A cyber-physical scheme for predicting tool wear based on a hybrid dynamic neural network

Yang, Haw‐Ching; Li, Yu Yung; Hung, Min Hsiung; Cheng, Fan‐Tien

doi:10.1080/02533839.2017.1372223

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…In 2017, Hung et al [8] provided a feasible systematic solution to effectively correct the MC related limitations of the existing AVM system, as the existing VM-related literature mainly focused on the development of the VM method. In 2017, Yang et al [9] proposed a tool cyber-physical prediction (TCPP) method and a hybrid dynamic neural network (HDNN) method. By integrating the theoretical maximum tool (cutter) life and actual tool (cutter) wear sensing functions, this solution enables users to build and use cloud methods that can be applied to factory machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang

Zhang

et al. 2021

Applied Sciences

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One of the important values of Industry 4.0 is to integrate people’s needs into the manufacture of enhanced products, systems, and services to achieve greater levels of product customization. This paper presents a prediction method for predicting screw process parameters; taking crystalline and non-crystalline polymers as the molding material, when there is a lack of sufficient historical screw process data to establish a data-driven method, using various screws and polymer materials to predict tool life under different cutting conditions is a challenge. A screw life prediction method is proposed based on the mixed compound screw process parameters method using a dynamic iteration work. To meet the requirements of mass production, this work proposes the combined application of the automatic virtual metrology (AVM) system with the recognizable performance evaluation (RPE) program. The method predicts the injection of compound screws by extracting given cutting conditions and related process parameters characteristics from the senor data by converting sampling inspections with measurement delays from real-time and online routine inspections to automatically and quickly complete method creation production goals.

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Section: Literature Reviewmentioning

confidence: 99%

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang

Zhang

et al. 2021

Applied Sciences

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“…Multivariate time series (MTS) data accessible from various sources may be used to determine the operating status or health of the devices. Yang et al [11] developed a binary classification model using a machine-learning algorithm to classify defects from collected time-series data to improve production quality in smart manufacturing. Wang et al [12] categorize the current Time-Series Classification approaches from various perspectives.…”

Section: Literature Reviewmentioning

confidence: 99%

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Kalyani¹,

Rao²,

Sowjanya³

2021

Structural Durability &Amp; Health Monitoring

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“…Multivariate time series (MTS) data from multiple resources can be used to present the operating statuses of the machines, or human health condition such as electrocardiography. In smart manufacturing, building a binary classification model by machine learning algorithm to identify defects or tool wearing (normal or abnormal) from the collected time series data is also a popular approach to improve production quality [1].…”

Section: Introductionmentioning

confidence: 99%

“…An MTS can be considered as a m × n matrix. Generally, MTS data mining research can be categorized into: (1) representation and indexing, (2) similarity measure, (3) segmentation, (4) visualization, and (5) mining [3]. Essentially, MTS classification belongs to a "mining" area that tries to categorize multiple time series as class labels [4].…”

Section: Introductionmentioning

confidence: 99%

Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images

Yang

Chen

Yang

2019

Sensors

124

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This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet). This study applied three transformation methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF). Two open multivariate datasets were used to evaluate the impact of using different transformation methods, the sequences of concatenating images, and the complexity of ConvNet architectures on classification accuracy. The results show that the selection of transformation methods and the sequence of concatenation do not affect the prediction outcome significantly. Surprisingly, the simple structure of ConvNet is sufficient enough for classification as it performed equally well with the complex structure of VGGNet. The results were also compared with other classification methods and found that the proposed framework outperformed other methods in terms of classification accuracy.

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A cyber-physical scheme for predicting tool wear based on a hybrid dynamic neural network

Cited by 9 publications

References 15 publications

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images

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