Prediction of surface conditions for robust control of a turning process based on in-process data acquisition and data driven soft sensor approach

表面决定了工件的功能、阻力和寿命。一方面，它定义了零件的几何形状，并结合其形貌确定了与其他零件的接触面。由于表面也暴露在环境中，它同时受到腐蚀过程的影响。此外，裂纹的形成和扩展以及对塑性变形的抗力取决于表层的性质(残余应力、硬度和显微组织)。计划项目的主要目标是将加工工件的形貌演变、残余应力和硬度与工艺参数、工艺内部的扰动以及表层的初始状态相关联。要开发的模型将基于硬车削产生的数据。加工力和局部温度将在加工过程中测量。现有的后处理表征技术，如X射线衍射残余应力分析，将被微磁3MA方法系统地取代，以实现表面层性能的在线测定。通过一组合适的样品进行校准是必要的。传感器对各种材料特性的不同敏感性使得标定的要求很高，基于采集的数据，采用系统辨识的方法建立一个非线性的经验过程模型。由于所有相关现象的物理建模非常复杂，因此提供了不适合控制设计的模型结构，这些模型不容易转移。最后，对于第二个供资期间的设想控制设计，有必要对刀具磨损进行建模和在线估计。因此，软测量设计是第二个建模任务。软测量将能够根据工艺变量和初始材料属性预测表面层属性。软测量将从不同硬度的工件和几个确定的刀具磨损阶段获得的数据得出，以便在预测表层特性时考虑两者。通过模型反演，可以计算出被操纵变量所需的值。

The surface determines functionality, resistance and lifespan of a workpiece. On the one hand it defines the workpiece geometry and in combination with its topography the contact surface with other workpieces. As the surface is also exposed to the environment, it is at the same time subjected to corrosive processes. Furthermore, crack formation and propagation as well as resistance against plastic deformation are determined by the properties (residual stress, hardness and microstructure) of the surface layer.The primary goal of the planned project is to correlate topography evolution, residual stress and hardness of a machined workpiece to the process parameters, the disturbances within the process as well as the initial condition of the surface layer. The model to be developed will be based on data generated by hard turning. Machining forces and local temperatures will be measured in-process. Established post-process characterization techniques such as XRD residual stress analysis will be systematically substituted by micro-magnetic 3MA approach to enable in-process determination of the surface layer properties. Calibration by means of a suitable set of specimens is necessary. The sensor’s distinct sensitivity to various material properties makes calibration highly demanding.A non-linear empirical process model will be generated on basis of acquired data using methods of system identification. As physical modeling of all relevant phenomena is very complex and, thus, provides for model structures inappropriate for control design, these models cannot easily be transferred. Finally, modelling and online estimation of tool wear is necessary for envisaged control design in the second funding period. Thus, soft sensor design is the second modelling task. The soft sensor will be able to predict surface layer properties based on process variables and initial material properties. The soft sensor will be derived from data obtained from workpieces of varying hardness and several defined stages of tool wear, such that both are considered when surface layer properties are predicted. By means of model inversion the required values of the manipulated variables can be computed.