Clutch Housing Temperature Prediction during Repeat Restart Test
Using Machine Learning 2023-01-5087
Product validation time reduction and limit number of physical testing is major
challenge all over the world OEMs are facing and they are trying to use latest
technologies to fill gap between design parameters, simulated results, and
physical validation results. Automotive industry is going through a major
transformation with use of artificial intelligence and machine learning and
especially in the area of transmission system design and development where lot
of data is available from physical testing. Clutch is still being used in
internal combustion engines vehicles. Clutch is an important part in
transmission system in vehicle, which transmits power generated from engine to
transmission and changes the gears at different speed. Design and validation of
clutch is a critical and laborious task. Clutch failure occurs due to excessive
rise in temperature. The motivation behind this work is to reduce clutch design
and selection cycle time and iteration, since physical testing and CAE iteration
are a time consuming and costly process for any automotive OEMs. Physical
testing requires manpower and test setup. If clutch design engineer knows
temperature inside clutch housing before design finalization then corrective
actions can be taken to avoid failures during field trials by providing early
feedback from results predicted by machine learning model. This paper focuses on
prediction of clutch housing temperature during physical validation of a clutch
using machine learning. Historical data of different vehicles is used for
development of machine learning model. The current study focuses on the machine
learning approach for prediction of clutch housing temperature. The machine
learning methodology and results correlation between machine learning predicted
results and physical test results for different types of commercial vehicles.
The developed solution using machine learning helps clutch design engineer in
selection of critical clutch parameters so that clutch design can be improved at
early vehicle design stage.