Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue.
Majstorovic, V., Stojadinovic, S., Jakovljevic, Z., Zivkovic, S., Djurdjanovic, D., Kostic, J., & Gligorijevic, N. (2018). Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue. In Procedia CIRP (Vol. 72, pp. 503–508). Elsevier B.V. https://doi.org/10.1016/j.procir.2018.03.091