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

点击翻译按钮获取中文摘要

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.