An industry 4.0-enabled low cost predictive maintenance approach for SMEs: a use case applied to a CNC turning centre

Date published

2018-08-16

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IEEE

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Conference paper

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Citation

Erim 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, Germany

Abstract

This 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.

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Software Description

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Github

Keywords

e-Maintenance, Predictive Maintenance, Condition Based Maintenance, Industry 4.0, Smart Manufacturing, Machine Learning, Small Manufacturing Enterprise, Low Cost, Open Source

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Attribution-NonCommercial 4.0 International

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