Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools

Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful l...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent manufacturing 2015-04, Vol.26 (2), p.255-268
Main Authors: Gokulachandran, J., Mohandas, K.
Format: Article
Language:English
Subjects:
Analysis
Assessments
Business and Management
Comparative studies
Control
Cutting tools
Experiments
Fuzzy logic
Intelligent systems
Machine learning
Machines
Manufacturing
Manufacturing execution systems
Manufacturing industry
Mathematical analysis
Mechatronics
Methods
Neural networks
Processes
Product development
Production
Productivity
Regression
Reuse
Robotics
Soft computing
Studies
Support vector machines
Tool life
Useful life
Waste management
Waste materials
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. This exercise will in turn reduce the overall cost and help achieve enhanced productivity. This paper reports the use of two soft computing techniques, namely, neuro fuzzy logic technique and support vector regression technique for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid two soft computing techniques and RUL obtained from these values are compared.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-013-0778-2