Soft computing includes machine learning, computer science, and computing technologies in certain engineering disciplines. Soft computing enables research, simulation, and analysis of complex problems and phenomena. Moreover, related developmental theories can be applied and used to solve problems that occurred in the manufacturing process of Industry 4.0.

Industry 4.0 is also known as industrial internet, smart factory, or advanced manufacturing. With the assistance of new technologies such as the Internet of Things (IoT) and the wide applications of mobile technologies, organizations are generating large amounts of data in different formats at a faster rate than before. In addition, data content, process, analytical model and management of big data transformation have also emerged huge challenges and opportunities. Hence, advanced soft computing methods and decision-making techniques can be used to extract useful information and obtain effective manufacturing intelligence. They are able to make equipment automation and combine it with decision-making techniques for adopting useful rules and patterns from the big data. In addition, they are able to detect potential failures in the early stage under certain circumstances, diagnose defects, control advanced equipment/process, decrease cycle time and costs, and increase productive rate. The aim and scope can cover wide ranges of fields such as artificial intelligence (AI), robotics, Internet of Things (IoT), autonomous vehicle, 3D printing, nanotechnology, materials science, energy storage, and so on.

The aim of this Special Issue is to bring together original research and review articles regarding the latest developments, problems and challenges of the applications of soft computing in the manufacturing process of Industry 4.0.

Potential topics include but are not limited to the following:

• Innovative methods for soft computing analysis
• Big data machine learning in soft computing
• Natural Language Processing (NLP) in soft computing
• Industry 4.0 applications of soft computing
• Potential failure and defect diagnosis and analysis in soft computing
• Intelligent quality tools and methods in soft computing
• Production planning and control in soft computing
• Performance evaluation in soft computing
• Soft computing analysis applied to industry, medical, or environment
• Case studies of effective production and operations management