NDLI: Monitoring in precision metal drilling process using multi-sensors and neural network

Content Provider	Springer Nature Link
Author	Cruz, Carlos Eduardo Dorigatti Aguiar, Paulo Roberto Machado, Álisson Rocha Bianchi, Eduardo Carlos Contrucci, João Gabriel Neto, Frederico Castro
Copyright Year	2012
Abstract	This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes.
Starting Page	151
Ending Page	158
Page Count	8
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	66
Issue Number	1-4
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-06-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Drilling process monitoring Hole diameter Surface roughness Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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