TY - JOUR

T1 - Self-optimizing process planning for helical flute grinding

AU - Denkena, Berend

AU - Dittrich, Marc-André

AU - Böß, Volker

AU - Wichmann, Marcel

AU - Friebe, Sven

N1 - Funding information: The investigations were conducted within the DFG transfer project BO 3523/6-1 of the priority program 1180. We thank the German Research Foundation for supporting this project.

PY - 2019/10

Y1 - 2019/10

N2 - Grinding of helical flutes is an important step in the process chain of cylindrical tool manufacturing. The grinding process defines the dynamic performance of the manufactured tool. Moreover, the surface quality and flute shape are primarily determined. For this reason, finding optimum process parameters is essential, especially in process planning of individual tools. In the industry, manual experiments are carried out for the machine set up. This leads to high costs due to machine hours, labor and material costs. This paper presents a self-optimizing and adaptable process planning method to reduce costs for process planning and improve the process result. The developed method allows to identify optimum cutting speed and feed with respect to economic efficiency and quality for new tools without additional machining experiments. Geometric-kinematical cutting simulations in combination with empirical models are used to predict the process outcome. The empirical models are derived from machine learning and improved automatically with an increase of process data. Applying the presented method in a case study, the machining time could be reduced by up to 38.1% and the core diameter deviation by up to 73.7%. Moreover, it is shown that the presented methods allow a continuous improvement of the process models.

AB - Grinding of helical flutes is an important step in the process chain of cylindrical tool manufacturing. The grinding process defines the dynamic performance of the manufactured tool. Moreover, the surface quality and flute shape are primarily determined. For this reason, finding optimum process parameters is essential, especially in process planning of individual tools. In the industry, manual experiments are carried out for the machine set up. This leads to high costs due to machine hours, labor and material costs. This paper presents a self-optimizing and adaptable process planning method to reduce costs for process planning and improve the process result. The developed method allows to identify optimum cutting speed and feed with respect to economic efficiency and quality for new tools without additional machining experiments. Geometric-kinematical cutting simulations in combination with empirical models are used to predict the process outcome. The empirical models are derived from machine learning and improved automatically with an increase of process data. Applying the presented method in a case study, the machining time could be reduced by up to 38.1% and the core diameter deviation by up to 73.7%. Moreover, it is shown that the presented methods allow a continuous improvement of the process models.

KW - Machine learning

KW - Optimization

KW - Process planning

KW - Simulation-based planning

KW - Tool grinding

UR - http://www.scopus.com/inward/record.url?scp=85067234513&partnerID=8YFLogxK

U2 - 10.1007/s11740-019-00908-0

DO - 10.1007/s11740-019-00908-0

M3 - Article

AN - SCOPUS:85067234513

VL - 13

SP - 599

EP - 606

JO - Journal of Manufacturing Science and Engineering, Transactions of the ASME

JF - Journal of Manufacturing Science and Engineering, Transactions of the ASME

SN - 1087-1357

IS - 5

ER -