NDLI: Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Content Provider	Springer Nature Link
Author	Bustillo, Andres Díez Pastor, José Francisco Quintana, Guillem García Osorio, César
Copyright Year	2011
Abstract	Surface roughness plays a key role in the performance of machined components—specially dies and moulds—manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors, cutting conditions, dynamic factors and lubricant type. An intensive comparison with an artificial neural network approach for the same dataset is included, to reveal the improvements of the new technique over other well-established ones for this industrial problem. This comparison shows that ensemble learning can by-pass the time-consuming task of tuning neural network parameters and can also improve prediction model accuracy, both of which are features that could lead to greater use of these kinds of prediction models in real workshops. Finally, a methodology, based on this new approach, is presented, in order to illustrate how the prediction model can be used in workshops to optimize cutting conditions in terms of their surface quality and productivity.
Starting Page	521
Ending Page	532
Page Count	12
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	57
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-07
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ball-end mill Cutting parameters Surface roughness Ensemble learning Data mining Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design 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

Prediction of cutting force in ball-end milling of sculptured surface using improved Z-map

Decoupled chip thickness calculation model for cutting force prediction in five-axis ball-end milling

An integrated system development for ball end mill design, creation and evaluation

Development of chatter detection in milling processes

Determination of Cutting-Condition-Independent Coefficients and Runout Parameters in Ball-End Milling

A study of surface roughness variation in ultrasonic vibration-assisted milling

Model-based identification of tool runout in end milling and estimation of surface roughness from measured cutting forces

Cutting speed modelling in ball nose milling applications

Improved dynamic cutting force model in ball-end milling. Part I: theoretical modelling and experimental calibration

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Similar Documents

Prediction of cutting force in ball-end milling of sculptured surface using improved Z-map

Decoupled chip thickness calculation model for cutting force prediction in five-axis ball-end milling

An integrated system development for ball end mill design, creation and evaluation

Development of chatter detection in milling processes

Determination of Cutting-Condition-Independent Coefficients and Runout Parameters in Ball-End Milling

A study of surface roughness variation in ultrasonic vibration-assisted milling

Model-based identification of tool runout in end milling and estimation of surface roughness from measured cutting forces

Cutting speed modelling in ball nose milling applications

Improved dynamic cutting force model in ball-end milling. Part I: theoretical modelling and experimental calibration

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations