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Proceedings Papers

An Industry 4.0 Project With Data Integration – From Part Design to Chip Flow and Physics-Based Machine Learning for AlMg5

Wolfgang Lortz
Proc. ASME. MSEC2024, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality & Reliability, V002T06A035, June 17–21, 2024
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2024-130340
https://doi.org/10.1115/MSEC2024-130340
View Paper titled, An Industry 4.0 Project With Data Integration – From Part Design to Chip Flow and Physics-Based Machine Learning for AlMg5
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Topics: Cutting, Data fusion, Design, Flow (Dynamics), Friction, Machine learning, Physics
Proceedings Papers

Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process

Danny Hoang, Nasir Mannan, Ruby ElKharboutly, Ruimin Chen, Farhad Imani
Proc. ASME. MSEC2023, Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A029, June 12–16, 2023
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2023-105170
https://doi.org/10.1115/MSEC2023-105170
View Paper titled, Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process
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Topics: Additive manufacturing, Data fusion, Manufacturing
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
Publisher: American Society of Mechanical Engineers
Paper No: IAM2022-96740
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
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2022-85443
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
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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
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2021-62348
https://doi.org/10.1115/MSEC2021-62348
View Paper titled, Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions
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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
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2020-8468
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
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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
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2019-2936
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
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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
Publisher: American Society of Mechanical Engineers
Paper No: MSEC2019-2976
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