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

基于过程中数据采集和数据驱动的软传感器方法预测表面条件，以实现车削过程的鲁棒控制

• 批准号: 401792249
• 负责人: Professor Dr.-Ing. Andreas Kroll
• 金额: --
• 依托单位: Fachgebiet Metallische Werkstoffe
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/401792249?language=en
• 关键词: Prediction; surface; conditions; robust; control

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.

表面决定了工件的功能性、耐受性和寿命。一方面，它定义了工件的几何形状，并结合其形貌与其他工件的接触表面。由于表面也暴露在环境中，因此同时也会受到腐蚀过程的影响。此外，裂纹的形成和扩展以及抗塑性变形的能力取决于表面层的性能（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、过程中的干扰以及表面层的初始条件相关联。待开发的模型将基于硬车削产生的数据。将在过程中测量加工力和局部温度。已建立的后处理表征技术（如XRD残余应力分析）将被微磁3MA方法系统地取代，以实现表面层特性的过程中测定。需要通过一组合适的样品进行校准。传感器对各种材料特性的独特敏感性使得校准要求很高。使用系统识别方法，根据采集的数据生成非线性经验过程模型。由于所有相关现象的物理建模是非常复杂的，因此，提供了不适合控制设计的模型结构，这些模型不能容易地被转移。最后，在第二个供资期内，为设想的控制设计建立模型和在线估计刀具磨损是必要的。因此，软测量设计是第二个建模任务。软测量将能够根据过程变量和初始材料特性预测表面层特性。软传感器将从不同硬度的工件和几个定义的刀具磨损阶段获得的数据中导出，以便在预测表面层特性时考虑两者。通过模型反演，可以计算所需的操纵变量值。