Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

Yang, Zhou; Liu, Changfu; Yu, Xinli; Liang, Bo; Quan, Yu

doi:10.1007/s42452-022-05114-9

Cited by 24 publications

(9 citation statements)

References 92 publications

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“…When we obtain all the predicted values x (t n ) corresponding to t n , we have to perform the backward propagation process. The first step is the construction of the loss function, as shown in equation (10),…”

Section: Model Training: Backward Propagation Of Neural Odementioning

confidence: 99%

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Monitoring robot machine tool sate via neural ODE and BP-GA

Zhu,

Zeng,

Gong

et al. 2023

Meas. Sci. Technol.

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Tool wear during robotic polishing affects material removal rates and surface roughness, leading to erratic and inconsistent polishing quality. Therefore, a method that can predict the tool state is needed to replace the robot end tool in time. In this paper, based on the cutting-edge neural ordinary differential equations (Neural ODE) and BP neural network optimization based on genetic algorithm (BP-GA), we propose a method to identify the tool state during robotic machining: firstly, a new training method of Neural ODE is proposed to avoid the model from falling into poor stationary points, and then on this basis, Neural ODE is utilized to predict the changes of vibration signals during robot machining; secondly, the predicted vibration signals of the tool are processed using variable modal decomposition method to extract the eigen kurtosis index and envelope entropy of the modal function as the vibration signal eigenvectors, and compare them with the traditional vibration signal eigenvectors. Finally, the predicted tool states were identified using BP-GA, and numerical experiments yielded an F1 score of 91.76% and an accuracy of 96.55% for model identification.

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Section: Model Training: Backward Propagation Of Neural Odementioning

confidence: 99%

“…The common nonlinear signal processing methods are mainly: empirical modal decomposition (EMD) and variational modal decomposition (VMD). EMD is capable of automatically processing non-smooth signals, and many scholars have applied it to the extraction of tool wear features [10]. However, its components are prone to modal confounding [11].…”

Section: Introductionmentioning

confidence: 99%

Monitoring robot machine tool sate via neural ODE and BP-GA

Zhu,

Zeng,

Gong

et al. 2023

Meas. Sci. Technol.

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“…Since tool wear is a key factor influencing the CFRP drilling operations, its monitoring and control are of vital importance to the current manufacturing sectors. Tool wear monitoring can be roughly divided into direct and indirect monitoring (Zhou et al, 2022). Direct measurements mainly involve the optical image method, the contact resistance method and the radiometric method.…”

Section: Wear Effects and Monitoringmentioning

confidence: 99%

“…However, these methods require a stoppage to detect the tool and have no real-time monitoring function. The measurement results are also affected by cutting fluids, lighting and chips (Zhou et al, 2022). In contrast, the indirect measurement method uses the effect of the state of the tool when it is worn or about to break with specific working parameters.…”

Section: Wear Effects and Monitoringmentioning

confidence: 99%

A review on tool wear issues in drilling CFRP laminates

2022

Front. Mater.

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Carbon fiber reinforced polymer (CFRP) laminates are featured by superior mechanical properties and excellent structural functions, which have been a promising alternative to conventional alloys and steels in diverse industrial fields. Mechanical drilling is a compulsory operation to shape fibrous composites to desired workpiece quality and target dimensional accuracy. Progressive tool wear is an extremely crucial issue when drilling CFRPs as it may result in undesired machining consequences such as increased force generation, elevated temperatures, and deteriorated surface quality. Despite great endeavors already made to address the wear issues, a complete review of the wear phenomena for CFRP drilling is still significantly lacking. The present paper aims to report the key research advances in tool wear aspects when drilling CFRPs. The wear phenomena, wear modes, and tool failure mechanisms are carefully reviewed. The effects of wear progression on various drilling outputs of CFRP laminates, such as drilling forces, cutting temperatures, and surface quality attributes, are discussed. A particular focus is placed on the quantification and assessment of tool wear during drilling CFRPs. Some research prospects for both academia and industry are outlined. The paper intends to offer a comprehensive understanding of wear mechanisms controlling the drilling of CFRP laminates.

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“…On the other hand, the techniques used for processing the information obtained for the identification, evaluation or prediction of cutting tool wear are varied. Several literature reviews have identified the main techniques, tools and trends for processing [21][22][23][24]. Firstly, signals are processed directly in the time domain [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] or techniques or transforms are used for their analyses in the frequency [3,5,[18][19][20] or time-frequency [19,20] domains.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

et al. 2023

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In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

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Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

Cited by 24 publications

References 92 publications

Monitoring robot machine tool sate via neural ODE and BP-GA

Monitoring robot machine tool sate via neural ODE and BP-GA

A review on tool wear issues in drilling CFRP laminates

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

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