Special Issue on Application of Artificial Intelligence Techniques in Production Engineering
Keiichi Nakamoto* and Keigo Takasugi**
*Tokyo University of Agriculture and Technology
Naka-cho, Koganei-shi, Japan
Kakuma-machi, Kanazawa-shi, Japan
Artificial intelligence (AI) techniques have been behind disruptive innovations in every industry. Based on AI techniques, large amounts of data can be converted into actionable insights and predictions. Manufacturers have frequently faced different kinds of challenges, such as unexpected machinery failures or defective product deliveries. Still, the adoption of AI techniques is expected to improve operational efficiency, enable the launch of new products, customize product designs, and plan future financial actions. Recently, manufacturers have been using AI techniques to improve the quality of their products, achieve greater speed and visibility across supply chains, and optimize inventory management.
Given that the attention and interest in AI techniques has been growing rapidly, it is time that the current state of the art of their practical applications be presented. The main aim of this special issue is to bring together the latest AI research and practical case studies of AI techniques in production engineering.
This special issue features 10 papers related to not only operation automation but also sophisticated skill transfer in manufacturers. Their subjects cover various advancements, such as failure diagnosis, product estimation, process planning, operation planning, and workpiece fixturing in the area of machining. Moreover, the authors boldly strive to apply AI technologies even to complex systems in manufacturing fields such as laser-assisted incremental forming, injection molded direct joining, and parts assembling.
We thank the authors for their interesting papers submitted for this special issue, and we are sure that both general readers and specialists will find the information the authors provide both interesting and informative. Moreover, we deeply appreciate the reviewers for their incisive efforts. Without these contributions, this special issue would not have been possible. We truly hope that this special issue triggers further research on AI techniques in production engineering.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.