Predictive Maintenance for Quality Control in High Precision Processes

Predictive Maintenance for Quality Control in High Precision Processes

María Carmen Carnero, Carlos López-Escobar, Rafael González-Palma, Pedro Mayorga, David Almorza
DOI: 10.4018/978-1-4666-8222-1.ch009
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In external grinding processes, vibrations induced by the process itself can lead to defects that affect the quality of the parts. The literature offers models that cannot include all process variables in the analysis. This research applies theoretical models and experimental analysis to determine their suitability for predicting the chatter profile of parts in a plunge grinding process. The application of variance analysis to overall vibration value induced by grinding wheel-workpiece contact allows us to show that high frequency displacements vibration are sensitive to the process setup as well as to the quality of the products manufactured. The final statistical analysis has provided a determination of the spectral bands of the process in which the vibrations causes by grinding wheel-workpiece contact influence the existence of flaws in the workpieces. The methodology described can contribute to increasing the environmental sustainability of an industrial organization.
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Maintenance has a significant impact on the environmental sustainability of an organization, by, for example using more efficient and less wasteful machines and devices, using energy-saving candle bulbs or light bulbs, incorporating sensors into devices to limit or reduce water consumption, installing movement sensors to control lights outside working hours, recovery usable parts from unusable machines, recycling paper and cardboard used in normal operations or removing the need for documents based on paper and replacing them with computer-based files and procedures, and especially, increasing the useful life of machines and devices by better maintenance, such as predictive maintenance.

Predictive Maintenance is a maintenance policy based on measuring and recording intermittently or continuously (on-line) certain physical parameters, including vibration, temperature, etc., associated with a working machine to obtain data and information through which failures can be detected and the future state of the machine can be determined as a function of the load to be applied to the equipment or process, that is, a prediction of the remaining life of the machine (Rao, 1996).

There exist a number of predictive diagnostic techniques, such as (Carnero, 2013; Mobley, 2001a; Scheffer & Girdhar, 2004):

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