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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

Paper No: GT2022-83414

DOI: https://doi.org/10.1115/GT2022-83414

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Topics: Metamodeling, Mode shapes, Turbine blades

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

Paper No: GT2022-83372

DOI: https://doi.org/10.1115/GT2022-83372

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Topics: Damage, Machine learning, Physics, Turbine components

Journal Articles

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

Guangqiang Xie, Zhaokun Zhang, Zhufeng Shao, Liping Wang

J. Mechanisms Robotics. June 2022, 14(3): 031003.

Paper No: JMR-21-1026

DOI: https://doi.org/10.1115/1.4052848

Published Online: November 16, 2021

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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

J. Mech. Des. February 2022, 144(2): 021706.

Paper No: MD-21-1275

DOI: https://doi.org/10.1115/1.4052301

Published Online: September 21, 2021

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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

Paper No: GT2021-59745

DOI: https://doi.org/10.1115/GT2021-59745

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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

Paper No: GT2021-58842

DOI: https://doi.org/10.1115/GT2021-58842

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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

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011106.

Paper No: RISK-21-1010

DOI: https://doi.org/10.1115/1.4051748

Published Online: August 9, 2021

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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

J. Manuf. Sci. Eng. February 2022, 144(2): 021007.

Paper No: MANU-21-1304

DOI: https://doi.org/10.1115/1.4051794

Published Online: August 5, 2021

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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

Paper No: MSEC2021-60227

DOI: https://doi.org/10.1115/MSEC2021-60227

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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

J. Mech. Des. July 2021, 143(7): 074502.

Paper No: MD-20-1623

DOI: https://doi.org/10.1115/1.4050246

Published Online: March 24, 2021

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Topics: Calibration, Modeling, Particle filtering (numerical methods), Algorithms, Chain, Particulate matter

Proceedings Papers

A New Kinematic Error Modelling and Identification Method of the Parallel Manipulator

Weitao Li, Liping Wang

Proc. ASME. IMECE2020, Volume 2B: Advanced Manufacturing, V02BT02A052, November 16–19, 2020

Paper No: IMECE2020-24438

DOI: https://doi.org/10.1115/IMECE2020-24438

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Topics: Errors, Kinematics, Manipulators, Modeling

Proceedings Papers

Design and Experimental Verification of a New Direct Cold Drawing Die for Section Rod From Round Bar

Liping Wang

Proc. ASME. DETC99, Volume 1: 25th Design Automation Conference, 521-528, September 12–16, 1999

Paper No: DETC99/DAC-8665

DOI: https://doi.org/10.1115/DETC99/DAC-8665

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Topics: Computer-aided design, Design, Finite element methods, Machining, Manufacturing, Metalworking, Rods, Strain gages

Proceedings Papers

Automatic Step-Size Procedure in Forward-Difference for Reliability and Design Optimization

Liping Wang, Kristy Gau

Proc. ASME. DETC99, Volume 1: 25th Design Automation Conference, 97-105, September 12–16, 1999

Paper No: DETC99/DAC-8603

DOI: https://doi.org/10.1115/DETC99/DAC-8603

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Topics: Design, Optimization, Reliability, Errors, Approximation, Event history analysis

Proceedings Papers

An Integrated CAD/CAM System for Design and Manufacturing of High-Curvature Surfaces

Liping Wang, Subramaniam S. Nair

Proc. ASME. DETC99, Volume 1: 25th Design Automation Conference, 677-684, September 12–16, 1999

Paper No: DETC99/DAC-8666

DOI: https://doi.org/10.1115/DETC99/DAC-8666

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Topics: Computer-aided design, Design, Manufacturing, Modeling, Molding

Proceedings Papers

Binbin Zhang, Liping Wang, Jun Wu

Proc. ASME. IMECE2019, Volume 4: Dynamics, Vibration, and Control, V004T05A083, November 11–14, 2019

Paper No: IMECE2019-10916

DOI: https://doi.org/10.1115/IMECE2019-10916

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Topics: Friction, Robots

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Journal citation

NARROW

Efficient Surrogate Modeling for Turbine Blade Row Cyclic Symmetric Mode Shapes

Automated Data-Driven Physics Discovery of Turbine Component Damage

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

Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning

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

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

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

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

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

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

A New Kinematic Error Modelling and Identification Method of the Parallel Manipulator

Design and Experimental Verification of a New Direct Cold Drawing Die for Section Rod From Round Bar

Automatic Step-Size Procedure in Forward-Difference for Reliability and Design Optimization

An Integrated CAD/CAM System for Design and Manufacturing of High-Curvature Surfaces

Low-Grade Heat Utilization Through Ultrasound-Enhanced Desorption of Activated Alumina/Water for Thermal Energy Storage

Study Tribological Properties of MoDTC and Its Interactions With Metal Detergents

Retrieval of Uniaxial Permittivity and Permeability for the Study of Near-Field Radiative Transport Between Metallic Nanowire Arrays

Advances in Bayesian Probabilistic Modeling for Industrial Applications

Workspace Analysis and Optimal Design of a Translational Cable-Driven Parallel Robot With Passive Springs

Dynamic Parameters and Friction Model Identification of an Industrial Hybrid Robot

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