Digital twins-based smart manufacturing system design in Industry 4.0: A review

Construction projects and cities account for over 50% of carbon emissions and energy consumption Industry 4 0 and digital transformation may increase productivity and reduce energy consumption A digital twin (DT) is a key enabler in implementing Industry 4 0 in the areas of construction and smart cities It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT) As a technology, it is in high demand in various industries, and its literature is growing exponentially Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, moni-toring, or controlling physical objects However, a DT is supposed to offer much more than digital representation Characteristics such as bi-directional data exchange and real-time self-management (e g , self-awareness or self-optimisation) distinguish a DT from other information modeling sys-tems The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19 This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems It also intends to review the state of play in DT development and offer research directions for future investigation It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19 The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors © 2021 by the author Licensee MDPI, Basel, Switzerland

Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment

Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.

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The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Latest trends and developments in digital technologies have enabled a new manufacturing model. Digital systems can monitor, optimize and control processes by creating a virtual copy of the physical...

Digital twin for smart manufacturing: a review of concepts towards a practical industrial implementation

There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects.

Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction

Production systems play an important role in intelligent manufacturing. A large number of manufacturing resources are designed and developed with virtual (digital) ones, which will be associated with the physical ones throughout their lifecycle. With the recent emergence of information and communications technologies (ICTs), such as internet of things, big data, virtual reality, artificial intelligence, and 5G, the interconnection and interaction between physical resources and virtual ones become possible in production systems. Digital twin (DT) shows great potential to realize the cyber-physical production system (CPPS) in the era of Industry 4.0. In this paper, we present our vision on integrating various physical resources into CPPS via DT and AutomationML. To elaborate on how to apply ICTs, this paper firstly explores a generic architecture of CPPS based on DT. DT is a virtual and authoritative representation of physical manufacturing resource, since DT includes various models and manufacturing big data of resource. The proposed architecture is illustrated in detail as follows: (1) physical layer, (2) network layer, (3) virtual layer, and (4) application layer. A case of expert fault diagnose for aircraft engine is presented using the proposed information fusion in the architecture. Secondly, this paper proposes an approach of information modeling for CPPS based on AutomationML. Various manufacturing services can be encapsulated and defined in the standardized format (AutomationML), and then the corresponding virtual manufacturing resources (DTs) will be integrated into CPPS. Finally, this paper describes a case of information modeling for blisk machining and demonstrates the modeling approach in real-life scenarios for support manufacturing resource sharing via DT. Furthermore, the conclusion and further work is briefly summarized.

Haijun Zhang

Papers

Information modeling for cyber-physical production system based on digital twin and AutomationML