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Concepts
Published in Gideon Samid, Computer-Organized Cost Engineering, 2020
Cluster analysis is a competitive approach that attracts cases in which the search for the coefficients leads to overheated computers but nothing else. Cluster analysis is based on the notion of distance. In the previous example, one could envision a five-dimensional space, and on it each project is represented as a point. If it happens that all the good projects are found clustered in one area (of that space) and all the bad ones form a cluster of their own (elsewhere of course), then a considered project will be declared good or bad on the basis of its joining one of these clusters.
Digital twin in manufacturing: conceptual framework and case studies
Published in International Journal of Computer Integrated Manufacturing, 2022
Igiri Onaji, Divya Tiwari, Payam Soulatiantork, Boyang Song, Ashutosh Tiwari
Tao et al. (2018) presented a digital twin-driven product design (DTPD) framework. This serves as a guide on the creation of a product digital twin and the utilisation of its generated knowledge in the product design process. Zhang et al. (2019a) proposed a data and knowledge-driven digital twin framework for a manufacturing cell (DMTC). This supports an autonomous manufacturing cell using data for the perception of manufacturing problems and knowledge for solving identified problems. This has five-dimensional space namely the physical, digital, data, knowledge and social space. This framework is expected to support self-thinking, self-decision-making, self-execution and self-improving. Cheng et al. (2018) also present the aims of a smart factory for the fourth manufacturing generation. In this case, the digital twin concept is used to achieve physical connection and data collection, virtual models and simulations, data and information technology systems integration and lastly, databased production operations and management methods. These expectations are also embraced by other authors like Ellgass et al. (2018), Qi et al. 2018a) and Zhang et al. (2019a).