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Proceedings Papers

Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network

Shenghan Guo, Dali Wang, Jian Chen, Zhili Feng, Weihong “Grace” Guo

Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A011, June 21–25, 2021

Paper No: MSEC2021-61775

DOI: https://doi.org/10.1115/MSEC2021-61775

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Topics: Artificial neural networks, Image segmentation, Nondestructive evaluation, Welded joints, Welding

Proceedings Papers

Multiple Sound Sensors And Fusion In Modern CNN-Based Machine State Prediction

Eunseob Kim, Huitaek Yun, Martin Byung-Guk Jun, Kyunghyun Kim, Suk Won Cha

Proc. ASME. MSEC2021, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T05A017, June 21–25, 2021

Paper No: MSEC2021-64036

DOI: https://doi.org/10.1115/MSEC2021-64036

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Topics: Artificial neural networks, Machine tools, Machinery, Sensors, Signals, Internet of things, Manufacturing, Acoustics, Bridges (Structures), Conveyor systems

Proceedings Papers

Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and SCADA Data

Shenglei Du, Jingmei Guo, Lin Yi, Chen Zhang, Shi Liu

Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A001, June 21–25, 2021

Paper No: MSEC2021-58639

DOI: https://doi.org/10.1115/MSEC2021-58639

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Topics: Artificial neural networks, Reliability, Wind turbines

Proceedings Papers

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

Yongzhi Qu, Gregory W. Vogl, Zechao Wang

Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A017, June 21–25, 2021

Paper No: MSEC2021-64065

DOI: https://doi.org/10.1115/MSEC2021-64065

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Topics: Artificial neural networks, Frequency response, Neural network models, Sensors

Proceedings Papers

Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification

Hao Wang, Yassine Qamsane, James Moyne, Kira Barton

Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A006, June 21–25, 2021

Paper No: MSEC2021-63661

DOI: https://doi.org/10.1115/MSEC2021-63661

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Topics: Artificial neural networks, Machine components, Engineering simulation, Machinery, Simulation, Testing, Time series, Cyber-physical systems, Delays, Design

Proceedings Papers

In-Situ Monitoring of Laser Powder Bed Fusion Process Anomalies via a Comprehensive Analysis of Off-Axis Camera Data

Chaitanya Krishna Prasad Vallabh, Yubo Xiong, Xiayun Zhao

Proc. ASME. MSEC2020, Volume 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation, V001T01A041, September 3, 2020

Paper No: MSEC2020-8300

DOI: https://doi.org/10.1115/MSEC2020-8300

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Topics: Lasers, Process monitoring, Additive manufacturing, Aerospace industry, Artificial neural networks, Biomedicine, Data acquisition, Dynamics (Mechanics), Process control, Quality control

Proceedings Papers

Prediction and Optimization of Surface Roughness and Cutting Force in Turning of UHMWPE by Using Taguchi Method, Response Surface Methodology and Neural Networks

Florin Susac, Felicia Stan, Catalin Fetecau, Irina Besliu

Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A035, September 3, 2020

Paper No: MSEC2020-8534

DOI: https://doi.org/10.1115/MSEC2020-8534

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Topics: Artificial neural networks, Cutting, Optimization, Response surface methodology, Surface roughness, Taguchi methods, Turning

Proceedings Papers

A Hybrid Physical Model Based Artificial Neural Network Approach to Predict Surface Texture in the Honing of Engine Cylinder Bore

Burhan Afzel, Xueping Zhang, Anil K. Srivastava

Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T06A005, September 3, 2020

Paper No: MSEC2020-8219

DOI: https://doi.org/10.1115/MSEC2020-8219

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Topics: Artificial neural networks, Cylinders, Engine cylinders, Surface roughness, Surface texture

Proceedings Papers

Edge Computing Accelerated Defect Classification Based on Deep Convolutional Neural Network With Application in Rolling Image Inspection

Jiayu Huang, Nurretin Sergin, Akshay Dua, Erfan Bank Tavakoli, Hao Yan, Fengbo Ren, Feng Ju

Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A037, September 3, 2020

Paper No: MSEC2020-8261

DOI: https://doi.org/10.1115/MSEC2020-8261

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Topics: Artificial neural networks, Inspection, Algorithms, Flaw detection, Machine learning, Manufacturing systems, Support vector machines, Visualization

Proceedings Papers

Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods

Joseph Cohen, Baoyang Jiang, Jun Ni

Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A029, September 3, 2020

Paper No: MSEC2020-8360

DOI: https://doi.org/10.1115/MSEC2020-8360

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Topics: Fault diagnosis, Machine learning, Principal component analysis, Programmable controllers, Algorithms, Signals, Artificial neural networks, Failure, Integrated circuit fabrication, Manufacturing

Proceedings Papers

OF-NET: Deep-Learning Based Sub-Pixel Optical Flow Estimation With Multi-Scale Convolutional Neural Network

Ru Yang, Ping Guo

Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T08A022, September 3, 2020

Paper No: MSEC2020-8517

DOI: https://doi.org/10.1115/MSEC2020-8517

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Topics: Artificial neural networks, Displacement, Flow (Dynamics)

Proceedings Papers

Image-Guided Variant Geometry Analysis of Layerwise Build Quality in Additive Manufacturing

Farhad Imani, Ruimin Chen, Evan Diewald, Edward Reutzel, Hui Yang

Proc. ASME. MSEC2019, Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, V001T02A041, June 10–14, 2019

Paper No: MSEC2019-3002

DOI: https://doi.org/10.1115/MSEC2019-3002

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Topics: Additive manufacturing, Artificial neural networks, Geometry

Proceedings Papers

Convolutional Neural Network Using Bayesian Optimization for Laser Welding Tailor Rolled Blanks Penetration Detection

Zhehao Zhang, Yi Zhang, Feng Luo, Jie Li, Cheng Lu, Yuze Zhao, Hang Zhang, Ange Lu

Proc. ASME. MSEC2019, Volume 2: Processes; Materials, V002T03A079, June 10–14, 2019

Paper No: MSEC2019-2836

DOI: https://doi.org/10.1115/MSEC2019-2836

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Topics: Artificial neural networks, Blanks, Laser welding, Optimization

Proceedings Papers

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

Achyuth Kothuru, Sai Prasad Nooka, Rui Liu

Proc. ASME. MSEC2018, Volume 4: Processes, V004T03A072, June 18–22, 2018

Paper No: MSEC2018-6680

DOI: https://doi.org/10.1115/MSEC2018-6680

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Topics: Artificial neural networks, Condition monitoring, Milling, Support vector machines, Wear, Machining, Cutting tools, Monitoring systems, Signals

Proceedings Papers

A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Xingjian Lai, Huanyi Shui, Jun Ni

Proc. ASME. MSEC2018, Volume 3: Manufacturing Equipment and Systems, V003T02A014, June 18–22, 2018

Paper No: MSEC2018-6678

DOI: https://doi.org/10.1115/MSEC2018-6678

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Topics: Manufacturing systems, Assembly lines, Dynamics (Mechanics), Artificial neural networks, Decision making, Design, Equipment performance, Machinery, Modeling, Production planning

Proceedings Papers

Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

Qiunan Meng, Jian Lou, Xun Xu, Shiqiang Yu

Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A033, June 4–8, 2017

Paper No: MSEC2017-2752

DOI: https://doi.org/10.1115/MSEC2017-2752

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Topics: Manufacturing, Support vector machines, Artificial neural networks, Chain, China, Design, Molding, Performance, Robustness

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

Proceedings Papers

Empirical Modeling of Material Removal Considering Tool Condition in Chemical Mechanical Planarization Process

Zhenhua Wu

Proc. ASME. MSEC2017, Volume 3: Manufacturing Equipment and Systems, V003T04A070, June 4–8, 2017

Paper No: MSEC2017-3027

DOI: https://doi.org/10.1115/MSEC2017-3027

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Topics: Artificial neural networks, Errors, Flow (Dynamics), Integrated circuit fabrication, Modeling, Polishing, Pressure, Rotation, Semiconductor wafers, Slurries

Proceedings Papers

Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes

Sushmit Chowdhury, Sam Anand

Proc. ASME. MSEC2016, Volume 3: Joint MSEC-NAMRC Symposia, V003T08A006, June 27–July 1, 2016

Paper No: MSEC2016-8784

DOI: https://doi.org/10.1115/MSEC2016-8784

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Topics: Additive manufacturing, Artificial neural networks, Thermal deformation, Deformation, Polymers, Algorithms, Alloys, Computer-aided design, Design, Extruding

Proceedings Papers

Sensor-Based Online Process Fault Detection in Additive Manufacturing

Prahalad K. Rao, Jia (Peter) Liu, David Roberson, Zhenyu (James) Kong

Proc. ASME. MSEC2015, Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, V002T04A010, June 8–12, 2015

Paper No: MSEC2015-9389

DOI: https://doi.org/10.1115/MSEC2015-9389

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Topics: Additive manufacturing, Flaw detection, Sensors, Accelerometers, Accuracy and precision, Artificial neural networks, Decision making, Failure, Failure mechanisms, Machinery

NARROW

Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network

Multiple Sound Sensors And Fusion In Modern CNN-Based Machine State Prediction

Real-Time Reliability Assessment of Wind Turbine Components Using a Back-Propagation Neural Network and SCADA Data

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification

In-Situ Monitoring of Laser Powder Bed Fusion Process Anomalies via a Comprehensive Analysis of Off-Axis Camera Data

Prediction and Optimization of Surface Roughness and Cutting Force in Turning of UHMWPE by Using Taguchi Method, Response Surface Methodology and Neural Networks

A Hybrid Physical Model Based Artificial Neural Network Approach to Predict Surface Texture in the Honing of Engine Cylinder Bore

Edge Computing Accelerated Defect Classification Based on Deep Convolutional Neural Network With Application in Rolling Image Inspection

Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods

OF-NET: Deep-Learning Based Sub-Pixel Optical Flow Estimation With Multi-Scale Convolutional Neural Network

Image-Guided Variant Geometry Analysis of Layerwise Build Quality in Additive Manufacturing

Convolutional Neural Network Using Bayesian Optimization for Laser Welding Tailor Rolled Blanks Penetration Detection

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

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

Empirical Modeling of Material Removal Considering Tool Condition in Chemical Mechanical Planarization Process

Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes

Sensor-Based Online Process Fault Detection in Additive Manufacturing

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