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Abstract
In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.
Highlights
The continuous pressure to reduce costs is one of the main challenges in subtractive manufacturing, especially for small- and medium-sized enterprises as well as contract manufacturers
The OberA research project is investigating how digital manufacturing solutions can be utilized in these environments to optimize machining processes during daily work
Tool condition monitoring (TCM) systems have been widely researched. Such TCM systems allow the condition of the cutting tool to be monitored, so that it can be used for as long as possible but is replaced before it breaks
Summary
The continuous pressure to reduce costs is one of the main challenges in subtractive manufacturing, especially for small- and medium-sized enterprises as well as contract manufacturers To this end, the OberA research project is investigating how digital manufacturing solutions can be utilized in these environments to optimize machining processes during daily work. Signals such as acoustic emission [3], power [4], current [5], torque [6], or vibrations [7] are used to deduce the tool’s condition While these approaches have the advantage of easy application to online monitoring, they are prone to noise from the environment [8,9]
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