MUTUAL CHANGE OF ACOUSTIC EMISSION STATISTICAL ENERGY PARAMETERS AT TREATING TOOL WEAR

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

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In-Process Monitoring and Estimation of Tool Wear on CNC Turning by Applying Multi-Sensor with Back Propagation Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.291-294.3036 ◽

2011 ◽

Vol 291-294 ◽

pp. 3036-3043 ◽

Cited By ~ 1

Author(s):

Somkiat Tangjitsitcharoen ◽

Channarong Rungruang

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Monitoring System ◽

Process Monitoring ◽

Back Propagation ◽

Force Sensor ◽

Accelerometer Sensor ◽

Cnc Turning ◽

Propagation Technique ◽

Cnc Turning Process

The aim of this research is to propose and develop the in-process monitoring system of the tool wear for the carbon steel (S45C) in CNC turning process by utilizing the multi-sensor which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The progress of the tool wear results in the larger cutting force, the higher amplitude of the acceleration signal, and the higher power spectrum densities of sound and acoustic emission signals. Hence, their signals have been integrated via the neural network with the back propagation technique to monitor the tool wear. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level with the higher accuracy and reliability.

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In-process tool wear and chip-form monitoring in face milling operation using acoustic emission.

Journal of Materials Processing Technology ◽

10.1016/0924-0136(94)90433-2 ◽

1994 ◽

Vol 44 (3-4) ◽

pp. 207-214 ◽

Cited By ~ 31

Author(s):

Sunilkumar Kakade ◽

L. Vijayaraghavan ◽

R. Krishnamurthy

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Face Milling ◽

Milling Operation

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Körperschallemission in CFK und Fräswerkzeugen/Acoustic emission in CFRP and milling tools – Analysis of emitted acoustic emission in analogous experiments

wt Werkstattstechnik online ◽

10.37544/1436-4980-2021-07-08-27 ◽

2021 ◽

Vol 111 (07-08) ◽

pp. 495-500

Author(s):

Eckart Uhlmann ◽

Tobias Holznagel ◽

Sebastian Ospina Mora

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Process Monitoring ◽

Detailed Examination ◽

Data Driven ◽

Material Damage ◽

Emission Measurement ◽

Wear Rates ◽

Milling Tools ◽

Acoustic Emission Measurement

Herausforderungen bei der spanenden Bearbeitung von CFK sind unerwünschte Materialschädigungen im Bauteil sowie hohe Werkzeugverschleißraten. Für diese Arbeit wurden zerstörende Analogversuche an CFK und diamantbeschichteten Hartmetallproben durchgeführt und die Körperschallmesssignale analysiert. Die eingehende Untersuchung der Messwerte aus den Analogversuchen kann zu einer datengetriebenen Parametrierung einer körperschallbasierten Prozessüberwachung für die Fräsbearbeitung von CFK beitragen.   Challenges in machining CFRP are unwanted material damage in the component and high tool wear rates. For this work, destructive analogy experiments were carried out on CFRP and diamond-coated milling tools while analyzing the acoustic emission measurement signals. The detailed examination of the measured values from the analogy experiment can contribute to a data-driven parameterization of an acoustic emission-based process monitoring for the milling of CFRP.

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