A5038599556- Metal Forming Simulation Techniques
- Metallurgy and Material Forming
- Microstructure and Mechanical Properties of Steels
- Microstructure and mechanical properties
- Laser and Thermal Forming Techniques
- High-Velocity Impact and Material Behavior
- Engineering Structural Analysis Methods
- Mechanical stress and fatigue analysis
- Aluminum Alloy Microstructure Properties
- Non-Destructive Testing Techniques
- Advanced Surface Polishing Techniques
- Magnesium Alloys: Properties and Applications
- Vibration and Dynamic Analysis
- Fatigue and fracture mechanics
- Structural Load-Bearing Analysis
- Mechanical Behavior of Composites
- Numerical methods in engineering
- Mechanical Engineering and Vibrations Research
- Aluminum Alloys Composites Properties
- Engineering Applied Research
- Elasticity and Material Modeling
- Advanced machining processes and optimization
- Tribology and Lubrication Engineering
- Mechanical Failure Analysis and Simulation
- Optical measurement and interference techniques
Tokyo University of Agriculture and Technology
2015-2024
Institute of Engineering
2016-2020
Tokyo University of Science
2017-2018
National Institute of Technology, Tokyo College
2018
College of Industrial Technology
2018
Nippon Steel (Japan)
2014-2017
Japan Society for the Promotion of Science
2016
Mitsubishi Materials (Japan)
2008-2014
Nippon Institute of Technology
2014
Engineering Systems (United States)
2012
This article details the ESAFORM Benchmark 2021. The deep drawing cup of a 1 mm thick, AA 6016-T4 sheet with strong cube texture was simulated by 11 teams relying on phenomenological or crystal plasticity approaches, using commercial self-developed Finite Element (FE) codes, solid, continuum classical shell elements and different contact models. material characterization (tensile tests, biaxial tensile monotonic reverse shear EBSD measurements) forming steps were performed care (redundancy...
To improve the accuracy of a sheet metal forming simulation, constitutive model is calibrated using results from multiaxial material testing. However, testing time-consuming and requires specialized equipment. This study proposes two different deep neural network (DNN) approaches, two- three-dimensional convolutional (DNN-2D DNN-3D), to efficiently estimate biaxial stress-strain curves aluminum alloy sheets digital image representing sample's crystallographic texture. DNN-2D designed {111}...