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Proceedings Papers

A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing

Zhuo Yang, Jaehyuk Kim, Yan Lu, Ho Yeung, Brandon Lane, Albert Jones, Yande Ndiaye
Proc. ASME. IAM2022, 2022 International Additive Manufacturing Conference, V001T02A012, October 19–20, 2022
Paper No: IAM2022-96740
DOI: https://doi.org/10.1115/IAM2022-96740
View Paper titled, A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing
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Topics: Additive manufacturing, Data fusion, Metal powders, Process monitoring
Proceedings Papers

Process Mechanics – a Guide for Industry 4.0: Modelling Cutting of Nimonic 90 and Ti-6Al-4V

W. Lortz, Radu Pavel
Proc. ASME. MSEC2022, Volume 2: Manufacturing Processes; Manufacturing Systems, V002T05A037, June 27–July 1, 2022
Paper No: MSEC2022-85443
DOI: https://doi.org/10.1115/MSEC2022-85443
View Paper titled, Process Mechanics – a Guide for Industry 4.0: Modelling Cutting of Nimonic 90 and Ti-6Al-4V
Click here to open pdf in another window PDF for Process Mechanics – a Guide for Industry 4.0: Modelling Cutting of Nimonic 90 and Ti-6Al-4V
Topics: Cutting, Hardening, Modeling, Temperature, Deformation, Simulation, Stress, Alloys, Artificial intelligence, Computers
Proceedings Papers

Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions

Xin Li, Xiaolei Fang
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A003, June 21–25, 2021
Paper No: MSEC2021-62348
DOI: https://doi.org/10.1115/MSEC2021-62348
View Paper titled, Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions
Click here to open pdf in another window PDF for Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions
Topics: Sensors, Signals, Failure, Engineering systems and industry applications, Aircraft, Data fusion, Engines, NASA, Switches, Turbofans
Proceedings Papers

Deep Learning-Based Data Fusion Method for In-Situ Porosity Detection in Laser-Based Additive Manufacturing

Qi Tian, Shenghan Guo, Weihong Guo, Linkan Bian
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A032, September 3, 2020
Paper No: MSEC2020-8468
DOI: https://doi.org/10.1115/MSEC2020-8468
View Paper titled, Deep Learning-Based Data Fusion Method for In-Situ Porosity Detection in Laser-Based Additive Manufacturing
Click here to open pdf in another window PDF for Deep Learning-Based Data Fusion Method for In-Situ Porosity Detection in Laser-Based Additive Manufacturing
Topics: Additive manufacturing, Data fusion, Lasers, Porosity
Proceedings Papers

Failure Detection and Remaining Life Estimation for Ion Mill Etching Process Through Deep-Learning Based Multimodal Data Fusion

Anqi He, Xiaoning Jin
Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A010, June 10–14, 2019
Paper No: MSEC2019-2936
DOI: https://doi.org/10.1115/MSEC2019-2936
View Paper titled, Failure Detection and Remaining Life Estimation for Ion Mill Etching Process Through Deep-Learning Based Multimodal Data Fusion
Click here to open pdf in another window PDF for Failure Detection and Remaining Life Estimation for Ion Mill Etching Process Through Deep-Learning Based Multimodal Data Fusion
Topics: Data fusion, Etching, Failure, Sensors
Proceedings Papers

A Decision-Level Data Fusion Approach to Surface Roughness Prediction

Yupeng Wei, Dazhong Wu, Janis Terpenny
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
View Paper titled, A Decision-Level Data Fusion Approach to Surface Roughness Prediction
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Topics: Additive manufacturing, Data fusion, Process monitoring, Surface roughness