Effective training data selection in tool condition monitoring system

Sun, Junying; Gao, Hong; Wong, Yoke San; Rahman, M.; Wang, Z.G.

doi:10.1016/j.ijmachtools.2005.04.005

Cited by 37 publications

(20 citation statements)

References 11 publications

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“…The possibility of reliable tool wear evaluation based on one signal feature (SF) has been questioned because the feature may offer incomplete or randomly distorted information about the condition of a cutting tool. Attempts at rectifying these shortcomings have focused primarily on pursuing a multi-sensor fusion strategy, which can be achieved by various means, such as statistical methods, auto-regressive modeling, pattern recognition, expert systems, and others [6,7]. Recently, the neural network (NN) approach has been the most intensively studied method for the feature fusion.…”

Section: Introductionmentioning

confidence: 99%

Application of AE and cutting force signals in tool condition monitoring in micro-milling

Jemielniak

Arrazola

2008

CIRP Journal of Manufacturing Science and Technology

136

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Section: Introductionmentioning

confidence: 99%

Application of AE and cutting force signals in tool condition monitoring in micro-milling

Jemielniak

Arrazola

2008

CIRP Journal of Manufacturing Science and Technology

136

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“…It has the basic ability to learn, memorize, and generalize. (11)(12)(13)(14)(15)(16) Among the many types of neural network, the back propagation neural network is the core of all the others and has the following four advantages: (1) function approximation: train the input vector and correspondent output vector to a model and establish function approximation; (2) model identification: link an undetermined output vector with an input vector, thereby achieving the effect of discrimination; (3) classification: define and classify the type of input vector; and (4) data compression: reduce the dimension of the input vector for convenient transfer and storage.…”

Section: Back Propagation Neural Network For Mtdsmentioning

confidence: 99%

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

2015

Sensors and Materials

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Key words: MEMS sensor, back propagation neural network, machine tool diagnostic systemIn this study, micro-electromechanicals (MEMS) system sensors were used to extract physical signals from a machine tool. A performance assessment was carried out using fuzzy logic theory. This, in turn, was used to judge the ownership of various signals. It then defined the level that each ownership group belonged to in order to determine whether the performance was broken or not. If normal, the system determined specified rules for such normality. During the process of extracting the vibration and noise signals, the system used Fourier transform to analyze any changes made to each signal in the frequency field, and then principal component analysis was used to decrease the data dimensions. We then evaluated the work status of the machine tool on the basis of the signal features. Furthermore, we built a feature math model from the recorded signals using a back propagation neural network and further determined the abnormal items using an error function. Finally, we obtained a diagnostic feature for the performance of the machine tool using physical signals through diagnostic reports from a humanmachine interface. The machine tool diagnostic system is able to provide maintenance personnel with a proper way of responding quickly to any reduction in the output from the machine tool so as to avoid further damage.

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“…4 One solution for training the neural network using large dataset is to to select training data from large training dataset. [5][6][7][8] Considering the solution, our training dataset is partitioned into several sub-datasets so that one network can train using one sub-dataset. Multiple neural networks are generated by using their corresponding sub-datasets, and form the simulator of the stomach deformation.…”

Section: Introducitonmentioning

confidence: 99%

A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

Morooka¹,

Taguchi²,

Xian³

et al. 2012

SPIE Proceedings

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This paper presents a method for simulating the behavior of stomach with large-scale deformation. This simulator is generated by the real-time FEM-based analysis by using a neural network. 4 There are various deformation patterns of hollow organs by changing both its shape and volume. In this case, one network can not learn the stomach deformation with a huge number of its deformation pattern. To overcome the problem, we propose a method of constructing the simulator composed of multiple neural networks by 1)partitioning a training dataset into several subsets, and 2)selecting the data included in each subset. From our experimental results, we can conclude that our method can speed up the training process of a neural network while keeping acceptable accuracy. INTRODUCITONIn the last decades, there have been developing many Virtual Reality (VR) based systems for supporting surgeons in minimally invasive surgery. The systems use volumetric tissue models to show the virtual images of the inside of a virtual patients' body. Examples of such VR-based systems include a training simulator for endoscopic surgery. 1 The simulator gives surgeons the opportunity of training in endoscopic surgical techniques for various surgery cases. Another application using the model is a navigator for endoscopic surgery. 2 The navigator shows virtual images by merging the models of target tissues into real endoscopic images. The tissue models contain the internal information of the tissues such as the presence of blood vessels and tumors which exist in the tissues. Such information can never be obtained from the endoscopic images. Therefore, the use of the virtual images is a powerful tool for surgeons to approach the tissues or tumors safely and accurately.One of the fundamental components of the support system using the tissue models is to display realistic deformations of tissue models according to their biomechanical behavior. Methods have been proposed for simulating the behavior of soft tissues. 1, 3, 4 Among these methods, the finite element method (FEM) is a well-known technique for accurately modeling the behaviors of continuous objects. Compared with other simulation techniques, FEM can achieve a more physically realistic simulation for deformable objects with linear and nonlinear material properties. On the contrary, the FEM-based simulation of tissue deformation is very time-consuming owing to the need to solve large scale system of equations. Particularly, in the case of soft tissues, the simulation is very complex because the soft tissues have the properties of anisotropy, inhomogeneity, and large-range deformation.To solve these problems, Morooka et al. 4 proposed a real-time FEM-based simulator for deforming a soft tissue model by using a neural network. To construct the simulator, the training dataset is generated by collecting many training data. Each data is a pair of an external force caused by the contact between virtual surgical instruments and tissue models and

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Effective training data selection in tool condition monitoring system

Cited by 37 publications

References 11 publications

Application of AE and cutting force signals in tool condition monitoring in micro-milling

Application of AE and cutting force signals in tool condition monitoring in micro-milling

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

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