Guest Editorial: Special Section on Artificial Intelligence and Big Data Analytics for Cloud Manufacturing

C LOUD manufacturing (CMfg) adopts and extends the concept of cloud computing to virtualize and manage mass manufacturing resources and capabilities, which forms a large-scale shareable and scalable resource pool. They are made widely accessible to users as services through the Internet beyond the boundaries of enterprises. It leverages new generation information and communication technologies (ICT) and modern manufacturing technologies to empower and upgrade manufacturing competitiveness along with the transformation of manufacturing modes, technologies, and ecosystems. The wide deployment of sensors and IoT devices gives rise to the explosive generation of huge and diverse volumes of Big Data, the new “oil,” that hides knowledge and insights to assist optimal decision-making. However, the world of Big Data includes a rich and complex set of cross-media contents, including text, images, video, and audio. This calls for advanced Big Data analytics, artificial intelligence, and machine learning technologies, which can exploit heterogeneous Big Data and make more accurate and timely decisions. Cloud/edge computing, virtual reality/augmented reality, software-defined networking, 5G and beyond, and robotics lay a solid foundation for building highly flexible, large-scale, service-oriented, plug and play, proactive, teleoperational cloud manufacturing systems. Artificial intelligence (AI) and machine learning can contribute to not only Big Data analytics, to assist the reliability, configuration, performance, and fault and security management, but also robots’ behavior learning and simulation. This emerging research promises to derive concrete endless impacts on the quick deployment, productivity, and sustainability of Industry 4.0 beyond the state-of-the-art of cyber-physical systems-based industrial world. Therefore, this Special Section is dedicated to providing cutting-edge technologies and novel studies, which can address the existing challenges of AI and Big Data analytics for cloud manufacturing. Twelve articles have been accepted by this Special Section. In [A1], Zhang et al. presented an optimization method for manufacturing service collaboration via personalized dynamic pricing strategies. The proposed approach includes preference estimation, personalized pricing, and a consumer utility model. First, the solution can estimate the long-term and short-term preferences of enterprises based on multiple attributes (e.g., service quantity and available time). Second, the personalized