In-Process Tool Wear Detection Using Internal Encoder Signals for Unmanned Robust Machining
2016 (English)In: High Speed Machining - Modern Manufacturing Technologies, ISSN 2299-3975, Vol. 2, no 1, 37-50 p.Article in journal (Refereed) Published
Automated Tool Condition Monitoring (TCM) often relies on additional sensors sensitive to tool wear to achieve robust machining processes. The need of additional sensors could impede the implementation of tool monitoring systems in industry due to the cost and retrofitting difficulties. This paper has investigated the use of existing position encoder signals to monitor a special face turning process with constant feed per revolution and machining speed. A signal processing method by converting encoder signals into a complex-valued form and a new vibration signature extraction method based on phase function were developed to analyze the encoder signals in the frequency domain. The cumulative spectrum indicated that the spectral energy would shift from the lower to the higher frequency band with increasing cutting load. The embedded vibration signatures extracted from the encoder signals provided real-time detectability of the machining condition with distinguishable spectral modes. The embedded vibration signatures extracted from the encoder signals provided additional detectability of the machining condition with distinguishable spectral modes. In particular, tool chipping manifested itself as significant amplitude changes at a specific frequency band 20-30 Hz in the extracted vibration signatures. A new monitoring metric based on the XY-plane modulations combined with statistical process control charts was proposed and shown to be a robust tool wear and tool wear rate indicator. The results show that when tool chipping occurred, it could be detected in real-time when this this tool wear rate value jumped in combination with breach of the control limits. This confirms that internal encoder signals, together with the proposed metric, could be a robust in-process tool wear monitor.
Place, publisher, year, edition, pages
De Gruyter Open, 2016. Vol. 2, no 1, 37-50 p.
Face turning, TCM, tool wear, encoder signals, SPC, EWMA
Manufacturing, Surface and Joining Technology
Research subject ENGINEERING, Manufacturing and materials engineering; Production Technology
IdentifiersURN: urn:nbn:se:hv:diva-10071DOI: 10.1515/hsm-2016-0004OAI: oai:DiVA.org:hv-10071DiVA: diva2:1040357