NDLI: Phenomenological model for drilling operation

Content Provider	Springer Nature Link
Author	Laporte, Sylvain K’Nevez, Jean Yves Cahuc, Olivier Darnis, Philippe
Copyright Year	2007
Abstract	Drilling is the oldest, most widely used manufacturing operation, but it is also quite misunderstood. As drilling accuracy and cost become crucial, a better understanding of tool behaviour is required. Unlike operations such as milling and turning, where cutting phenomena are easily observable, during drilling, the tool is confined to the material. Using a six-component dynamometer (Couétard Y (1993) Capteur de forces à deux voies et application à la mesure d’un torseur de forces. Brevet français 93403025.5) enables the complete identification of forces and torques applied by the tool to the workpiece (Couétard Y, Cahuc O, Darnis Ph (2001) Mesure des 6 actions de coupe en fraisage grande vitesse. Third International Conference on Metal Cutting and high speed machining, Metz, 27–29 Juin, pp. 37–42). Therefore, it is possible to take into account radial components (forces and moments) which are usually neglected in models and experimental approaches (Yang et al. in Int J Mach Tools Manuf 42(2002):299–311, 2001; Chen et al. in J Mater Process Technol 58(1996):314–322, 1995; Joseph Mazoff. Drill point geometry. http://www.newmantools.com/machines/drillpoint.html ). Tool behaviour during critical phases (tool entry and exit) which impact hole quality has been studied. Correlations between six-component dynamometer signals and hole metrology (roughness and geometry specifications) imply that all of six components must be considered to interpret completely drilling phenomena. This way of characterising drill behaviour will ensure optimisation of the tool point geometry and coating according to roughness and geometry specification criteria.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	40
Issue Number	1-2
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Drill Six-component dynamometer Wear Tool behaviour Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
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
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting drill wear using an artificial neural network

Influence of different cooling methods on drill temperature in drilling GFRP

Use of process signals for tool wear progression sensing in drilling small deep holes

Assessment of machining performance using the wear map approach in micro-drilling

On-line monitoring of tool wear in turning operation in the presence of tool misalignment

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

Cutting performance of an indexable insert drill for difficult-to-cut materials under supplied oil mist

Drill and clamping interface in high-performance drilling

Analysis of thrust-induced drilling in composite materials using a hemispherical drill

Phenomenological model for drilling operation

Similar Documents

Predicting drill wear using an artificial neural network

Influence of different cooling methods on drill temperature in drilling GFRP

Use of process signals for tool wear progression sensing in drilling small deep holes

Assessment of machining performance using the wear map approach in micro-drilling

On-line monitoring of tool wear in turning operation in the presence of tool misalignment

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

Cutting performance of an indexable insert drill for difficult-to-cut materials under supplied oil mist

Drill and clamping interface in high-performance drilling

Analysis of thrust-induced drilling in composite materials using a hemispherical drill

Phenomenological model for drilling operation