2018,43(5):0-0

摘要：

在热径向锻造工艺过程中，轴类锻件的端部凹坑值反映了锻造过程中的金属轴向流动不均匀性。通过有限元法（FEM）,建立并验证了45号钢轴类零件径向锻造工艺的三维模型，采用田口法设计了实验方案，分析了锤头几何参数和径向压下率等工艺参数对锻件端部凹坑的影响，并引入咬入比参数，提出先将锻件咬入锤头原地锻打再径向锻造的新工艺。结果表明，新工艺有利于减小端部凹坑，锤头几何参数的合理取值范围是锤面直径比φ≤1.2和锤宽比ω≥0.5。基于有限元法的结果，采用T-S模糊神经网络(T-S FNN)较为准确地预测了端部凹坑和锻造载荷。综合考虑锻件缺陷、设备锻造力、生产效率等要求，给出了合理的工艺实例，保证了锻件质量。

In hot radial forging process , the value of end surface concave of axial forging reflects the axial metal flow inhomogeneity. A threedimensional model of radial forging process for steel 45 axis part was established and verified by finite element method (FEM), and the influences of geometrical parameters of hammers and process parameters such as radial reduction rate on end surface concave were analyzed, while the Taguchi method was employed for numerical experiment design. In addition, a new process was proposed with the bite ratio, adding a step of putting the forging into hammers and forging it in situ before the radial forging process. The results show that the new process is beneficial to reduce the depth of end surface concave, and the reasonable range of geometrical parameters of hammers is hammer diameter ratio φ≤1.2 and hammer width ratio ω≥0.5， respectively. Based on the FEM results, the TakagiSugeno Fuzzy Neural Network (T-S FNN) accurately predicts the value of end surface concave and forging load. Considering forging defects, forging force of equipment and production efficiency, a reasonable process case is presented to guarantee the quality of forgings.

基金项目：

作者简介：王碧凝(1993-)，女，硕士研究生 Email：ginkgotea@163.com 通讯作者：董湘怀(1955-)，男，博士，教授,博士生导师 Email：dongxh@sjtu.edu.cn

参考文献：

［1］Lahoti G D, Dembowski P V, Altan T. Radial forging of tubes and rods with compoundangle dies
［J］. Proceeding of NAMRC-IV. 1976,(5): 17-19.

［2］Altan T, Oh S I, Gegel H. Metal forming fundamentals and applications
［J］. American Society for Metals,1983，(17):31-35.

［3］Rooks B W. Software synchronization for radial forging machine manipulators
［J］. Industrial Robot,1996, 23 (6) :19-23.

［4］Wu Y, Dong X, Yu Q. Upper bound analysis of axial metal flow inhomogeneity in radial forging process
［J］. International Journal of Mechanical Sciences, 2015, 93: 102-110.

［5］余琼, 董湘怀, 吴云剑. 径向压下率与送进率对径向锻造工件质量的影响
［J］. 锻压技术, 2015, 40(8): 64-70.

Yu Q, Dong X H, Wu Y J. Influences of radial reduction ratio and feed ratio on radial forging quality
［J］. Forging & Stamping Technology,2015,40(8): 64-70.

［6］周旭东, 刘香茹. 台阶轴径向锻造锤头数对锻透性和生产率的影响
［J］. 锻压技术, 2011, 36(4): 26-29.

Zhou X D, Liu X R. Effects of radial forging die number on forging penetration efficiency and production rate of stepped shaft
［J］. Forging & Stamping Technology,2011, 36(4): 26-29.

［7］Mahfouf M, Abbod M F, Linkens D A. A survey of fuzzy logic monitoring and control utilisation in medicine
［J］. Artificial Intelligence in Medicine, 2001, 21(1): 27-42.

［8］Qiao J, Li W, Han H. Soft computing of biochemical oxygen demand using an improved T-S fuzzy neural network
［J］. Chinese Journal of Chemical Engineering, 2014, 22(11): 1254-1259.

［9］Zhang Y. Application of T-S fuzzy neural network based on declination compensation in soft sensing
［J］. Mathematics and Computers in Simulation, 2012, 86(8):92-99.

［10］Uhlig A. Investigation of the Motions and the Forces in Radial Swaging
［D］. German: Technical University Hannover, 1964.

［11］Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control
［J］. Readings in Fuzzy Sets for Intelligent systems,1993,15(1):387-403.

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