Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

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Failure time prognosis in manufacturing process using multi-dislocated time series convolutional neural network

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/0954408920974141 ◽

2020 ◽

pp. 095440892097414

Author(s):

Bohao Li ◽

Liping Zhao ◽

Yiyong Yao

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Failure Time ◽

Great Difficulty ◽

Milling Process ◽

Machining Process ◽

Massive Data ◽

The Difference

Failure time prognosis in manufacturing process plays a crucial role in guaranteeing manufacturing safety and reducing maintenance loss. However, most current prognosis methods face great difficulty when handling massive data collected from manufacturing process. Convolutional neural network (CNN) provides an effective way to extract features with massive data. Due to the difference between images and multisensory signals, CNN is not suitable for machining process. Inspired by the idea of CNN, a novel prognosis framework is proposed based on the characteristics of multisensory signals, which is called multi-dislocated time series convolutional neural network (MDTSCNN). The proposed MDTSCNN is composed of multi-dislocate layer, convolutional layer, pooling layer and fully connected layer. By adding a multi-dislocate layer, this model can learn the relationship between different signals and different intervals in periodic multisensory signals. The effectiveness of proposed method is validated by a milling process. Compared to other prognosis method, the proposed MDTSCNN shows enhanced performances in prediction accuracy.

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Convolutional Neural Network Classification for Machine Tool Wear Based on Unsupervised Gaussian Mixture Model

10.1109/shircon53068.2021.9652266 ◽

2021 ◽

Author(s):

Victor A. Arias ◽

Juan Vargas-Machuca ◽

Fabio C. Zegarra ◽

Alberto M. Coronado

Keyword(s):

Neural Network ◽

Tool Wear ◽

Machine Tool ◽

Convolutional Neural Network ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Neural Network Classification

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Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

Journal of Sensors ◽

10.1155/2022/7693393 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Rui Yang ◽

Zenghui An ◽

Shijun Song

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Feature Recognition ◽

Optimization Problems ◽

Self Regulation ◽

Training Model ◽

Feedback Mechanism ◽

Model Parameters

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.

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An adaptive procedure for tool life prediction in face milling

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology ◽

10.1177/1350650111414332 ◽

2011 ◽

Vol 225 (11) ◽

pp. 1130-1136 ◽

Cited By ~ 6

Author(s):

D D Zhang

Keyword(s):

Tool Wear ◽

Prediction Model ◽

Tool Life ◽

Life Prediction ◽

Face Milling ◽

Accurate Prediction ◽

Machining Process ◽

Model Parameters ◽

Adaptive Procedure ◽

Life Prediction Model

Accurate prediction of tool life is essential to guarantee surface quality and economics of cutting operations in face milling. This article presents a procedure for tool life prediction through in-process adaptation of tool wear rate based on indirect measures. The procedure effectively accounts for the uncertainty of tool wear progress owing to the complexity of the machining process. First, sensor fusion of spindle motor current AC and DC portions is taken to estimate the actual tool wear through relevance vector machine. Then, a tool life prediction model relating flank wear with cutting time is proposed for tracking the progress of tool wear under certain cutting settings. Further, a recursive least square algorithm is developed to update the parameters of the tool life prediction model by considering the error between the predicted tool wear and the estimated tool wear. Finally, the updated model capturing the uncertainty of tool wear progress is used to predict tool life in face milling. Tool life experiments validate that the adaptive procedure can quickly track the progress of tool wear, and make more accurate prediction of tool life compared with the procedure with constant model parameters.

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KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

Jurnal Ilmiah Informatika Komputer ◽

10.35760/ik.2019.v24i3.2364 ◽

2019 ◽

Vol 24 (3) ◽

pp. 220-228

Author(s):

Gusti Alfahmi Anwar ◽

Desti Riminarsih

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Input Layer ◽

Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

2019 ◽

Vol 13 ◽

Author(s):

Niha Kamal Basha ◽

Aisha Banu Wahab

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Monitoring System ◽

Single Channel ◽

Confusion Matrix ◽

Sudden Change ◽

Automatic Detection ◽

Support Vector ◽

Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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