Empowering SMEs with Cyber-Physical Production Systems: From Modelling a Polishing Process of Cutlery Production to CPPS Experimentation

2020 
As technology evolves, the contemporary technological paradigm of manufacturing Small and Medium Enterprises (SME’s) has been changing and gaining traction to accommodate its ever-growing needs for adaptation to modern industrial processes and standards. Their willingness to integrate Cyber-Physical Systems (CPS) modules in their manufacturing processes and to implement Cyber-Physical Production Systems (CPPS) is firmly based on the perception that value added services result from the technological evolution and, in the future, better tools are expected to guarantee process control, surveillance and maintenance. These Intelligent Systems involve transdisciplinary approaches to guarantee interaction and behavioural fluidity between hardware and software components which often leads to complexity in the coordination of these components and processes. The main objective of this work is to study these aspects and to contribute with data regarding the applicability of CPS components in the current SME’s environment with the intent of improving the performance of manufacturing processes. To accomplish this, an architecture is proposed, which is based in the process modelling and process simulation of the different stages of an existing SME factory production, allowing real-time information, through IoT data collection, to feed different mechanisms of production improvement modules such as planning, scheduling and monitoring. Since SME’s are the most active and common company profile in the northern part of Portugal, it seemed beneficial to take part in this environment, by implementing the solution in a promising cutlery producing SME, with the objective of investigate and validate the applicability of the BEinCPPS components, within the proposed architecture, to improve the performance of the company industrial processes.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    34
    References
    3
    Citations
    NaN
    KQI
    []