Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

Lin, Cheng‐Jian; Jhang, Jyun-Yu; Chen, Shao Hsien; Young, Kuu‐Young

doi:10.1109/access.2020.3006849

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Recently, fuzzy neural networks (FNNs) [35][36][37][38][39] that have a human-like fuzzy inference mechanism and the powerful learning functions of neural networks have been widely used in various fields, such as classification, control, and forecasting. Asim et al [35] applied an adaptive networkbased fuzzy inference system to classification problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared with traditional neural networks, this method yielded higher classification accuracy. Lin et al [36] used an interval type-2 FNN and tool chips to predict flank wear, and their method yielded superior prediction results. A few researchers have used a locally recurrent functional link fuzzy neural network [37] and Takagi-Sugeno-Kang-type FNNs [38][39] to solve system identification and prediction problems, and both methods have yielded good results.…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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“…Generally, prediction methods based on deep learning models have the capability to directly learn features from input datasets, which may not make full use of extensive expert knowledge. The adaptive network based fuzzy inference system (ANFIS) proposed by Jang et al [27] combined adaptive learning ability of artificial neural network (ANN) with knowledge expression ability of FIS and can effectively use expert knowledge to deal with complex problems, such as fault diagnosis [28][29][30]. However, tool wear is a time-dependent dynamic process, whose input-output modes are difficult to be fully recognized only by static ANFI.…”

Section: Introductionmentioning

confidence: 99%

“…29) where T i and P i are the true and predicted tool wear features, respectively. The errors of tool state and RUL prediction are shown in TableIand Table II.…”

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confidence: 99%

Multi-Step-Ahead Tool State Monitoring Using Clustering Feature-Based Recurrent Fuzzy Neural Networks

Yao

Zhang

2021

IEEE Access

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Reliable and precise multi-step-ahead tool wear state prediction is significant to modern industries for maintaining part quality and reducing cost. This study proposes a Clustering Feature-based Recurrent Fuzzy Neural Network (CFRFNN) for tool wear state monitoring and remaining useful life (RUL) prediction based on K-means Clustering, Recurrent Fuzzy Neural Network (RFNN) and Genetic Algorithm (GA). K-means Clustering method is utilized to realize tool wear state definition and input signal division, which reduces the dependence on the prior knowledge of tool wear degree and improves the prediction accuracy. Then, an enhanced RFNN model is designed and applied on the clustered features to predict tool wear state. The optimized GA technique is helpful for adaptive optimization of model parameters, which significantly improves convergence rate and prediction accuracy. The experiments on tool state prediction are performed to validate superiority of CFRFNN, and the results demonstrate that the proposed network could reasonably configure the complex non-stationary tool wear process and have high prediction accuracy of tool wear state.INDEX TERMS Multi-step-ahead tool state prediction, remaining useful life (RUL), recurrent fuzzy neural network (RFNN), tool wear monitoring.

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“…To acquire the global optimum solution, many studies adopted evolutionary computation methods to optimize network parameters. The evolutionary algorithms include differential evolution (DE), (16,17) particle swarm optimization (PSO), (18) artificial bee colony (ABC), (19) genetic algorithm (GA), (20) and whale optimization algorithm (WOA). (21) Compared with other evolutionary algorithms, DE has the advantages of rapid convergence, fewer parameters, and a simple structure.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Prediction and Parameter Selection for Grinding Process with Computer Numerical Control

Lin¹,

Jhang²,

Huang³

et al. 2021

Sensors and Materials

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We propose a novel intelligent grinding assistance system (IGAS) for the grinding of silicon carbide (SiC) with computer numerical control (CNC). The proposed IGAS predicts surface roughness (Ra) and suggests suitable parameters for the grinding process. To establish the Ra prediction model, a type-2 functional-link-based fuzzy neural network (T2FLFNN), which updates the network parameter by Lévy-based dynamic group differential evolution (LDGDE), is developed. The LDGDE includes the Lévy flight and dynamic group mechanism to improve the shortcomings of the traditional differential evolution (DE) algorithm. Subsequently, DE is adopted to optimize the grinding parameters according to user requirements. Experimental results of practical machining show that the mean absolute percentage error (MAPE) using the IGAS is as low as 1.62%. Therefore, the proposed IGAS can provide suitable grinding parameters according to the requirements of users.

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Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

Cited by 5 publications

References 24 publications

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Multi-Step-Ahead Tool State Monitoring Using Clustering Feature-Based Recurrent Fuzzy Neural Networks

Surface Roughness Prediction and Parameter Selection for Grinding Process with Computer Numerical Control

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