Abstract:
Metal Working Fluids (MWF) are crucial for both lubrication and cooling during operation of machinery processes. MWF undergo changes over time due to various factors such...Show MoreMetadata
Abstract:
Metal Working Fluids (MWF) are crucial for both lubrication and cooling during operation of machinery processes. MWF undergo changes over time due to various factors such as evaporation or the growth of bacteria. Due to these changes, a variety of additives are added over the service life of the fluids to ensure that the properties remain at the optimum for the end-user. At present, MWF assessments are carried out using either hand-held devices or using automatic analysis systems. Manual assessment is done at best once a week which results in irregular testing and deterioration of fluids. Whilst the automated systems available can analyze the parameters without human intervention, they lack predictive capabilities and still require operators to dose and pour the additives. MWF and additives both pose occupational health risks to the workers. During metalworking, the degraded MWF affects the quality of the metal piece; the life of the equipment tool and the health of the workers. A present, there are no artificial intelligence technology systems available on the market able to autonomously predict fluid degradation events and manage the fluids themselves. We propose an automated system able to fulfill the above-mentioned specs, also developing an IA algorithm able to produce a forecast concerning the WMF degradation over time, in order to develop an adequate predictive maintenance.
Published in: 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)
Date of Conference: 09-12 September 2019
Date Added to IEEE Xplore: 11 November 2019
ISBN Information: