Development of an adequate online tool wear monitoring system in turning process using low cost sensor

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Tool wear is well known to affect tool life, surface quality and production time. Due to this reason, an online tool wear measurement and prediction system has been developed, using a low-cost sensor. This study proposes an alternative for cutting force measurement using strain gauge. A two-channel strain gauge is mounted at the tool holder to measure the deflection in both tangential direction and feed direction. New statistical analysis is used to identify and characterize the changes in signals from the sensors. A database for prediction tool wear is built from the experimental data when cutting hardened carbon steel S45C using cutting tool insert NC30P grade. A user-friendly graphical user interface (GUI) has been developed for the online prediction purpose. Results show that online prediction tool wear is quite satisfactory with RMSE between 0.0135 and 0.0273, and MAPE between 0.0745 and 0.1226. This is an efficient and low-cost method which can be used in the real machining industry to predict the level of wear in the cutting tool.

Original languageEnglish
Pages (from-to)702-706
Number of pages5
JournalAdvanced Science Letters
Volume13
DOIs
Publication statusPublished - Jun 2012

Fingerprint

Tool Wear
Monitoring System
monitoring system
Wear of materials
monitoring
sensor
Costs and Cost Analysis
Sensor
Strain Gauge
Monitoring
Prediction
Steel
Sensors
costs
cost
Costs
Industry
prediction
Carbon
Quality of Life

Keywords

  • GUI
  • I-kaz™ method
  • Low Cost Sensor
  • Prediction Tool Wear

ASJC Scopus subject areas

  • Education
  • Health(social science)
  • Mathematics(all)
  • Energy(all)
  • Computer Science(all)
  • Environmental Science(all)
  • Engineering(all)

Cite this

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title = "Development of an adequate online tool wear monitoring system in turning process using low cost sensor",
abstract = "Tool wear is well known to affect tool life, surface quality and production time. Due to this reason, an online tool wear measurement and prediction system has been developed, using a low-cost sensor. This study proposes an alternative for cutting force measurement using strain gauge. A two-channel strain gauge is mounted at the tool holder to measure the deflection in both tangential direction and feed direction. New statistical analysis is used to identify and characterize the changes in signals from the sensors. A database for prediction tool wear is built from the experimental data when cutting hardened carbon steel S45C using cutting tool insert NC30P grade. A user-friendly graphical user interface (GUI) has been developed for the online prediction purpose. Results show that online prediction tool wear is quite satisfactory with RMSE between 0.0135 and 0.0273, and MAPE between 0.0745 and 0.1226. This is an efficient and low-cost method which can be used in the real machining industry to predict the level of wear in the cutting tool.",
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author = "{A Ghani}, Jaharah and Muhammad Rizal and Nuawi, {Mohd. Zaki} and {Che Haron}, {Che Hassan}",
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AU - Nuawi, Mohd. Zaki

AU - Che Haron, Che Hassan

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N2 - Tool wear is well known to affect tool life, surface quality and production time. Due to this reason, an online tool wear measurement and prediction system has been developed, using a low-cost sensor. This study proposes an alternative for cutting force measurement using strain gauge. A two-channel strain gauge is mounted at the tool holder to measure the deflection in both tangential direction and feed direction. New statistical analysis is used to identify and characterize the changes in signals from the sensors. A database for prediction tool wear is built from the experimental data when cutting hardened carbon steel S45C using cutting tool insert NC30P grade. A user-friendly graphical user interface (GUI) has been developed for the online prediction purpose. Results show that online prediction tool wear is quite satisfactory with RMSE between 0.0135 and 0.0273, and MAPE between 0.0745 and 0.1226. This is an efficient and low-cost method which can be used in the real machining industry to predict the level of wear in the cutting tool.

AB - Tool wear is well known to affect tool life, surface quality and production time. Due to this reason, an online tool wear measurement and prediction system has been developed, using a low-cost sensor. This study proposes an alternative for cutting force measurement using strain gauge. A two-channel strain gauge is mounted at the tool holder to measure the deflection in both tangential direction and feed direction. New statistical analysis is used to identify and characterize the changes in signals from the sensors. A database for prediction tool wear is built from the experimental data when cutting hardened carbon steel S45C using cutting tool insert NC30P grade. A user-friendly graphical user interface (GUI) has been developed for the online prediction purpose. Results show that online prediction tool wear is quite satisfactory with RMSE between 0.0135 and 0.0273, and MAPE between 0.0745 and 0.1226. This is an efficient and low-cost method which can be used in the real machining industry to predict the level of wear in the cutting tool.

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