Special Issue "Computing Methods for Industrial Artificial Intelligence"

Submission Deadline: 31 December 2022
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Guest Editors
Prof. Liang Gao, Huazhong University of Science and Technology, China
Prof. Dazhong Wu, University of Central Florida Orlando, USA
Prof. Long Wen, China University of Geosciences, China
Prof. Junliang Wang, Donghua University, China
Dr. Yiping Gao, Huazhong University of Science and Technology, China


Nowadays, with the rapid developments of smart technology, data can be collected more comprehensively and extensively than before in industry. Data-driven intelligent manufacturing has become the hot point and has been widely investigated worldwide. Artificial intelligence (AI) is the key technology, which can mine the valuable information from industrial data to help the analysis and optimization on the industrial manufacturing system.

Recently, various advanced AI techniques have been developed, such as swarm intelligence, intelligent computation and deep learning. These AI techniques have shown their potential to promote the efficiency and effectiveness for the industrial manufacturing system. The proposed Special Issue on Computing Methods in Industrial Artificial Intelligence will focus on the theories, methodologies and applications of the advanced AI techniques in intelligent manufacturing. The special issue is encouraging to use the advanced AI techniques to handle with the full life-cycle data in intelligent manufacturing with different application scenarios, such as workshop scheduling, quality control and intelligence operations. The purpose of this special issue is to reflect the latest developments of AI techniques and their application in intelligent manufacturing.


Potential topics include but are not limited to the following:

• Advanced industrial AI theories and methodologies

• AI-based industrial data preprocessing, modeling, analysis and decision-making

• AI-driven methods for optimization of the manufacturing system

• AI-driven methods for intelligent equipment operation

• AI-driven methods for product quality control

• AI-driven methods for full life-cycle product design

• AI-driven methods for imbalanced data in intelligent manufacturing

• AI-driven methods for small-scale samples in intelligent manufacturing

Artificial intelligence; intelligent manufacturing; industrial data analysis; deep learning; workshop scheduling and optimization