Continuous improvement of HSM process by data mining

Continuous improvement of HSM process by data mining The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach. Keywords Monitoring · Machining · Data mining Introduction almost unused, whereas it is key leverage point for process improvement. In an Industry 4.0 approach, data from the A huge and increasing amount of digital data is generated in CNC (Computer Numerical Control) and from additional every industry and business area, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Manufacturing Springer Journals

Continuous improvement of HSM process by data mining

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Business and Management; Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics
ISSN
0956-5515
eISSN
1572-8145
D.O.I.
10.1007/s10845-018-1426-7
Publisher site
See Article on Publisher Site

Abstract

The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach. Keywords Monitoring · Machining · Data mining Introduction almost unused, whereas it is key leverage point for process improvement. In an Industry 4.0 approach, data from the A huge and increasing amount of digital data is generated in CNC (Computer Numerical Control) and from additional every industry and business area,

Journal

Journal of Intelligent ManufacturingSpringer Journals

Published: Jun 4, 2018

References

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