Authors:
Ivan Peinado-Asensi
1
;
2
;
N. Montes
2
and
E. García
1
Affiliations:
1
Ford Spain, Polígono Industrial Ford S/N, 46440, Almussafes, Valencia, Spain
;
2
Mathematics, Physics and Technological Sciences Department, University CEU Cardenal Herrera, C/ San Bartolomé 55, 46115, Alfara del Patriarca, Valencia, Spain
Keyword(s):
Smart Stamping Plant, Real-time Data Analysis, Machine Health Monitoring, Predictive Maintenance.
Abstract:
Automotive companies are going through a rough time due to the decrease in the car sales market, therefore OEMs trend is cost reduction in the next years over improving efficiency increasing digitalization, implementing new industry 4.0 technologies to turn their facilities in smart factories. Within car manufacturing processes, stamping present many possibilities for development, in this paper an approach to bring stamping plants closer to smart factories is presented. The most common problems in stamping are unexpected breakdowns in equipment and poor quality parts produced, to avoid these problems corrective and predictive maintenance tasks are carried out to improve presses and tools performance. One of the critical maintenance tasks in press machines are parallelism, a malfunction in the kinetic transmission can lead to high cost and duration breakdowns. To monitor machine working parameters a novel method is presented using IIoT techniques, having access to machine working para
meters in Real-Time to predict machine malfunction in order to reduce the number of breakdowns.
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