Parameter based characterization of topographies and the adjustment of grinding processes by self-learning models (T12#)

CRC 653的子项目E1的重点是磨削工具形貌和磨削工件形貌的相互作用。在子项目K2中，开发了用于预测刀具磨损和零件形状误差以及切削过程控制策略的过程模型。目前的资助计划的重点是将这些成果转化为应用程序之间的合作沃尔特Maschinenbau有限公司和生产工程和机床研究所（IFW汉诺威）。计划的项目的目的是扩展的方法来描述粗糙度参数的磨削刀具形貌。首先，将在测量时间、鲁棒性和工业环境中的应用方面对所使用的测量方法进行优化。在此之后，编译的技术知识之间的关系，刀具磨损和修整过程在一方面网站和工件和磨削工具的粗糙度参数在另一方面形成的基础上，从K2到应用程序的自学习模型的转移。该过程模型预测刀具磨损并对过程参数的调整做出反应或推荐修整过程。考虑到粗糙度参数，在机床上进行修整将更有效和经济。调整工艺模型后，Walter Helitronic Vision 400 L工具磨床能够测量和表征磨削工具，并为下一个工件调整工艺参数。此外，磨床知道必要的进给，以可持续的方式锐化和修整磨削工具。

Focus of the sub project E1 of the CRC 653 was the interaction of grinding tool topography and the ground work piece topography. In sub project K2 process models for the prediction of tool wear and form errors of components as well as control strategies of the cutting process were developed. The present funding proposal focuses to transfer these results into applications in a cooperation between Walter Maschinenbau GmbH and the Institute of Production Engineering and Machine Tools (IFW Hannover).The aim of the planned project is the extension of a method to describe grinding tool topographies with roughness parameters. At first the used measurement method will be optimized with regard to measurement time, robustness and the application in an industrial environment. After that compiled technological knowledge about the relationship between tool wear and dressing process at the one hand site and roughness parameter of work piece and grinding tool at the other hand forms the basis for the transfer of self-learning models from K2 into application. The process model predicts tool wear and reacts with the adjustment of the process parameter or recommends a dressing process. Considering the roughness parameter the dressing process in the machine tool will be more efficient and economic. After adjusting the process models the tool grinding machine Walter Helitronic Vision 400L is able to measure and characterize grinding tools and adjust the process parameter for the next work piece. Furthermore the grinding machine knows the necessary infeed to sharpen and dress a grinding tool in a sustainable way.