A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network

This study presents an innovative methodology for preform design in metal forging processes based on the convolution neural network (CNN) algorithm. The proposed approach extracts the features of inputted forging product geometries and utilizes them to derive the corresponding preform shapes by employing weight arrays (filters) determined during the convolutional operations. The filters are progressively updated during the training process, emulating the learning steps of a process engineer responsible for the design of preform shapes for the forging processes. The design system is composed of multiple three-dimensional (3D) CNN sub-models, which can automatically derive individual 3D preform design candidates. It also implies that the 3D surfaces of preforms are easily acquired, which is important for the forging industry. The proposed preform design methodology was validated by applying it to two-dimensional (2D) axisymmetric shapes, one-quarter plane-symmetric 3D shapes, and two other industrial cases. In all the considered cases, the design methodology achieved substantial reductions in the forging load without forging defects, proving its reliability and effectiveness for application in metal forging processes.

Download Full-text

Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization

Computational Materials Science ◽

10.1016/j.commatsci.2020.109850 ◽

2020 ◽

Vol 184 ◽

pp. 109850 ◽

Cited By ~ 1

Author(s):

Chengping Rao ◽

Yang Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Three Dimensional ◽

Heterogeneous Material ◽

3D Cnn

Download Full-text

Convolution Neural Network for Pose Estimation of Noisy Three-Dimensional Face Images

2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS) ◽

10.1109/icetas.2018.8629150 ◽

2018 ◽

Cited By ~ 1

Author(s):

Bharindra Kamanditya ◽

Randy Pangestu Kuswara ◽

Muhammad Adi Nugroho ◽

Benyamin Kusumoputro

Keyword(s):

Neural Network ◽

Pose Estimation ◽

Three Dimensional ◽

Convolution Neural Network ◽

Face Images ◽

Dimensional Face

Download Full-text

Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution

Laser & Optoelectronics Progress ◽

10.3788/lop57.141009 ◽

2020 ◽

Vol 57 (14) ◽

pp. 141009

Author(s):

冯博文 Feng Bowen ◽

吕晓琪 Lü Xiaoqi ◽

谷宇 Gu Yu ◽

李菁 Li Qing ◽

刘阳 Liu Yang

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Three Dimensional ◽

Convolution Neural Network ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Dilated Convolution

Download Full-text

Three-dimensional fingerprint recognition by using convolution neural network

2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems ◽

10.1117/12.2294017 ◽

2018 ◽

Author(s):

Qianyu Tian ◽

Zonghua Zhang ◽

Nan Gao

Keyword(s):

Neural Network ◽

Three Dimensional ◽

Convolution Neural Network ◽

Fingerprint Recognition

Download Full-text

Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition

Information ◽

10.3390/info11080380 ◽

2020 ◽

Vol 11 (8) ◽

pp. 380

Author(s):

Boyu Chen ◽

Zhihao Zhang ◽

Nian Liu ◽

Yang Tan ◽

Xinyu Liu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Three Dimensional ◽

Expression Recognition ◽

Learning Framework ◽

The Face ◽

3D Cnn ◽

Visual Attention Mechanism ◽

Casme Ii ◽

Micro Expression

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.

Download Full-text

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Remote Sensing ◽

10.3390/rs13204065 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4065

Author(s):

Run Yu ◽

Youqing Luo ◽

Haonan Li ◽

Liyuan Yang ◽

Huaguo Huang ◽

...

Keyword(s):

Neural Network ◽

Early Detection ◽

Convolutional Neural Network ◽

Three Dimensional ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Hyperspectral Images ◽

Pine Wilt ◽

Pine Trees ◽

3D Cnn

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

Download Full-text

ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification

Applied Sciences ◽

10.3390/app12010174 ◽

2021 ◽

Vol 12 (1) ◽

pp. 174

Author(s):

Byungjin Kang ◽

Inho Park ◽

Changmin Ok ◽

Sungho Kim

Keyword(s):

Neural Network ◽

Spatial Information ◽

Hyperspectral Image ◽

Convolution Neural Network ◽

Spectral Information ◽

Hyperspectral Image Classification ◽

One Dimensional ◽

Proposed Model ◽

3D Cnn ◽

2D And 3D

Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information.

Download Full-text