Abstract
The contact profile of a train wheel has a key role in its operation performance. Rolling smoothly and with reduced resistance results in an increase in the efficiency and safety of rail transport. The original shape and dimensions of the profile of the wheel are altered under operation of the train, especially due to braking events and the presence of external objects between the wheel and the railway. With the purpose of recovering the optimum contact profile, train wheels are periodically machined using special lathes. This repair operation is particularly critical in freight trains, which are only reshaped a few times throughout their service life and, therefore, high depths of cut are required to recover the wheel in a productive way. As the presence of chatter vibrations limits the productivity of these operations, a hybrid edge–cloud computing approach has been developed for chatter vibration suppression. An expert system based on automatic chatter detection and suppression has been developed in the edge. The expert system is based on continuous real-time vibration monitoring and combines continuous spindle speed variation (CSSV) and cutting speed reduction to suppress chatter. Cloud computing is used to extract wheel profile machining fingerprints and obtain insights from multiple aggregated machined wheels. An industrial implementation of the system is described in the present work.
Highlights
The maintenance and periodic replacement of railway wheelsets represents a significant cost faced by train operating companies
TheThe objective wasresponse to identify the vibration modes responsible for the during frequency functions (FRF) of the machine were obtained by chatter means ofvibrations impact tests
An expert algorithm which ran in this real-time programmable automation controller (PAC) processed this signal and when the machining becomes unstable, it commanded an action from the computer numerical control (CNC)
Summary
The maintenance and periodic replacement of railway wheelsets represents a significant cost faced by train operating companies. The reprofiling of the worn wheels and the machining of the brake-disc are the usual main corrective actions. The problem arises when trying to reshape severely damaged freight train wheelsets, where the high deterioration of the tread profile requires a high depth of cut turning operation. This fact, linked to the usual forged steel material of the wheel and its hardened outer layer, makes machining of the parts very demanding. The correct behavior of the system was experimentally validated through industrial real wheel reprofiling tests where the vibration of the process was removed through the smart system and the turning process was optimally performed. The data were remotely accessed and analyzed allowing a forensic examination
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