Tool wear state prediction based on feature-based transfer learning

Li, Jianbo; Lü, Juan; Chen, Chaoyi; Ma, Junyan; Liao, Xiaoping

doi:10.1007/s00170-021-06780-6

Cited by 42 publications

(15 citation statements)

References 41 publications

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“…Recently, computer vision and feature extraction are continuously evolving and their popularity is increasing with the help of advanced technology [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements

Akkoyun

Erçetin

Aslantaş

2021

Preprint

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In this study, the burr and slot widths formed after micro-milling process are investigated using a rapid and accurate image processing method. The measurements are obtained by processing the images and results were compared with a manual measurement method. In the cutting experiment stage, Inconel 718 alloy was chosen as the workpiece and cutting tools with various specific properties were used. The images of the burr and slots were captured using scanning electron microscope (SEM). Different tool geometries and cutting parameters were considered for choosing the SEM images. Captured images were processed with a computer vision software which was written in C + + programming language and open-sourced computer library (Open CV). The demonstrated approach was successfully measured the slot and burr widths in plain and complex conditions where slot and burr are nested. According to the close findings of manual and automated measurements, it was observed that burr widths increased especially at the down milling sides and slot widths decreased due to the increased cutting length. Specific tool properties such as number of cutting edge, helix angle and cutting length affected the slot and burr widths. It was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively.

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“…Recently, computer vision and feature extraction are continuously evolving and their popularity is increasing with the help of advanced technology [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements

Akkoyun

Erçetin

Aslantaş

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The feature selection techniques can be categorized as follows [ 173 ]: Filter techniques are open-loop computational methods that only consider the relationship between features and class label without involving the subsequent tool wear classification model, as shown in Figure 6 . They evaluate the usefulness of features subsets based on their intrinsic properties using evaluation measures, such as dependency, consistency, or information, to eliminate low-ranking features [ 171 , 174 , 175 ]. The ranking measure is determined using statistical measures, such as Pearson’s correlation coefficient, the coefficient of determination, minimum redundancy maximum relevance (mRMR), or analysis of variance ANOVA [ 171 , 174 , 176 , 177 , 178 ].…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

“…They evaluate the usefulness of features subsets based on their intrinsic properties using evaluation measures, such as dependency, consistency, or information, to eliminate low-ranking features [ 171 , 174 , 175 ]. The ranking measure is determined using statistical measures, such as Pearson’s correlation coefficient, the coefficient of determination, minimum redundancy maximum relevance (mRMR), or analysis of variance ANOVA [ 171 , 174 , 176 , 177 , 178 ]. A detailed discussion on various performance measures is available in [ 179 ].…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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“…This approach allows for considerable efficiencies to be gained, as we are able to re-use much of the network, reducing the amount of data required, while obtaining similar accuracy. This approach significantly reduces the barriers to implementing many neural networks, and has successfully been applied in many different CNC applications [24] [19]. However, this approach has not been implemented using LSTM networks for anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Three-Axis CNC Machines using LSTM Networks and Transfer Learning

Bedi²,

Melek³

2022

Preprint

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There is a growing interest in developing automated manufacturing technologies to achieve a fully autonomous factory. An integral part of these smart machines is a mechanism to automatically detect operational and process anomalies before they cause serious damage. The Long-Short-Term-Memory (LSTM) network has shown considerable promise in the literature, with applications in detection of tool wear and tool breakage to name a few. However, these methods require a significant amount of machine specific training data to be successful, which makes these networks custom to a machine, requiring new networks and new data for each machine. Transfer learning is an approach where we use a network developed with a rich data set on one machine, and re-train it with a smaller data set on a target machine. We have implemented this approach for chatter detection with a LSTM network, using sensor data and a rich data set from one machine, and then use a transfer learning methodology, similar sensors, and a smaller data set for the chatter detection algorithm on another machine. This allows for the transfer of knowledge from one machine to be applied to a similar machine, with some local optimization from transfer learning

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Tool wear state prediction based on feature-based transfer learning

Cited by 42 publications

References 41 publications

WITHDRAWN: Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements

WITHDRAWN: Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements

Tool Condition Monitoring for High-Performance Machining Systems—A Review

Anomaly Detection in Three-Axis CNC Machines using LSTM Networks and Transfer Learning

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