# Special Issue: Smart Resilient Manufacturing
###### Abstract  
During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc. It has fostered the concept of smart manufacturing as the next generation manufacturing paradigm, which is the core of Industry 4.0. The worldwide COVID-19 pandemic has significantly impacted the manufacturing industry and requires manufacturing companies to create rapid and radical innovations in both business and operational models in order to continue production, despite disrupted supply chains. Advanced manufacturing and production technologies will play a critical role to make the manufacturing systems more resilient, thus being able to cope with the pandemic disruption. This Special Issue aimed to collect and represent all the high-quality research that covers different topics of smart manufacturing including, but not limited to, data-driven smart manufacturing, Industrial Internet of Things (IIoT), data sharing for manufacturing, advanced cyber-physical production systems (CPPS), innovative models and reference architectures, process/quality optimization, systems engineering and model-based systems engineering (MBSE), applications of AI/machine learning/blockchain, etc. There are a total of eleven articles (ten research articles and one review article) in this Special Issue which are mainly focused on resilient manufacturing system design, digital twins, semantic modeling and decision making for manufacturing systems, simulation-based risk management, AI-based techniques for manufacturing, model-based systems engineering and manufacturing, etc. [PERSON] et al. [1] proposed a functional deployment approach for selecting the product design concept from the perspective of quality trade-off. [PERSON] et al. [2] developed a robust engineering approach for designing resilient manufacturing systems based on data and simulation. Except for the perspective of manufacturing system design, several articles focus on the decision making of manufacturing and service systems. [PERSON] et al. [3] developed a cognitive digital twin framework supporting the agile supply chain. [PERSON] et al. [4] designed a digital twin framework for decision making in the industrial economy. [PERSON] et al. [5] provided a digital twin-based approach for value co-creation. In the specific technological domains, simulation and AI are used for resilient manufacturing lifecycle. [PERSON] et al. [6] made use of Monte Carlo simulations to support risk assessment in the system lifecycle. [PERSON] et al. [7] used digital twins and reinforcement learning to develop a service to implement production control for a micro smart factory. [PERSON] et al. [8] used a machine learning approach to monitor cyber-physical LPG debutanizer distillation columns. [PERSON] et al. [9] provided an ANN-based approach for analyzing and optimizing the material properties. Finally, MBSE is a new systems engineering approach supporting the manufacturing system proposed by researchers. Two papers focus on MBSE, as follows. [PERSON] et al. [10] implemented a systematic literature review of MBSE tool-chains. This paper was cited by the INCOSE SEBoK (Guide to the Systems Engineering Body of Knowledge)whose section is about MBSE. Moreover, [PERSON] et al. [11] proposed a data-knowledge hybrid approach for gas turbine diagnosis using MBSE and knowledge graph models. Although submissions for this Special Issue have been closed, more in-depth research in the field of smart resilient manufacturing continues to address the challenges we face today. For example, a new conference, the 2 nd SESC MBSE&DE International Symposium, will be held in 2023, in which the topic of digitalization for smart resilient manufacturing is welcome. Conceptualization, J.L., X.Z. and D.K. ; methodology, J.L., X.Z. and D.K. ; validation, J.L., X.Z. and D.K.