1-20 of 66
Liping Wang
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems

Ryan Jacobs, Sandipp Krishnan, Anindya Bhaduri, Lele Luan, Piyush Pandita, Sayan Ghosh, Liping Wang
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT25A004, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125838
https://doi.org/10.1115/GT2024-125838
View Paper titled, Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems
Open the PDF for Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems in another window
Topics: Aircraft propulsion, Design, Maintenance, Propulsion, Blades, Machine learning, Artificial neural networks, Cycles, Deep neural networks, Downtime
Journal Articles

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Accepted Manuscript
Anirban Samaddar, Sandipp Krishnan Ravi, Nesar Ramachandra, Lele Luan, Sandeep Madireddy, Anindya Bhaduri, Piyush Pandita, Changjie Sun, Liping Wang
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des.
Paper No: MD-24-1156
https://doi.org/10.1115/1.4066224
Published Online: August 16, 2024
View Article titled, Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields
Open the PDF for Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields in another window
Topics: Metamodeling, Modeling, Stress, Simulation, Tensors, Artificial intelligence, Artificial neural networks, Compression, Convolutional neural networks, Data-driven design
Journal Articles

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. May 2024, 24(5): 051008.
Paper No: JCISE-23-1360
https://doi.org/10.1115/1.4064622
Published Online: March 5, 2024
View Article titled, Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data
Open the PDF for Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data in another window
Topics: Computer-aided engineering, Convolutional neural networks, Shapes, Stress
Journal Articles

Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based on Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network

Jianhong Liang, Liping Wang, Guang Yu, Jun Wu, Dong Wang, Song Lin
Journal: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. June 2024, 10(2): 021102.
Paper No: RISK-23-1049
https://doi.org/10.1115/1.4064642
Published Online: February 28, 2024
View Article titled, Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based on Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network
Open the PDF for Indirect Prediction of Spindle Rotation Error Through Vibration Signal Based on Supervised Local Mean Decomposition Filter Fusion and Bi-LSTM Classification Network in another window
Topics: Errors, Rotation, Signals, Vibration
Journal Articles

Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study

Liping Wang, Qingwei Nie, Zequn Zhang, Dunbing Tang, Changchun Liu
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. April 2024, 24(4): 041008.
Paper No: JCISE-23-1325
https://doi.org/10.1115/1.4064409
Published Online: January 29, 2024
View Article titled, Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study
Open the PDF for Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study in another window
Topics: Energy consumption, Machinery, Manufacturing systems, Modeling, Negotiation, Workshops (Work spaces), Decision making, Manufacturing, Construction
Proceedings Papers

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A004, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-115077
https://doi.org/10.1115/DETC2023-115077
View Paper titled, Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data
Open the PDF for Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data in another window
Topics: Artificial neural networks, Shapes, Stress
Proceedings Papers

Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A011, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-110704
https://doi.org/10.1115/DETC2023-110704
View Paper titled, Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems
Open the PDF for Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems in another window
Topics: Artificial neural networks, Machine learning, Metamodeling, Modeling
Proceedings Papers

Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming

Lele Luan, Ryan Jacobs, Sayan Ghosh, Liping Wang
Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT25A005, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-104043
https://doi.org/10.1115/GT2023-104043
View Paper titled, Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming
Open the PDF for Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming in another window
Topics: Computer programming, Engineering systems and industry applications, Machine learning, Optimization, Physics
Proceedings Papers

Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components

Panagiotis Tsilifis, Sandipp Krishnan Ravi, Feng Zhang, Piyush Pandita, Anindya Bhaduri, Ambarish Kulkarni, Liping Wang
Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT25A002, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-100814
https://doi.org/10.1115/GT2023-100814
View Paper titled, Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components
Open the PDF for Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components in another window
Topics: Damage assessment, Jet engines, Machinery, Redundancy (Engineering)
Proceedings Papers

Determination of Flat-End Cutter Orientation in 5-Axis Machining

Zhongping Deng, Ming C. Leu, Liping Wang, Denis Blackmore
Proc. ASME. IMECE96, Manufacturing Science and Engineering, 73-80, November 17–22, 1996
Publisher: American Society of Mechanical Engineers
Paper No: IMECE1996-0777
https://doi.org/10.1115/IMECE1996-0777
View Paper titled, Determination of Flat-End Cutter Orientation in 5-Axis Machining
Open the PDF for Determination of Flat-End Cutter Orientation in 5-Axis Machining in another window
Topics: Machining, Errors, Yaw, Algorithms
Proceedings Papers

Automated Data-Driven Physics Discovery of Turbine Component Damage

Sheng Zhang, Ryan Jacobs, Sayan Ghosh, Ambarish Kulkarni, Liping Wang
Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A008, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-83372
https://doi.org/10.1115/GT2022-83372
View Paper titled, Automated Data-Driven Physics Discovery of Turbine Component Damage
Open the PDF for Automated Data-Driven Physics Discovery of Turbine Component Damage in another window
Topics: Damage, Machine learning, Physics, Turbine components
Proceedings Papers

Efficient Surrogate Modeling for Turbine Blade Row Cyclic Symmetric Mode Shapes

Anindya Bhaduri, Jing Li, Sayan Ghosh, Liping Wang
Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A009, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-83414
https://doi.org/10.1115/GT2022-83414
View Paper titled, Efficient Surrogate Modeling for Turbine Blade Row Cyclic Symmetric Mode Shapes
Open the PDF for Efficient Surrogate Modeling for Turbine Blade Row Cyclic Symmetric Mode Shapes in another window
Topics: Metamodeling, Mode shapes, Turbine blades
Journal Articles

Research on the Orientation Error of the Translational Cable-Driven Parallel Robots

Guangqiang Xie, Zhaokun Zhang, Zhufeng Shao, Liping Wang
Journal: Journal of Mechanisms and Robotics
Publisher: ASME
Article Type: Research Papers
J. Mechanisms Robotics. June 2022, 14(3): 031003.
Paper No: JMR-21-1026
https://doi.org/10.1115/1.4052848
Published Online: November 16, 2021
View Article titled, Research on the Orientation Error of the Translational Cable-Driven Parallel Robots
Open the PDF for Research on the Orientation Error of the Translational Cable-Driven Parallel Robots in another window
Topics: Cables, End effectors, Errors, Robots, Kinematics
Journal Articles

Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning

Sayan Ghosh, Govinda Anantha Padmanabha, Cheng Peng, Valeria Andreoli, Steven Atkinson, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. February 2022, 144(2): 021706.
Paper No: MD-21-1275
https://doi.org/10.1115/1.4052301
Published Online: September 21, 2021
View Article titled, Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning
Open the PDF for Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning in another window
Topics: Design, Modeling, Machine learning, Blades, Computational fluid dynamics
Proceedings Papers

Bayesian Optimization for Multi-Objective High-Dimensional Turbine Aero Design

Yiming Zhang, Sayan Ghosh, Thomas Vandeputte, Liping Wang
Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT27A005, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-59745
https://doi.org/10.1115/GT2021-59745
View Paper titled, Bayesian Optimization for Multi-Objective High-Dimensional Turbine Aero Design
Open the PDF for Bayesian Optimization for Multi-Objective High-Dimensional Turbine Aero Design in another window
Topics: Design, Optimization, Turbines
Proceedings Papers

A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

Sayan Ghosh, Valeria Andreoli, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT27A003, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-58842
https://doi.org/10.1115/GT2021-58842
View Paper titled, A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades
Open the PDF for A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades in another window
Topics: Design, Industrial gases, Machine learning, Turbine blades
Journal Articles

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

Piyush Pandita, Sayan Ghosh, Vipul K. Gupta, Andrey Meshkov, Liping Wang
Journal: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011106.
Paper No: RISK-21-1010
https://doi.org/10.1115/1.4051748
Published Online: August 9, 2021
View Article titled, Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion
Open the PDF for Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion in another window
Topics: Additive manufacturing, Design, Modeling, Simulation, Uncertainty quantification, Lasers, Computers, Physics
Journal Articles

Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes

Liping Wang, Weitao Li, Guang Yu
Journal: Journal of Manufacturing Science and Engineering
Publisher: ASME
Article Type: Research Papers
J. Manuf. Sci. Eng. February 2022, 144(2): 021007.
Paper No: MANU-21-1304
https://doi.org/10.1115/1.4051794
Published Online: August 5, 2021
View Article titled, Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes
Open the PDF for Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes in another window
Topics: Construction, Dynamic models, Milling, Stability, Chatter
Proceedings Papers

Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes

Weitao Li, Liping Wang, Guang Yu
Proc. ASME. MSEC2021, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T05A002, June 21–25, 2021
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-60227
https://doi.org/10.1115/MSEC2021-60227
View Paper titled, Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes
Open the PDF for Time Domain Study on the Construction Mechanism of Milling Stability Lobe Diagrams With Multiple Modes in another window
Topics: Chatter, Construction, Milling, Stability
Journal Articles

Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications

Piyush Pandita, Panagiotis Tsilifis, Sayan Ghosh, Liping Wang
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Technical Briefs
J. Mech. Des. July 2021, 143(7): 074502.
Paper No: MD-20-1623
https://doi.org/10.1115/1.4050246
Published Online: March 24, 2021
View Article titled, Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications
Open the PDF for Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications in another window
Topics: Calibration, Modeling, Particle filtering (numerical methods), Algorithms, Chain, Particulate matter