scholarly journals A Research of Tool Wear Recognizing Based on Wavelet Packet Pretreated and Neural Network

2007 ◽  
Vol 1 (4) ◽  
pp. 760-770 ◽  
Author(s):  
Xu CHUANGWEN ◽  
Chen HUALING
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Wavelet Packet

Tool Wear Monitoring in Turning Operation Using Vibration and Strain Measurement With Neural Network

2005 ◽  
Author(s):  
Nabeel Kadim Abid Al-Sahib ◽  
Aimen Mohammed Bachaa
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Wavelet Packet ◽  
Wear Test ◽  
Work Piece ◽  
Manufacturing Processes ◽  
Self Organizing Map ◽  
Data Acquisition Card ◽  
The Time Domain ◽  
Coated Carbide

The selection of appropriate monitoring processes is an important decision to be made. Monitoring of manufacturing processes plays a very important role to avoid down time of the machine, or prevent unwanted conditions such as chatter, excessive tool wear or breakage. Most monitoring systems developed up to date employ force, acoustic emission and vibration, or a combination of these and other techniques with a sensor integration strategy. In this work, the implementation of a monitoring system utilizing simultaneous vibration and strain measurements on the tool tip is investigated for the average flank wear of coated carbide tools which are used in finishing turning process, with cast iron shaft as a work-piece. Data from the manufacturing processes were recorded with one piezoelectric strain sensor and an accelerometer, each coupled to the data acquisition card. There are 24 features indicative of tool wear were extracted from the original signal. These include features from the time domain, frequency domains, time-series model coefficients and four packet features extracted from wavelet packet analysis. The (2 × 3) self organizing map (SOM) neural network was employed to identify the tool state as a result of the present work, the tool wear classified by applied two independent wear test as a training tests, and checked the results of the SOM by applied an independent test, finally we have an SOM model can classifying the tool wear with minimal error.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

Structural Damage Identification Using Wavelet Packet Analysis and Neural Network

2006 ◽  
Vol 324-325 ◽  
pp. 205-208
Author(s):  
Qing Guo Fei ◽  
Ai Qun Li ◽  
Chang Qing Miao ◽  
Zhi Jun Li
Keyword(s):  
Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Wavelet Packet ◽  
Uniform Design ◽  
Network Models ◽  
Sampling Technique ◽  
Wavelet Packet Analysis ◽  
Neural Network Models ◽  
Network Training

This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.

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