Empirical models of mechanical and electrical drilling power of mild steel

F. A. Al-Sulaiman; A. K. Sheikh; M. A. Baseer

doi:10.1243/0954405041897077

Empirical models of mechanical and electrical drilling power of mild steel

F. A. Al-Sulaiman^*, A. K. Sheikh, M. A. Baseer

^*Corresponding author for this work

Department of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Two empirical models for electrical and mechanical power prediction were developed for drilling mild steel. Multiple linear regression analysis was implemented using a three-level three-variable (3³) factorial design experiment for mild steel. The drill diameter, feed rate and cutting speed are the independent variables. Both models were in close agreement with the experimental results. The correlation coefficient for both models was 91 per cent. These models can be effectively utilized in conjunction with a proposed on-line monitoring system to predict the tool condition. Tool replacement decision making can be implemented using either model. However, the model for electrical power was a better indicator of tool wear.

Original language	English
Pages (from-to)	1181-1189
Number of pages	9
Journal	Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume	218
Issue number	9
DOIs	https://doi.org/10.1243/0954405041897077
State	Published - Sep 2004

Keywords

Data acquisition system
Multiple linear regression
Numerical control machining
Tool condition monitoring

ASJC Scopus subject areas

Mechanical Engineering
Industrial and Manufacturing Engineering

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10.1243/0954405041897077

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title = "Empirical models of mechanical and electrical drilling power of mild steel",

abstract = "Two empirical models for electrical and mechanical power prediction were developed for drilling mild steel. Multiple linear regression analysis was implemented using a three-level three-variable (33) factorial design experiment for mild steel. The drill diameter, feed rate and cutting speed are the independent variables. Both models were in close agreement with the experimental results. The correlation coefficient for both models was 91 per cent. These models can be effectively utilized in conjunction with a proposed on-line monitoring system to predict the tool condition. Tool replacement decision making can be implemented using either model. However, the model for electrical power was a better indicator of tool wear.",

keywords = "Data acquisition system, Multiple linear regression, Numerical control machining, Tool condition monitoring",

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year = "2004",

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AU - Al-Sulaiman, F. A.

AU - Sheikh, A. K.

AU - Baseer, M. A.

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Y1 - 2004/9

N2 - Two empirical models for electrical and mechanical power prediction were developed for drilling mild steel. Multiple linear regression analysis was implemented using a three-level three-variable (33) factorial design experiment for mild steel. The drill diameter, feed rate and cutting speed are the independent variables. Both models were in close agreement with the experimental results. The correlation coefficient for both models was 91 per cent. These models can be effectively utilized in conjunction with a proposed on-line monitoring system to predict the tool condition. Tool replacement decision making can be implemented using either model. However, the model for electrical power was a better indicator of tool wear.

AB - Two empirical models for electrical and mechanical power prediction were developed for drilling mild steel. Multiple linear regression analysis was implemented using a three-level three-variable (33) factorial design experiment for mild steel. The drill diameter, feed rate and cutting speed are the independent variables. Both models were in close agreement with the experimental results. The correlation coefficient for both models was 91 per cent. These models can be effectively utilized in conjunction with a proposed on-line monitoring system to predict the tool condition. Tool replacement decision making can be implemented using either model. However, the model for electrical power was a better indicator of tool wear.

KW - Data acquisition system

KW - Multiple linear regression

KW - Numerical control machining

KW - Tool condition monitoring

UR - https://www.scopus.com/pages/publications/6344231976

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JF - Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

IS - 9

ER -

Empirical models of mechanical and electrical drilling power of mild steel

Abstract

Keywords

ASJC Scopus subject areas

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