In the context of Industry 4.0, it is necessary to meet customization manufacturing demands on a timely basis. Based on the related concepts of Industry 4.0, this paper intends to introduce mobile services and cloud computing technology into the intelligent manufacturing environment. A customization manufacturing system is designed to meet the demands of personalization requests and flexible production mechanisms. This system consists of three layers, namely, a manufacturing device layer, cloud service system layer, and mobile service layer. The manufacturing device layer forms the production platform. This platform is composed of a number of physical devices, such as a flexible conveyor belt, industrial robots, and corresponding sensors. The physical devices are connected to the cloud via the support of a wireless module. In the cloud, the manufacturing big data are processed, and the optimization decision-making mechanism pertaining to customization manufacturing is formed. Then, mobile services running in a mobile terminal are used to receive orders from customers and to inquire the necessary production information. To verify the feasibility of the proposed customization manufacturing system, we also established a customizable candy production system.

Industry 4.0, which exploits cyber-physical systems and represents digital transformation of manufacturing, is deeply affecting healthcare as well as other traditional production sector. To accommodate the increasing demand of agility, flexibility, and low cost in healthcare sector, a data-driven reconfigurable production mode of Smart Factory for pharmaceutical manufacturing is proposed in this paper. The architecture of the Smart Factory is consisted of three primary layers, namely perception layer, deployment layer, and executing layer. A Manufacturing's Semantics Ontology based knowledgebase is introduced in the perception layer, which is responsible for plan scheduling of pharmaceutical production. The reconfigurable plans are generated from the production demand of drugs as well as the information statement of low-level machine resources. To further functionality reconfiguration and low-level controlling, the IEC 61499 standard is also introduced for functionality modeling and machine controlling. We verify the proposed method with an experiment of demand-based drug packing production, which reflects the feasibility and adequate flexibility of the proposed method.

Due to the recent development of cyber-physical systems, big data, cloud computing, and industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a revolutionary change in computer vision, natural language processing, and a variety of other applications, has significant potential for solutions providing in sophisticated industrial applications. In this paper, a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the feasibility and effectiveness of the proposed algorithm are validated experimentally. The proposed concept and algorithm combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.

With the deep integration of cyber physical production systems in the era of Industry 4.0, smart workshop dramatically increases the amount of data collected by smart device. A key factor in achieving smart manufacturing is to use data analysis methods for evaluating the equipment reliability and for supporting the predictive maintenance of equipment. Based on these insights, this paper proposes a deep learning-based approach that uses time series data for equipment reliability analysis. First, a framework of the TensorFlow-enabled deep neural networks (DNN) model for equipment reliability analysis is presented. Secondly, using time series equipment data, an evaluation strategy of equipment reliability based on deep learning is proposed. Finally, the reliability of a cylinder, an important part of the small trolley in automobile assembly line, is evaluated in a case study. Compared with the traditional reliability analysis method such as PCA and HMM, the prediction results show a significant improvement in prediction accuracy. This work contributes to promoting artificial intelligence algorithms for realizing highly efficient manufacturing.

With the continued advancement of Artificial Intelligence (AI) technology in Customized Manufacturing (CM), the current intelligence model, which separates ‘perception’ from ‘execution,’ lacks adaptability and generalizability. Embodied Intelligence (EI), an emerging technology emphasizing real-time environmental interaction and feedback, is expected to enable integrated intelligent manufacturing systems characterized by ‘perception-cognition-execution-feedback,’ enhancing the performance and intelligence of multifactor systems like the Internet of Manufacturing Things (IoMT), equipment management, and resource scheduling. However, current AI systems in CM remain isolated and lack methods for environmental interaction with multi-source perception and feedback, hindering the development of autonomous, evolving intelligent systems. To address the environmental interaction bottlenecks among diverse production elements in CM, this paper proposes a Circular Embodied Intelligence Manufacturing (CEIM) architecture aimed at enabling the fusion of heterogeneous production-element information. The objective is to enhance environmental perception and establish autonomous system evolution mechanisms, thereby optimizing production decision-making. By integrating sensor data and AI model outputs, and leveraging the advanced reasoning capabilities of large language models (LLMs), CEIM facilitates the semantic fusion of multiple production elements—including IoMT, intelligent devices, and manufacturing resources—to enable the deployment of EI applications in CM. The implementation of CEIM is illustrated and validated through a case study on a customized gift box packaging platform. Finally, this paper discusses the opportunities and challenges of applying EI in manufacturing and aims to provide insights into the future development of EI-oriented manufacturing systems.