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Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A030, June 10–14, 2019

Paper No: MSEC2019-2976

DOI: https://doi.org/10.1115/MSEC2019-2976

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Topics: Additive manufacturing, Data fusion, Process monitoring, Surface roughness

Proceedings Papers

Surface Roughness Prediction in Additive Manufacturing Using Machine Learning

Dazhong Wu, Yupeng Wei, Janis Terpenny

Proc. ASME. MSEC2018, Volume 3: Manufacturing Equipment and Systems, V003T02A018, June 18–22, 2018

Paper No: MSEC2018-6501

DOI: https://doi.org/10.1115/MSEC2018-6501

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Proceedings Papers

Scalable Detection of Defects in Additively Manufactured PLA Components

Amol Kulkarni, Amey Vidvans, Mustafa Rifat, Gregory Bicknell, Xi Gong, Guha Manogharan, Janis Terpenny, Saurabh Basu

Proc. ASME. MSEC2018, Volume 1: Additive Manufacturing; Bio and Sustainable Manufacturing, V001T01A042, June 18–22, 2018

Paper No: MSEC2018-6623

DOI: https://doi.org/10.1115/MSEC2018-6623

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Topics: Additive manufacturing, Rotation, Imaging, Lasers, Resolution (Optics)

Journal Articles

Cloud-Based Parallel Machine Learning for Tool Wear Prediction

Dazhong Wu, Connor Jennings, Janis Terpenny, Soundar Kumara, Robert X. Gao

J. Manuf. Sci. Eng. April 2018, 140(4): 041005.

Paper No: MANU-17-1344

DOI: https://doi.org/10.1115/1.4038002

Published Online: February 12, 2018

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Topics: Algorithms, Machine learning, Wear, Manufacturing, Milling, Cloud computing, Cutting, Sensors, Vibration

Proceedings Papers

Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

Zhixiong Li, Dazhong Wu, Chao Hu, Janis Terpenny, Sheng Shen

Proc. ASME. IDETC-CIE2017, Volume 2A: 43rd Design Automation Conference, V02AT03A045, August 6–9, 2017

Paper No: DETC2017-68315

DOI: https://doi.org/10.1115/DETC2017-68315

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Topics: Aircraft engines, Algorithms, Engines, Service life (Equipment), Weight (Mass)

Proceedings Papers

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

Dazhong Wu, Connor Jennings, Janis Terpenny, Robert Gao, Soundar Kumara

Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A048, June 4–8, 2017

Paper No: MSEC2017-2679

DOI: https://doi.org/10.1115/MSEC2017-2679

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Topics: Wear, Artificial neural networks, Manufacturing systems, Artificial intelligence, Algorithms, Failure, Machine learning, Machinery, Maintenance, Manufacturing

Journal Articles

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Dazhong Wu, Connor Jennings, Janis Terpenny, Robert X. Gao, Soundar Kumara

J. Manuf. Sci. Eng. July 2017, 139(7): 071018.

Paper No: MANU-16-1567

DOI: https://doi.org/10.1115/1.4036350

Published Online: April 18, 2017

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Topics: Algorithms, Artificial neural networks, Wear, Machine learning

Journal Articles

Immersive Distributed Design Through Real-Time Capture, Translation, and Rendering of Three-Dimensional Mesh Data

Kevin Lesniak, Janis Terpenny, Conrad S. Tucker, Chimay Anumba, Sven G. Bilén

J. Comput. Inf. Sci. Eng. September 2017, 17(3): 031010.

Paper No: JCISE-16-2084

DOI: https://doi.org/10.1115/1.4035001

Published Online: February 16, 2017

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Topics: Algorithms, Rendering, Sensors, Virtual reality, Design

Proceedings Papers

Networked, Real Time Translation of 3D Mesh Data to Immersive Virtual Reality Environments

Kevin Lesniak, Conrad S. Tucker, Sven Bilen, Janis Terpenny, Chimay Anumba

Proc. ASME. IDETC-CIE2016, Volume 1B: 36th Computers and Information in Engineering Conference, V01BT02A053, August 21–24, 2016

Paper No: DETC2016-59762

DOI: https://doi.org/10.1115/DETC2016-59762

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Topics: Virtual reality, Algorithms, Computer-aided design, Computers, Design, Engineering prototypes, Internet, Rendering, Sensors, Teams

Proceedings Papers

A Survey of Cloud-Based Design and Engineering Analysis Software Tools

Dazhong Wu, Janis Terpenny, Dirk Schaefer

Proc. ASME. IDETC-CIE2016, Volume 1A: 36th Computers and Information in Engineering Conference, V01AT02A016, August 21–24, 2016

Paper No: DETC2016-59341

DOI: https://doi.org/10.1115/DETC2016-59341

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Topics: Computer software, Design, Internet, Thread

Proceedings Papers

Forecasting Obsolescence Risk Using Machine Learning

Connor Jennings, Dazhong Wu, Janis Terpenny

Proc. ASME. MSEC2016, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A033, June 27–July 1, 2016

Paper No: MSEC2016-8625

DOI: https://doi.org/10.1115/MSEC2016-8625

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Topics: Machine learning, Risk, Bills of materials, Design, Electronics, Innovation, Product design

Proceedings Papers

Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems

Dazhong Wu, Janis Terpenny, Li Zhang, Robert Gao, Thomas Kurfess

Proc. ASME. MSEC2016, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A032, June 27–July 1, 2016

Paper No: MSEC2016-8559

DOI: https://doi.org/10.1115/MSEC2016-8559

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Topics: Manufacturing, Machinery, Internet of things, Maintenance, Cloud computing, Computer software, Cyber-physical systems, Data storage systems, Failure, Machine learning

Proceedings Papers

Performance Evaluation of Cloud-Based High Performance Computing for Finite Element Analysis

Dazhong Wu, Xi Liu, Steve Hebert, Wolfgang Gentzsch, Janis Terpenny

Proc. ASME. IDETC-CIE2015, Volume 1A: 35th Computers and Information in Engineering Conference, V01AT02A043, August 2–5, 2015

Paper No: DETC2015-46381

DOI: https://doi.org/10.1115/DETC2015-46381

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Topics: Finite element analysis, Performance evaluation, Cloud computing, Computer-aided engineering, Simulation, Bridges (Structures)

Journal Articles

Economic Benefit Analysis of Cloud-Based Design, Engineering Analysis, and Manufacturing

Dazhong Wu, Janis Terpenny, Wolfgang Gentzsch

J. Manuf. Sci. Eng. August 2015, 137(4): 040903.

Paper No: MANU-14-1607

DOI: https://doi.org/10.1115/1.4030306

Published Online: August 1, 2015

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Topics: Cloud computing, Computer software, Design, Economics , Manufacturing

Proceedings Papers

A Study of Correlation Between DMSMS Obsolescence and Counterfeit Electronics

Liyu Zheng, Nihal Orfi, Janis Terpenny

Proc. ASME. IDETC-CIE2014, Volume 1B: 34th Computers and Information in Engineering Conference, V01BT02A005, August 17–20, 2014

Paper No: DETC2014-35424

DOI: https://doi.org/10.1115/DETC2014-35424

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Topics: Electronics, Aircraft, Avionics, Decision making, Governments, Manufacturing, Military systems, Risk, Ships, Testing

Proceedings Papers

Ontology-Enabled Data Inquiry for Cost Analysis and Obsolescence Mitigation

Xiaomeng Chang, Liyu Zheng, Janis Terpenny

Proc. ASME. IDETC-CIE2013, Volume 2B: 33rd Computers and Information in Engineering Conference, V02BT02A006, August 4–7, 2013

Paper No: DETC2013-12251

DOI: https://doi.org/10.1115/DETC2013-12251

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Topics: Ontologies

Proceedings Papers

Ontology-Based Malfunction Forecast and Root Cause Analysis in Product Design

Xiaomeng Chang, Janis Terpenny

Proc. ASME. IDETC-CIE2012, Volume 2: 32nd Computers and Information in Engineering Conference, Parts A and B, 937-945, August 12–15, 2012

Paper No: DETC2012-70754

DOI: https://doi.org/10.1115/DETC2012-70754

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Topics: Ontologies, Product design, Root cause analysis, Errors, Design, Cycles, Knowledge management

Proceedings Papers

Design Refresh Planning Models for Managing Obsolescence

Liyu Zheng, Janis Terpenny, Peter Sandborn, Raymond Nelson, III

Proc. ASME. IDETC-CIE2012, Volume 5: 6th International Conference on Micro- and Nanosystems; 17th Design for Manufacturing and the Life Cycle Conference, 579-590, August 12–15, 2012

Paper No: DETC2012-70743

DOI: https://doi.org/10.1115/DETC2012-70743

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Topics: Cycles, Design, Uncertainty

Journal citation

NARROW

Constructing Robust and Reliable Health Indices and Improving the Accuracy of Remaining Useful Life Prediction

A Survey of Methods and Approaches to Knowledge Management in the Product Development Environment

A Decision-Level Data Fusion Approach to Surface Roughness Prediction

Surface Roughness Prediction in Additive Manufacturing Using Machine Learning

Scalable Detection of Defects in Additively Manufactured PLA Components

Cloud-Based Parallel Machine Learning for Tool Wear Prediction

Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Immersive Distributed Design Through Real-Time Capture, Translation, and Rendering of Three-Dimensional Mesh Data

Networked, Real Time Translation of 3D Mesh Data to Immersive Virtual Reality Environments

A Survey of Cloud-Based Design and Engineering Analysis Software Tools

Forecasting Obsolescence Risk Using Machine Learning

Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems

Performance Evaluation of Cloud-Based High Performance Computing for Finite Element Analysis

Economic Benefit Analysis of Cloud-Based Design, Engineering Analysis, and Manufacturing

A Study of Correlation Between DMSMS Obsolescence and Counterfeit Electronics

Ontology-Enabled Data Inquiry for Cost Analysis and Obsolescence Mitigation

Ontology-Based Malfunction Forecast and Root Cause Analysis in Product Design

Design Refresh Planning Models for Managing Obsolescence

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