Grinding Burn Prediction by Sensor Data in Generating Gear Grinding

Lecture notes in production engineering, pp. 183–191

Abstract

Abstract Continuous generating grinding is a highly productive process for hard finishing gears to meet quality and surface integrity standards. Due to its abrasive characteristics and the resulting significant material deformation, the process requires substantial energy per volume of material removed. This energy results in thermal and mechanical loads into the workpiece. Thermal loads account for the largest share, resulting in a risk to surface integrity through grinding burn. To ensure sufficient surface integrity while enhancing productivity, the current approach involves iterative adjustment of process parameters along with monitoring for the presence of grinding burn. Previous research has shown a correlation between the grinding machine tool spindle power signals and analytically calculated thermal energy, indicating potential for in-situ assessment of surface integrity. Therefore, the objective of this paper is to systematically investigate the correlations between machine signals and the presence of thermal damage. To verify the effects in the high frequency range, where micro-cutting components are dominant, acoustic emission and acceleration sensors were employed to record the energy responses during the process. To determine which sensors effectively acquire process-relevant information correlated to grinding burn, the sensors were installed at different locations in the grinding machine. Generating gear grinding trials were conducted, varying the feed and infeed to generate different levels of grinding burn. The sensor data were processed and evaluated, and the observed patterns were correlated with the presence of grinding burn in the ground gears.

Authors 4

1. Mariana Mendes Wilfinger corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   MTI of RWTH Aachen University, Aachen, Germany

   WZL of RWTH Aachen University, Aachen, Germany

2. Mareike Davidovic Aachen

   RWTH Aachen University

   Affiliation as printed

   MTI of RWTH Aachen University, Aachen, Germany

   WZL of RWTH Aachen University, Aachen, Germany

3. Christian Westphal Aachen

   RWTH Aachen University

   Affiliation as printed

   MTI of RWTH Aachen University, Aachen, Germany

   WZL of RWTH Aachen University, Aachen, Germany

4. Thomas Bergs Aachen Fraunhofer IPT

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer IPT, Aachen, Germany

   MTI of RWTH Aachen University, Aachen, Germany