The Application of Force and Acoustic Emission Sensors for Detecting Tool Damage in Turning Processes

Al‐Habaibeh, Amin; Al-Azmi, A; Radwan, N.; Song, Yang

doi:10.4028/www.scientific.net/kem.419-420.381

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…The sensitivity of condition monitoring system. This research builds on the Automated Sensor and Signal Processing Selection (ASPS) approach [5] to investigate further combination of techniques and parameters using a wide range of signal analysis and simplification techniques. The proposed monitoring system consists simplification the collected signals and measuring the sensitivity of the sensors.…”

Section: Resultsmentioning

confidence: 99%

“…Automated Sensor and Signal Processing Selection (ASPS) approach [5] has been implemented to define the most sensitive features (SCF) which are constructed from the sensor and the signal processing method. Linear regression slop method used to measure the sensitivity of the signal, then these values combined with the real roughness measurements using surface tester.…”

Section: Resultsmentioning

confidence: 99%

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Investigating the Design of Condition Monitoring Systems to Evaluate Surface Roughness under the Variability in Tool Wear and Fixturing Conditions

Abbas

Al‐Habaibeh

2013

KEM

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Surface finish of machined parts in end milling operations is significantly influenced by process faults such as tool wear and tool holding (fixturing system). Therefore, monitoring these faults is considerably important to improve the quality of the product. In this paper, an investigation is presented to design the condition monitoring system to evaluate the surface roughness of the workpiece under effects of gradual tool wear and different types of the fixturing system. Automated Sensor and Signal Processing Selection (ASPS) approach is implemented and tested to determine the sensitivity of the sensory signals to estimate surface roughness under the variable conditions in comparison to surface roughness measurement device. The results indicate that the system is capable of detection the change and the trend in surface roughness. However, the sensitive features are found to be different based on the change in the fixturing system.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Investigating the Design of Condition Monitoring Systems to Evaluate Surface Roughness under the Variability in Tool Wear and Fixturing Conditions

Abbas

Al‐Habaibeh

2013

KEM

Self Cite

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“…Many researchers have used AE for monitoring the turning process while Teti et al [8] investigated additional milling and drilling processes. Bhuiyan et al [26] used AE for monitoring turning and evaluating tool condition, while Al-Habaibeh et al [27] applied additionally force measurement for that purpose. Because the tool condition has a great impact on treatment conditions and the same on quality of the product, that aspect was considered [28].…”

Section: Introductionmentioning

confidence: 99%

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

Dudzik

Labuda

2020

Materials

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Ensuring optimal turning conditions has a huge impact on the quality and properties of the machined surface. The condition of the cutting tool is one of the factors to achieve this goal. In order to control its wear during the turning process, monitoring was used. In this study, the acoustic emission method and measure of cutting forces during turning were used for monitoring that process. The research was carried out on a universal lathe center (CU500MRD type) using a Kistler dynamometer with assembled removable insert CCET09T302R-MF by DIJET Industrial CO., LTD. A dynamometer allows to measure forces Fx (radial force), Fy (feed force) and Fz (cutting force). The turning process was performed on a shaft with 60 mm diameter made of 304L stainless steel. The AE research was carried at Physical Acoustics Corporation with the kit that includes: recorder USB AE Node, preamplifier, AE-sensor VS 150M and computer with dedicated software used for recording and analyzing AE data. The aim of this paper is to compare selected diagnostic methods: acoustic emission and cutting forces measurement for monitoring wear of cutting tool edge. Analysis of the research results showed that both selected methods of monitoring the turning process allowed the determination of the beginning of the tool damage process.

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“…However, it was observed that such models had difficulty estimating approximation errors and therefore required development to capture uncertainties during the turning process. Al-Habaibeh et al 11 and Fang et al 12 used multiscale methods, both of which were based on a milling force signal. Their research demonstrated the usefulness of the milling force, and the designed systems were able to predict tool wear successfully.…”

Section: Introductionmentioning

confidence: 99%

A feature selection method based on an improved fruit fly optimization algorithm in the process of numerical control milling

Yuan

Wang

2018

Advances in Mechanical Engineering

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Automatic control is the key to improved production quality and efficiency of numerical control milling operations. Because the milling cutter is the most important tool in milling operations, the automatic monitoring of the tool wear state is of great significance. This work establishes a set of time domain and time-frequency domain features based on measurements of the cutting force for a computer numerical control milling machine and develops a method incorporating the Fisher criterion in an improved fruit fly optimization algorithm for selecting features most indicative of the tool wear state. A back propagation neural network was employed to test the effectiveness of the proposed feature selection method. Experimental comparisons with three other feature selection methods demonstrate that the proposed improved fruit fly optimization algorithm offers the advantages of the selection of a small number of significant features, easy implementation, precise optimization, rapid training, and good back propagation network performance. The proposed method has great potential for facilitating the practical monitoring of the milling tool wear state.

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The Application of Force and Acoustic Emission Sensors for Detecting Tool Damage in Turning Processes

Cited by 7 publications

References 7 publications

Investigating the Design of Condition Monitoring Systems to Evaluate Surface Roughness under the Variability in Tool Wear and Fixturing Conditions

Investigating the Design of Condition Monitoring Systems to Evaluate Surface Roughness under the Variability in Tool Wear and Fixturing Conditions

The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process

A feature selection method based on an improved fruit fly optimization algorithm in the process of numerical control milling

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