高端装备关键机械部件表面力学性能微磁测量方法研究

The surface mechanical properties of key mechanical components such as turbine blades, large marine crankshaft, high speed/power gears and bearings of high-end equipment directly affect the product quality and reliability. Developing rapid, non-destructive and high-precision measurement methods to determine steel components’ surface mechanical properties can provide fundamental support to surface quality inspection and control, which is of great scientific significance and economic value. This project focuses on measuring and analyzing the mechanical components’ surface mechanical properties. Static and dynamic hysteresis model for a three-layer structure, which is composed of hardened cover layer, transition layer and core, will be developed to study the influence of elasto-magnetic effect and micro-magnetic parameters distribution along the hardened-layer depth direction on the hysteresis characteristics. The relationships between macroscopic magnetic parameters and hardness, case depth, residual stress can be revealed based on the developed model, so that the derived innovative micro-magnetic theory-based methods can be used for characterizing and measuring hardness, case depth, residual stress. A novel multifunctional sensor will be designed to have a common magnetizing circuit using superposed magnetizing current signal for all the measuring modes. Then the experimental data will be used for building effective equations and sensitivity matrices to describe the relationships between the surface mechanical properties and the macroscopic parameters based on predictive statistical models. Finally, the obtained predictive statistical models and sensitivity matrices are applied for the quantitative prediction of hardness, case depth and residual stress. The research outcomes of this project can provide solid technical supports to the manufacturing quality control of key mechanical components in high-end equipment.

高端装备关键机械部件如汽轮机叶片、大型船用曲轴锻件、大功率高速齿轮与轴承等的表面力学性能直接影响高端装备制造产品的质量和可靠性。研究无损、快速、高精度的表面力学性能测量方法，可为机械部件制造过程中的表面质量检验及控制提供基础支撑，具有重要的科学意义和经济价值。本项目围绕机械部件表面硬度、硬化层深度、残余应力的测量与分析，考虑铁磁性材料的磁弹效应及材料微磁特征参数沿深度方向的梯度分布特性，建立硬化层、过渡层与心部组织三层结构的静、动态磁滞模型，揭示硬度、硬化层深度、残余应力对磁学参量的影响规律，提出基于微磁理论的表面力学性能磁学表征和测量方法，发展一种新型共源叠加式多功能微磁测量传感器技术，利用标定实验数据，采用统计模型预测方法建立多元磁学参量对硬度、硬化层深度、残余应力的描述方程及灵敏度矩阵，实现对上述表面力学性能评价指标的定量预测，为高端装备关键机械部件的制造质量控制提供坚实的技术保障。

项目紧密围绕力学性能的微磁无损检测问题，在微磁检测理论、多功能微磁检测传感器技术和力学性能的微磁定量预测模型等方面取得了有益的进展，完成了所有既定目标。取得的主要成果包括：.（1）发展了含应力的多层材料动态磁滞模型，提出了基于微分磁导率双峰曲线的硬化层深度定量检测新方法。基于点状磁荷假设，考虑各层退磁因子和层间磁场耦合对经典J-A-S磁滞方程输入量的调控作用，建立了多层材料动态磁滞模型，分析了层厚、层内应力对磁特性的影响规律，提出了基于微分磁导率双峰曲线的硬化层深度定量检测新方法。.（2）建立了考虑应力的巴克豪森噪声描述模型，提出并实现了基于巴克豪森噪声幅值谱的应力梯度反演方法。建立了考虑不可逆磁化和巴克豪森跳变过程的巴克豪森噪声描述模型，以及考虑应力的巴克豪森噪声幅值谱随传播深度的衰减模型。针对四点弯曲试件表面接收的巴克豪森噪声实验信号，采用遗传算法对衰减模型参数进行反演，定性表征了表面应力及应力梯度。.（3）提出并实现了高、低频叠加的共源励磁条件下的多功能微磁检测方法，研制出多功能微磁检测传感器，集成了微磁检测技术平台。基于共源叠加励磁和切向磁场强度的线性外推测量方法，研制出多功能微磁传感器，集成了实验平台，可同步实施包括磁特性曲线、巴克豪森噪声、切向磁场强度、增量磁导率和涡流在内的5种检测功能。.（4）基于多元线性回归和BP神经网络算法，建立了多项微磁参量输入、多项力学性能输出的定量预测模型，实现了表面硬度、硬化层深度、应力等指标的高精度预测。采用巴克豪森噪声和切向磁场强度同步检测方法，利用BP神经网络技术，建立了多项微磁参数输入的表面硬度定量预测模型，预测误差小于10%；建立的四元线性回归模型对淬硬层深度的预测平均误差仅为3.87%；采用磁滞回线、巴克豪森噪声和切向磁场强度同步检测技术，基于BP神经网络技术，成功实现了硬度、硬化层深度、应力的同步定量预测，平均预测误差分别约为0.19%、3.10%和5.20%。