Sezer, ErimRomero, DavidGuedea, FedericoMacchi, MarcoEmmanouilidis, Christos2019-03-262019-03-262018-08-16Erim Sezer, David Romero, Federico Guedea, et al., An industry 4.0-enabled low cost predictive maintenance approach for SMEs: a use case applied to a CNC turning centre. 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 17-20 June 2018, Stuttgart, Germany978-1-5386-1469-3https://doi.org/ 10.1109/ICE.2018.8436307https://dspace.lib.cranfield.ac.uk/handle/1826/14010This paper outlines the base concepts, materials and methods used to develop an Industry 4.0 architecture focused on predictive maintenance, while relying on low-cost principles to be affordable by Small Manufacturing Enterprises. The result of this research work was a low-cost, easy-to-develop cyber-physical system architecture that measures the temperature and vibration variables of a machining process in a Haas CNC turning centre, while storing such data in the cloud where Recursive Partitioning and Regression Tree model technique is run for predicting the rejection of machined parts based on a quality threshold. Machining quality is predicted based on temperature and/or vibration machining data and evaluated against average surface roughness of each machined part, demonstrating promising predictive accuracy.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/e-MaintenancePredictive MaintenanceCondition Based MaintenanceIndustry 4.0Smart ManufacturingMachine LearningSmall Manufacturing EnterpriseLow CostOpen SourceConference paper