What does this research mean for the field?

Integrating spindle power information into a Kalman filter methodology enables the prediction of tool flank wear in hard-to-machine gamma-prime strengthened alloys with a maximum estimation error of 18%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to enhance the prediction of tool wear during the machining of gamma-prime strengthened alloys using a Kalman filter.

January 1, 2015Procedia ManufacturingOpen Access

In-process Tool Flank Wear Estimation in Machining Gamma-prime Strengthened Alloys Using Kalman Filter

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Authors

FNFarbod Akhavan NiakiClemson University DUDurul UlutanRutgers, The State University of New Jersey LMLaine MearsClemson University

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Implication

Randomized trial integrates spindle power data to predict tool wear in machining gamma-prime alloys, indicating effective monitoring.

Key Points

This research aims to enhance the prediction of tool wear during the machining of gamma-prime strengthened alloys using a Kalman filter.
Integrated spindle power consumption data into a Kalman filter methodology.
Monitored tool wear during CNC machining of gamma-prime strengthened alloys.
Analyzed the predictive accuracy of the tool wear estimation.
Achieved a maximum estimation error of 18%.
Demonstrated the potential of using a Kalman filter for predicting tool flank wear.
Validated the effectiveness of low-cost sensing technology for monitoring tool wear.

Cite This Study

Niaki et al. (2015) studied this question.

synapsesocial.com/papers/6a6345f0809dd4a9e2c1cd24 https://doi.org/10.1016/j.promfg.2015.09.018

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In-process Tool Flank Wear Estimation in Machining Gamma-prime Strengthened Alloys Using Kalman Filter

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Key Points

Cite This Study

In-process Tool Flank Wear Estimation in Machining Gamma-prime Strengthened Alloys Using Kalman Filter

Discussion

Member takes

Implication

Key Points

Cite This Study

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