Smart  manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

We outline the smart manufacturing challenges for formulated products, which are typically multicomponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricultural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing—namely, digitalization and the use of large datasets with predictive models and solution-finding algorithms—in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic problems in order to demonstrate their benefits and highlight any systemic weaknesses.

Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.

The middle tower of Taizhou Bridge consists of deepwater caisson foundation and steel tower with herringbone shape along the longitudinal direction. The construction is complex. Based on previous study, using the technology of up-stream and down-stream anchor pier positioning and the system of information real-time monitoring ensures position and implantation. Meanwhile，control measure is used to prevent caisson sinking suddenly and extra-sinking in final stage. Erection accuracy of steel tower is very high, but adjusted segments are few. During the construction, the structure system changes frequently. To the feature, the method of whole process control based on geometric control is used in the project, which analyzed the error of manufacturing segment, linear of pre-assembled segment and erection, and accuracy management system is established uniformly. The final accuracy of erection is better than that designed. Many innovative technologies are obtained in this project, and the key technologies about construction of deepwater caisson , manufacturing and erection of steel tower are formed, which can be referred for other similar projects in the future.

Manufacturing industry is the key carrier of the technological innovation. The technological innovation is the key force for the development of manufacturing industry. The manufacturing industry in China has experienced the fast development since reform and opening up, but in general, the industry appears to be “large but not strong”. The research utilizes a scientific analysis to detect environments the development of manufacturing in China facing to and capabilities of the innovation. It is critical to persist the principle of “independent innovation, intelligence leaded, green development, and breakthrough in key areas” to realize the innovation-driven development of China Manufacturing. The core of the developments must be to improve the capability of independent innovation, and the main path is to develop the manufacturing digitalized, networked, and intelligentialized. It is important to develop green manufacturing with energy efficiency increased and friendly relationship with environments. Firstly, breakthroughs in key products in the key fields and key manufacturing technology can be realized, and the transformation and upgrading of manufacturing industry can be realized. Finally, we will realized the historic leap of China Manufacturing from high capacity to high capability.

Since twenty-first century, most of the developed countries have realized the importance of advanced manufacturing. “Industrial Internet” from the U.S. and “Industrie 4.0” from Germany should be the best two strategies to develop advanced manufacturing. They have caused the transformation of product development, production mode, and the achievement of the production value, globally. In this critical moment, it is important to promote the integration of informatization and industrialization, to insist on innovation driven, to strengthen the intelligent manufacturing base, to consolidate the training of high skilled talents, and to strengthen international cooperation for consolidating and enhancing the global status of China manufacturing.

This paper explains the capability structure of aerospace equipments manufacturing system based on the trait analysis of aerospace equipments and the manufacturing process, analyzes the major problems of China's aerospace equipments manufacturing, and gives suggestions for improving China’s aerospace equipments manufacturing capabilities.

Based on a thorough study of the implications and key techniques of intelligent manufacturing, this paper proposes three development models and specific technical approaches to assist in China's transition from digital manufacturing to intelligent manufacturing. Starting from the production characteristics of a typical industry, it puts forward a corresponding technical roadmap for the development of intelligent manufacturing. This roadmap provides guidance for technical approaches that can promote the development of China's manufacturing industry from digital manufacturing to intelligent manufacturing.