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Proc. ASME. MSEC2014, Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing, V001T01A004, June 9–13, 2014

Paper No: MSEC2014-3948

DOI: https://doi.org/10.1115/MSEC2014-3948

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Topics: Computer simulation, Steel, Phase transitions, Diffusion (Physics), Ferrites (Magnetic materials), Heating, Particulate matter, Temperature

Journal Articles

Experimental Study of High Temperature Hydrozing Annealing 1J50 Alloy's Magnetic Properties

Yingtao Zhang, Gang Wang, Zhenguo Nie, Wankai Shi, Yiming Rong

J. Manuf. Sci. Eng. December 2013, 135(6): 061024.

Paper No: MANU-13-1197

DOI: https://doi.org/10.1115/1.4025806

Published Online: November 27, 2013

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Topics: Annealing, Grain size, High temperature

Proceedings Papers

Prediction of Distortion in Quenching-Tempering Processes for a SM256 Steel Component With Internal Thread

Zhenguo Nie, Yiming (Kevin) Rong, Gang Wang

Proc. ASME. MSEC2013, Volume 1: Processing, V001T01A030, June 10–14, 2013

Paper No: MSEC2013-1139

DOI: https://doi.org/10.1115/MSEC2013-1139

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Topics: Quenching (Metalworking), Steel, Thread

Journal citation

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Supplemental Material

Intelligent Detection and Classification of Surface Defects on Cold-Rolled Galvanized Steel Strips Using a Data-Driven Faulty Model With Attention Mechanism

Classification of Surface Defects on Galvanized Cold-Rolled Steel Sheets Using Data-Driven Fault Model With Attention Mechanism

SuperMeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method

SuperMeshing: A Deep Learning Method for Boosting Mesh Density in Numerical Computation Within 2D Domain

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field With Attention Mechanism And Perceptual Features

SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

A Study on Using Image-Based Machine Learning Methods to Develop Surrogate Models of Stamp Forming Simulations

StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

Optimization of Part Consolidation for Minimum Production Costs and Time Using Additive Manufacturing

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Optimization of Parts Consolidation for Minimum Production Costs and Time Using Additive Manufacturing

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

A Coupled Thermomechanical Modeling Method for Predicting Grinding Residual Stress Based on Randomly Distributed Abrasive Grains

A Statistical Model of Equivalent Grinding Heat Source Based on Random Distributed Grains

Numerical Simulation of the Austenitizing Process in Hypoeutectoid Fe-C Steels

Experimental Study of High Temperature Hydrozing Annealing 1J50 Alloy's Magnetic Properties

Prediction of Distortion in Quenching-Tempering Processes for a SM256 Steel Component With Internal Thread

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