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The successful implementation of Human–Robot Collaboration (HRC) has become a prominent feature of smart manufacturing environments. Key industrial requirements, such as flexibility, efficiency, collaboration, consistency, and sustainability, present pressing HRC needs in the manufacturing sector. This paper provides a systemic review and an in-depth discussion of the key technologies currently being employed in smart manufacturing with HRC systems. The work presented here focuses on the design of HRC systems, with particular attention given to the various levels of Human–Robot Interaction (HRI) observed in the industry. The paper also examines the key technologies being implemented in smart manufacturing, including Artificial Intelligence (AI), Collaborative Robots (Cobots), Augmented Reality (AR), and Digital Twin (DT), and discusses their applications in HRC systems. The benefits and practical instances of deploying these technologies are showcased, emphasizing the substantial prospects for growth and improvement in sectors such as automotive and food. However, the paper also addresses the limitations of HRC utilization and implementation and provides some insights into how the design of these systems should be approached in future work and research. Overall, this paper provides new insights into the current state of HRC in smart manufacturing and serves as a useful resource for those interested in the ongoing development of HRC systems in the industry. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework. [ABSTRACT FROM AUTHOR]

As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry 4.0 networks presents significant challenges, particularly in terms of security and the integration of diverse technologies into a functioning and efficient infrastructure. To address these challenges, emerging digital twin standards are enabling the connection of various systems by linking individual digital twins, creating a system of systems. The objective is to develop a "universal translator" that can interpret inputs from both the real and digital worlds, merging them into a seamless cyber-physical reality. It will be demonstrated how the myriad of technologies and systems in Industry 4.0 networks can be connected through the use of digital twins to create a seamless "system of systems". This will improve interoperability, resilience, and security in smart manufacturing systems. The paper will also outline the potential benefits and limitations of digital twins in addressing the challenges of Industry 4.0 networks. [ABSTRACT FROM AUTHOR]

Innovation and transformative changes in products, manufacturing technologies, business strategies, and manufacturing paradigms have profoundly changed the manufacturing systems. In addition to being environmentally, economically socially sustainable, manufacturing systems are increasingly using intelligent technologies to be even more resilient, responsive, and adaptable. A new Adaptive Cognitive Manufacturing Systems (ACMS) paradigm, its drivers, enablers, and characteristics, including cognitive adaptation, is presented. Classification and definitions of four types of adaptability in manufacturing systems are included. Human-centric collaboration of workers and intelligent machines and applications, and the future of work in cognitive adaptive manufacturing systems are outlined. Cognitive Digital Twins (CDT), their features, evolution, and their use to support humans in intelligent, collaborative manufacturing settings are discussed. Industrial applications and case studies are used to illustrate the presented concepts and paradigms. Challenges and future research directions to achieve the ACMS paradigm and implement more intelligent, more adaptive, and sustainable manufacturing systems are presented. The presented novel concepts and technologies make significant contributions to the fast-evolving field of manufacturing systems. This pioneering research sheds light on many important future research topics and provides a road map and motivation for researchers in this field. [ABSTRACT FROM AUTHOR]