Model-Based Online Tool Monitoring for Hobbing Processes

n Continuous increase in flight passengers alongside with a high demand for fuel efficiency has led to the development of Geared Turbo Fans (GTF). Being a safety- critical part, the gearbox faces strong safety requirements that also account for sophisticated manufacturing processes and monitoring systems. One major issue is tool wear and the threat of tool breakage during the hobbing process. Due to the high costs of both the raw material and the tool, wear induced tool breakage is a major cost driver. Common practice today is to use each tool for a designated time, but in-situ online wear assessment would result in a reduction of costs as tools can be used to their individual potential. Tool wear of hobs is not spread equally across all cutting edges; hence the assignment of tool wear to each tooth would enable a monitoring system to analyze the individual tool life and predict its operational capability. This research paper presents a method of using effective power signals in combination with a predictive model to determine the actual wear status of each tooth. The model uses the chip geometry and a force model in order to predict the expected torques of the spindle and compares them in real-time with measurement data. An algorithm then estimates the wear status of each tooth. These findings enable further research on an online, model based and position- oriented tool wear monitoring system. (C) 2017 The Authors. Published by Elsevier B.V.