Tool Wear and Surface Finish Investigation of Hard Turning Using Tool Imaging

Sharma, Anurag; Seshu, K. V. G. Rama; Gopichand, A.; Subbaiah, K.

doi:10.15623/ijret.2012.0103025

IJRET

2012

DOI: 10.15623/ijret.2012.0103025

|View full text |Cite

Tool Wear and Surface Finish Investigation of Hard Turning Using Tool Imaging

Anurag Sharma¹,

K. V. G. Rama Seshu²,

A. Gopichand³

et al.

Abstract: Surface roughness and tool wear is one of the most specified customer requirements in a machining process. To predict the surface roughness and tool wear, Genetic Algorithm& Image processing model was designed through MATLAB 7.1 software for the data obtained.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Tool wear and tool life estimation based on linear regression learning

Karuppusamy

Lee

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

View full text Add to dashboard Cite

Tools have remained an integral part of the society without which stimulation of certain aspects of human evolution would not have been possible. In recent times the modern tools are used in the manufacturing of high precision components. We know that the accuracy and surface finish of these components can be achieved only by the usage of accurate tools. Sharp edged tools may loosen their sharpness due to repeated usage and machining parameters. Hence to address this issue we propose a system to monitor tool wear by using the captured image of cutting tool tip. We used vision system since it is the primitive method of predicting tool wear and two main machining parameters feed rate and depth of cut. The image of flank wear cutting edge at tool tip is captured by examining under profile projector. The system uses linear regression model to calculate tool wear which is mapped onto continuous 2-D coordinates with feed rate and depth of cut as axis from a captured digital image. Thus the proposed intelligent system uses profile projector and digital image processing methods to estimate tool wear continuously and predictively like humans rather than using strict rules. By estimating tool wear continuously the machine can better perform and machine components accurately by using the resultant values of feed rate and depth of cut as a threshold which are arrived as a result.

show abstract

Tool wear and tool life estimation based on linear regression learning

Karuppusamy

Lee

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tool Wear and Surface Finish Investigation of Hard Turning Using Tool Imaging

Abstract: Surface roughness and tool wear is one of the most specified customer requirements in a machining process. To predict the surface roughness and tool wear, Genetic Algorithm& Image processing model was designed through MATLAB 7.1 software for the data obtained.

Cited by 1 publication

References 6 publications

Tool wear and tool life estimation based on linear regression learning

Tool wear and tool life estimation based on linear regression learning

Contact Info

Product

Resources

About