Multiple Regression Prediction Model for Cutting Forces in Turning Carbon-Reinforced PEEK CF30. (8th August 2010)
- Record Type:
- Journal Article
- Title:
- Multiple Regression Prediction Model for Cutting Forces in Turning Carbon-Reinforced PEEK CF30. (8th August 2010)
- Main Title:
- Multiple Regression Prediction Model for Cutting Forces in Turning Carbon-Reinforced PEEK CF30
- Authors:
- Mata, Francisco
Beamud, Elena
Hanafi, Issam
Khamlichi, Abdellatif
Jabbouri, Abdallah
Bezzazi, Mohammed - Other Names:
- Gillespie J. W. Academic Editor.
- Abstract:
- Abstract : Among the thermoplastic polymers available, the reinforced polyetheretherketone with 30% of carbon fibres (PEEK CF 30) demonstrates a particularly good combination of strength, rigidity, and hardness, which prove ideal for industrial applications. Considering these properties and potential areas of application, it is necessary to investigate the machining of PEEK CF30. In this study, response surface methodology was applied to predict the cutting forces in turning operations using TiN-coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design in the design of experiments (DOEs) on CNC turning machine. Based on statistical analysis, multiple quadratic regression model for cutting forces was derived with satisfactoryR 2 -squared correlation. This model proved to be highly preferment for predicting cutting forces.
- Is Part Of:
- Advances in materials science and engineering. Volume 2010(2010)
- Journal:
- Advances in materials science and engineering
- Issue:
- Volume 2010(2010)
- Issue Display:
- Volume 2010, Issue 2010 (2010)
- Year:
- 2010
- Volume:
- 2010
- Issue:
- 2010
- Issue Sort Value:
- 2010-2010-2010-0000
- Page Start:
- Page End:
- Publication Date:
- 2010-08-08
- Subjects:
- Materials science -- Periodicals
Materials science
Periodicals
620.11 - Journal URLs:
- http://www.hindawi.com/journals/amse ↗
- DOI:
- 10.1155/2010/824098 ↗
- Languages:
- English
- ISSNs:
- 1687-8434
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10280.xml