Advanced Materials & Sustainable Manufacturing

Guide for Authors Submit to AMSM

AI-based Sustainable Smart Industrial Systems

Deadline for manuscript submissions: 30 November 2024.

Guest Editors (4)

Zhenglei He
Prof. Dr. Zhenglei He 
State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, 510640, China
Interests: Intelligent Manufacturing; Industrial Engineering; Simulation and Optimization
Website: https://www.researchgate.net/profile/Zhenglei-He
Matilde Santos
Prof. Dr. Matilde Santos 
Instituto de Tecnología del Conocimiento, Computer Sciences Faculty, University Complutense of Madrid, Madrid, Spain
Interests: Intelligent Control; Modeling and Simulation; Soft Computing; Engineering Applications; Floating Wind Turbines
Website: http://www.dacya.ucm.es/msantos/indexe.html
Kim-Phuc TRAN
Prof. Dr. Kim-Phuc TRAN 
University of Lille - ENSAIT, GEMTEX laboratory, F-59100 Roubaix, France 
Interests: Industrial AI; Statistical Computing; Embedded AI; Human-centered AI; Decision Support Systems
Website: https://www.gemtex.fr/gemtex-members/phuc-tran/
Xianyi Zeng
Prof. Dr. Xianyi Zeng 
University of Lille - ENSAIT, GEMTEX laboratory, F-59100 Roubaix, France
Interests: Fashion Digitalization; Wearable Systems; Decision Support Systems
Website: https://www.gemtex.fr/gemtex-members/xianyi-zeng/

Topic Collection Information

The last decades have witnessed the rapid growth of Artificial Intelligence (AI) and its applications that add intelligence into industrial applications to drive continuous improvement, knowledge transfer, and data-based decision making, leading to development of modern industries towards sustainable and smart innovative manufacturing and management models. Multiple sustainable criteria, including product quality, environmental impacts, recycling capacity, should be simultaneously considered in these models. A huge volume of data collected from various industrial process can feed real-time analytic solutions provided by AI and Decision Support Systems (DSS), which can lead to optimal industrial operations.

In this context, extended from the related papers presented in the international conference of FLINS-ISKE 2024, this special Issue aims to offer a systematic overview of AI-based sustainable smart industrial systems and provide innovative computational intelligent approaches, to effectively support decision making in big data environments. The concerned industrial applications will include quality control, manufacturing process optimization, recycling, environmental impacts evaluation, and so on.

Published Papers (2 papers)

Article

18 October 2024

Multi-Objective Distributed Real-Time Trajectory Planning for Gliding Aircraft Cluster

A new combat strategy that enables coordinated operations of gliding aircraft clusters for multi-target strikes imposes higher demands on the coordination, real-time responsiveness, and strike accuracy of gliding aircraft clusters. Due to the high speed and large inertia characteristics of gliding aircraft, traditional trajectory planning methods often face challenges such as long computation times and difficulty in responding to dynamic environments in real-time when dealing with large-scale gliding aircraft clusters. This paper proposes a distributed cooperative trajectory planning method for multi-target strikes by gliding aircraft clusters to address this issue. By introducing a multi-objective distributed real-time trajectory planning approach based on Multi-Agent Deep Deterministic Policy Gradients (MADDPG), the gliding aircraft execute distributed cooperative trajectory planning based on the trained model. Due to its robust real-time performance, the gliding aircraft do not need to recalculate trajectories for different initial positions of the cluster. Simulation results show that the average error between the gliding aircraft cluster and the target point is 2.1 km, with a minimum error of 0.06 km and a hit rate of 96.6%, verifying the significant advantages of this method in real-time planning capability and strike accuracy.

Jiaming Yu
Qinglin Sun*
Hao Sun

Article

23 October 2024

Federated Transfer Learning-Based Paper Breakage Fault Diagnosis

The diagnosis of paper breakage faults during the papermaking process is of great significance for improving product quality and maintaining stability in the production process. This paper develops a cross-condition transfer learning fault diagnosis model. This study proposes a fault diagnosis method based on transfer learning to address the issue of single-condition diagnostic models performing poorly when applied to different conditions..This method uses both parameter transfer and feature transfer to diagnose faults across different conditions. At the same time, in response to the issue of insufficient small sample operating data, we introduce federated learning technology to explore the impact of model compression rates on the diagnostic accuracy of the federated global model during the federated model training process. The results indicate that compared to single operating condition models, fault diagnosis performance based on transfer learning across different operating conditions has improved. The diagnostic model based on feature transfer performs even better, achieving accuracy rates of 98.31%, 94.64%, and 96.43% under different transfer tasks, allowing for accurate classification of the majority of samples. Additionally, the federated learning method provides an effective solution for fault diagnosis in small sample operating conditions, and an appropriate model compression rate can ensure diagnostic accuracy while protecting data privacy.

Xiaoru Yu
Guojian Chen
Xianyi Zeng
Zhenglei He*
TOP