{"id":2418,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=2418"},"modified":"2021-07-13T20:19:19","modified_gmt":"2021-07-13T18:19:19","slug":"manufacturing-process-impacts-on-occupational-health-a-machine-learning-framework","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/biological-transformation-sustainability\/manufacturing-process-impacts-on-occupational-health-a-machine-learning-framework\/","title":{"rendered":"Manufacturing Process Impacts on Occupational Health: a Machine Learning Framework"},"content":{"rendered":"\n

by Alessandra Caggiano, Rebecca Grant, Changxin Peng, Zhijie Li, Alessandro Simeone<\/em> (Italy, China, UK)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

The Operator 4.0 generation represents a smart and skilled operator performing “cooperative work” with robots, machines and cyber-physical systems. In this taxonomy, a healthy operator is an operator equipped with wearable technology to monitor biometrics in a workplace to monitor and ideally prevent urgent threats to safety, stress in manufacturing and production quality as a result. In a digitalized healthcare context, a cloud manufacturing platform for occupational health assessment, capable of collecting physiological, environmental and manufacturing process data can potentially enable prompt action to prevent fatalities. With reference to manufacturing industry in this paper, a number of critical operations are identified to be potentially hazardous to operator health, such as the employment of cutting fluids in machining processes. This paper proposes a novel machine learning-based framework and associated methods to classify physiological data acquired using wearable sensors during manufacturing work, to be utilized in an expert system (fuzzy) to determine the risk category of health implications for Operator 4.0. The classification algorithms are investigated to evaluate their industrial suitability, and a manufacturing case study is illustrated to exemplify the proposed methodology.<\/p>\n\n\n\n

Keywords<\/strong>: Operator 4.0, Cloud manufacturing, Wearable technology, Health risk assessment<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n