NDLI: The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Grasso, Marco Colosimo, Bianca Maria Moroni, Giovanni
Copyright Year	2012
Abstract	In different manufacturing applications the assessment of the health conditions of a machine tool, together with the quality and stability of the process, requires the capability of dealing with response variables described in terms of profile data. In the frame of in-process monitoring of sensor signals this is the case, for instance, of monitoring either series production of large lots of parts or machining processes characterized by cyclic signals, where both the condition of the machine components and the final quality of the worked piece may be correlated with the stability of repeating signal profiles in time. However, as far as real time data acquisition is concerned, and when measurements are performed with high sampling frequency, data are likely to be auto-correlated, and hence it is of fundamental importance to develop adaptive monitoring tools robust with respect to non-steady state conditions. The paper deals with the utilization of profile monitoring approaches for in-process monitoring of manufacturing operations and investigates their applicability to the problem of monitoring auto-correlated signals. In particular Principal Component Analysis (PCA) is applied in combination with an adaptive approach based on a moving time window for continuously revise the reference model is evaluated and discussed. A real case study is used to test the performances of the method: the task is to detect tool chipping and breakage in end milling operations by means of real-time monitoring of cutting force signals. The evolution of tool wear imposes a trend in observed signals which leads to the need for an adaptive approach to properly isolate the breakage event from the slow pattern change due to wear mechanism.
Sponsorship	International
Starting Page	133
Ending Page	140
Page Count	8
File Format	PDF
ISBN	9780791844878
DOI	10.1115/ESDA2012-82676
Volume Number	Volume 4: Advanced Manufacturing Processes; Biomedical Engineering; Multiscale Mechanics of Biological Tissues; Sciences, Engineering and Education; Multiphysics; Emerging Technologies for Inspection and Reverse Engineering; Advanced Materials and Tribology
Conference Proceedings	ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis
Language	English
Publisher Date	2012-07-02
Publisher Place	Nantes, France
Access Restriction	Subscribed
Subject Keyword	Machine components Mass production Stability Data acquisition Machining Condition monitoring Cutting Wear Milling Signals Manufacturing Machine tools Sensors Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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