Data quality management for service-oriented manufacturing cyber-physical systems

Formal modeling and control of cyber-physical manufacturing systems

“…Lee et al 28 introduce cyber manufacturing in which CPSs are the core driving technologies. Song et al 29 present a service-oriented manufacturing cyberphysical system (SMCPS) which aims to provide highquality products and excellent services for customers. Data quality management policies for defective data in SMCPS are developed.…”

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

“…While some approaches can be found in the literature to improve specific dimensions of data quality [93], the assessment and measurement of these dimensions have historically relied on self-report surveys and user questionnaires [94] due to their association with subjective and situational judgements for quantification. Thus, further research is needed to jointly improve the way the different dimensions of data quality can be monitored and optimized, as this will likely have a direct impact towards improving the performance of Industrial AI applications leveraging these data.…”

Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper

“…• efficiently and cost-effectively converting manufacturing resources and capabilities into services and placing them in cloud-based platforms (Zhong et al, 2017); • the development of effective algorithms of managing resource service transactions (Tao et al, 2012); • the development of optimisation algorithms and strategies to support comprehensive QoS management to achieve high quality and efficiency (He & Wu, 2015); • QoS-based service composition selection in a cloud manufacturing system (Zhong et al, 2017); • the introduction of semantic models for manufacturing resources and capability servitisation and data sharing (Xie et al, 2017); • the development of the algorithms for data quality management (Song et al, 2017); • the elaboration of optimisation algorithms for optimal service composition in the cloud environment with the consideration of service correlations (Zhou & Yao, 2017); • manufacturing services configuration (Zhong et al, 2017); • robust service compositions that are autonomously reconfigured with minimal human intervention (Wu et al, 2013); • service encapsulation and virtualisation access models for manufacturing machines, combining the Internet of Things techniques and cloud computing (Zhang et al, 2017); • protocol, safety and security, reliability, and management techniques of application in CMfg systems (Yuan et al, 2017); • the development of the trust evaluation models increases the credibility of service transaction trust evaluation in CMfg systems (Yan et al, 2015). Beside cloud-based manufacturing, the authors also identified the most recent manufacturing concepts that appeared in literature: smart manufacturing, sustainable manufacturing, green manufacturing, social manufacturing, nanomanufacturing, additive +3d printing manufacturing, advanced manufacturing, remanufacturing and cyber-physical systems.…”