Tool Condition Monitoring System: A Review

Ambhore, Nitin; Kamble, Dinesh; Chinchanikar, Satish; Wayal, Vishal

doi:10.1016/j.matpr.2015.07.317

Cited by 139 publications

(65 citation statements)

References 52 publications

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“…Condition monitoring [4] is a technique which is particularly prevalent in machine monitoring applications within the automotive [5,6], aeronautical [7], and manufacturing [8] industries. Condition monitoring is used as an effective tool to promote a predictive maintenance strategy, rather than operating the traditional run-to-break strategy [9], which can lead to catastrophic failures.…”

Section: Introductionmentioning

confidence: 99%

Performance Verification of a Flexible Vibration Monitoring System

et al. 2020

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The performance of measurement or manufacturing systems in high-precision applications is dependent upon the dynamics of the system, as vibration can be a significant contributor to the measurement uncertainty and process variability. Technologies making use of accelerometers and laser vibrometers are available to rapidly measure and process structural dynamic data but the software infrastructure is yet to be available in an open source or standardised format to allow rapid inter-platform use. In this paper, we present a novel condition monitoring system, which uses commercially available accelerometers in combination with a control-monitoring infrastructure to allow for the appraisal of the performance of a measurement or manufacturing system. A field-programmable gate array (FPGA)-based control system is implemented for high-speed data acquisition and signal processing of six triaxial accelerometers, with a frequency range of 1 Hz to 6000 Hz, a sensitivity of 102.5 mV/ms−2 and a maximum sample rate of 12,800 samples per second per channel. The system includes two methods of operation: real-time performance monitoring and detailed measurement/manufacturing verification. A lathe condition monitoring investigation is undertaken to demonstrate the utility of this system and acquire typical machining performance parameters in order to monitor the “health” of the system.

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Section: Introductionmentioning

confidence: 99%

Performance Verification of a Flexible Vibration Monitoring System

et al. 2020

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“…Real-time tool wear measurement is difficult to put in practice as the tool is continuously in contact with the workpiece during machining. For this reason, a plethora of indirect approaches for tool wear estimation (also referred as Prognosis) have been proposed utilising sensor signals such as cutting forces, vibrations, acoustic emissions and power consumption [4].…”

Section: Introductionmentioning

confidence: 99%

Tool wear classification using time series imaging and deep learning

Martínez-Arellano

Terrazas

Ratchev

2019

Int J Adv Manuf Technol

164

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Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

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“…Regarding the significant failures of machine tools, they mostly monitor the machining process and mechanical structures of machine tools (feeding systems, tool changer, pallet and spindle system) by physical signals such as the vibration, power, current, acoustic emission, etc. [1][2][3][4]. The acquired signals are generally processed with the pattern recognition methods, such as neural networks, expert systems and fuzzy logic for condition monitoring and fault diagnosis [5].…”

Section: Introductionmentioning

confidence: 99%

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Xing

Mayer

Achiche

2018

JMMP

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Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.

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Tool Condition Monitoring System: A Review

Cited by 139 publications

References 52 publications

Performance Verification of a Flexible Vibration Monitoring System

Performance Verification of a Flexible Vibration Monitoring System

Tool wear classification using time series imaging and deep learning

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

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