Detecting catastrophic failure events in large-scale milling machines
Satti, Teysseer, Young, Ken and Grover, Samuel. (2009) Detecting catastrophic failure events in large-scale milling machines. International Journal of Machine Tools and Manufacture, Vol.49 (No.14). pp. 1104-1113. ISSN 0890-6955Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.ijmachtools.2009.07.01...
Catastrophic failure in milling machines is a major concern for manufacturers employing these processes in the production of vital parts. Tool chipping or breakage can lead to machine breakdown, which is a costly consequence in today's highly demanding industry. This paper introduces a novel and practical concept for the detection of failure events in milling. Using the historic data of the machining process (a collection of average spindle power signals) the detection algorithm computes discrete probability distributions representing the power consumption profile along finite synchronous process segments. These distributions play a central role in identifying failure: an unexpected occurrence in the process. Using a combination of real data collected from a powerful industrial milling machine and failure disturbance simulations, concept testing results illustrate that the proposed algorithm is capable of promptly detecting catastrophic faults while keeping unnecessary interruptions to the machine operation to a minimum. (C) 2009 Elsevier Ltd. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||T Technology > TS Manufactures
T Technology > TJ Mechanical engineering and machinery
|Divisions:||Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)|
|Journal or Publication Title:||International Journal of Machine Tools and Manufacture|
|Publisher:||Elsevier Science Ltd.|
|Number of Pages:||10|
|Page Range:||pp. 1104-1113|
|Access rights to Published version:||Restricted or Subscription Access|
|Funder:||UK's Engineering and Physical Sciences Research Council (ESPRC), Airbus UK|
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