Topics: Artificial neural networks , Errors , Machine tools , Optimization , Particle swarm optimization , Particulate matter , Temperature , Modeling , Neural network models

Dilated Convolution Neural Network for Remaining Useful Life Prediction

Xin Xu, Qianhui Wu, Xiu Li, Biqing Huang

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021004. doi: https://doi.org/10.1115/1.4045293

Abstract

View articletitled, Dilated Convolution Neural Network for Remaining Useful Life Prediction

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Topics: Artificial neural networks , Filters , Sensors , Service life (Equipment) , Feature extraction

A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification

Tianyuan Liu, Jinsong Bao, Junliang Wang, Yiming Zhang

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021005. doi: https://doi.org/10.1115/1.4045294

Abstract

View articletitled, A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification

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Topics: Algorithms , Artificial intelligence , Artificial neural networks , Engineering systems and industry applications , Machine learning , Machinery , Testing , Weight (Mass) , Welding

An Integrated Target Acquisition Approach and Graphical User Interface Tool for Parallel Manipulator Assembly

Haodong Chen, Zhiqiang Teng, Zheng Guo, Ping Zhao

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021006. doi: https://doi.org/10.1115/1.4045411

Abstract

View articletitled, An Integrated Target Acquisition Approach and Graphical User Interface Tool for Parallel Manipulator Assembly

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Topics: Algorithms , Collisions (Physics) , Graphical user interfaces , Machine components , Machinery , Manipulators , Manufacturing , Robots , Shapes , Support vector machines

Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network

Yonghe Su, Fei Tao, Jian Jin, Tian Wang, Qingguo Wang, Lei Wang

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021007. doi: https://doi.org/10.1115/1.4045445

Abstract

View articletitled, Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network

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Topics: Artificial neural networks , Bearings , Failure , Signals , Vibration , Simulation , Machine learning

A Simulation Data-Driven Design Approach for Rapid Product Optimization

Yanli Shao, Huawei Zhu, Rui Wang, Ying Liu, Yusheng Liu

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021008. doi: https://doi.org/10.1115/1.4045527

Abstract

View articletitled, A Simulation Data-Driven Design Approach for Rapid Product Optimization

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Topics: Algorithms , Design , Optimization , Simulation , Data mining , Optimization algorithms , Weight (Mass)

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

Ming-Chuan Chiu, Chien-De Tsai, Tung-Lung Li

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021009. doi: https://doi.org/10.1115/1.4045663

Abstract

View articletitled, An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

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Topics: Flaw detection , Machine learning , Machinery , Maintenance , Printing , Sensors , Time series , Algorithms , Engineers , Manufacturing

Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing

V. Akhil, G. Raghav, N. Arunachalam, D. S. Srinivas

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021010. doi: https://doi.org/10.1115/1.4045719

Abstract

View articletitled, Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing

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Topics: Additive manufacturing , Surface roughness , Surface texture , Texture (Materials) , Machine learning , Lasers , Artificial neural networks , Statistics

Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes

Changxuan Zhao, Mahmoud Dinar, Shreyes N. Melkote

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021011. doi: https://doi.org/10.1115/1.4045410

Abstract

View articletitled, Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes

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Topics: Manufacturing , Shapes , Design

NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation

Ali Al-Dulaimi, Soheil Zabihi, Amir Asif, Arash Mohammed

J. Comput. Inf. Sci. Eng. April 2020, 20(2): 021012. doi: https://doi.org/10.1115/1.4045491

Abstract

View articletitled, NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation

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Topics: Artificial neural networks , Service life (Equipment) , NASA , Robustness , Architecture , Noise (Sound)

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Special Issue: Machine Learning Applications in Manufacturing Part I

Guest Editorial

Special Issue: Machine Learning Applications in Manufacturing

Research Papers

Ferrograph Analysis With Improved Particle Segmentation and Classification Methods

Data-Driven Predictive Modeling of Tensile Behavior of Parts Fabricated by Cooperative 3D Printing

Thermal Error Modeling of Feed Axis in Machine Tools Using Particle Swarm Optimization-Based Generalized Regression Neural Network

Dilated Convolution Neural Network for Remaining Useful Life Prediction

A Coarse-Grained Regularization Method of Convolutional Kernel for Molten Pool Defect Identification

An Integrated Target Acquisition Approach and Graphical User Interface Tool for Parallel Manipulator Assembly

Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network

A Simulation Data-Driven Design Approach for Rapid Product Optimization

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing

Automated Classification of Manufacturing Process Capability Utilizing Part Shape, Material, and Quality Attributes

NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation

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Special Issue: Machine Learning Applications in Manufacturing Part I

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