MACHINE LEARNING FOR DECISION-MAKING IN THE REMANUFACTURING OF WORN-OUT GEARS AND BEARINGS

Abstract

Mechanical industries use rotating mechanical equipment in their day to day operations. The equipment suffers from wear and tear, and is usually discarded as scrap. But is there a way to recover some of this equipment and reuse it? This paper uses machine learning to capture and analyse the wearing damage of bearings and gears to determine whether they can be redeemed. Finite element analysis is conducted on worn-out spur gears and pillow bearings in order to facilitate feature extraction in image processing algorithms. This converts the actual gears, bearings, and seals into CAD files. The decision-making system is designed, and it uses these CAD files to decide on the optimum manufacturing process to restore redeemable components. The mechanical components of the system are designed using SOLIDWORKS. MATLAB, Proteus software, and the Arduino micro-controller are used for the system application design and simulation. The results from tests conducted on a worn-out gear and bearing show that the gear is 4% non-redeemable, while the bearing is 60.2% non-redeemable. The decision taken by the system is to redeem the gear and to discard the bearing.