A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Psarommatis, Foivos

doi:10.1016/j.jmsy.2021.03.021

Cited by 90 publications

(46 citation statements)

References 43 publications

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“…Within the various presented concepts and frameworks [ 34 , 35 , 36 , 37 , 38 , 39 ], automated production systems, including mixed reality assistance systems [ 40 , 41 ], could be rapidly modularized [ 42 ] and reconfigured [ 43 , 44 , 45 ], enhanced with AI [ 46 , 47 ] and sensors [ 48 , 49 ] and, in combination with cloud and edge computing [ 50 ], transformed into distributed control systems, while detailed production environments can be generated and updated in the form of 3D point clouds [ 51 , 52 , 53 , 54 , 55 , 56 ]. Based on these infrastructures, DT demonstrates the capability of handling increasingly complex operational problems, such as production planning and scheduling [ 57 , 58 , 59 , 60 ], production monitoring and control [ 61 , 62 , 63 , 64 , 65 , 66 ], quality control and management [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ], as well as logistics [ 76 , 77 , 78 ], supply chain management (SCM) [ ...…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

162

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

162

View full text Add to dashboard Cite

show abstract

“…DT is defined by NASA as an integrated multiphysics, multiscale simulation of a vehicle, or system with the best available physical models to mirror the life of its corresponding flying twin (Shafto et al 2012). As one of the essential supporting technologies for the construction of cyber-physical space (CPS), DT has been applied in many fields such as aerospace (Shafto et al 2012), intelligent manufacturing (Psarommatis 2021), supply chain (Chen et al 2020), and the Internet of Things (Chen et al 2019).…”

Section: Definition Of Cognitive Twinmentioning

confidence: 99%

Co-simulation of complex engineered systems enabled by a cognitive twin architecture

Chen

et al. 2021

International Journal of Production Research

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Since the complex engineered system involves multi-disciplinary, co-simulation is the key technique to the performance analysis. However, the co-simulation is hindered by heterogeneous sub-systems and ununified environments. In this paper, a Cognitive Twin (CT) to support the co-simulation of the complex engineered system is introduced. It is a generic approach that can be applied in many complex engineered systems such as the aerospace field, automotive system, the Internet of Things, manufacturing systems, etc. CT adopts an ontology model to develop cognition capability based on CT architecture. Then, a unified ontology modelling approach based on GOPPRR (graph, object, point, property, role, relationship) is presented to support an accurate semantic description of the topology between digital entities that use FMI 2.0 as the interconnection standard. Besides, four types of information are included in the ontology model to form the knowledge in co-simulation. Finally, the co-simulation is automatically executed using the cognition capability. Furthermore, a master-slave algorithm is deployed to establish a unified co-simulation environment. The flexibility of CT is evaluated using a gas turbine case. The results demonstrate that the complication in the cosimulation of complex engineered systems is solved by the unified ontology modelling approach and the architecture of CT.

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“…When a disruptive event occurs, the DSS and the dynamic scheduling tool interact to produce a new schedule. This solution was evaluated based on product quality and other KPIs (Psarommatis, 2021). A more generic model that links scheduling with ZDM is proposed by Dreyfus and Kyritsis (2018), who aim to increase production capabilities without large investments.…”

Section: Product or Quality-oriented Reschedulingmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

Self Cite

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Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

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A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM

Cited by 90 publications

References 43 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Co-simulation of complex engineered systems enabled by a cognitive twin architecture

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

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