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Highly competitive automotive market demands shorter product development cycle while maintaining higher standards of performance in terms of durability and Noise Vibration & Harness (NVH). Engine cranktrain system is one of the major vibration sources in engine and first torsional mode frequency is a key parameter which influences vibration characteristics. Current CAE (Computer Aided Engineering) workflow for evaluating cranktrain system performance is time-consuming and takes around 55 Hrs. It involves crankshaft geometry cleanup, stiffness calculation, 1D model building and post processing. Over the time, significant historical data has been created while performing this virtual simulation during the product development cycle. Having a trained Machine Learning (ML) model based on this historical data, which can predict first torsional mode frequency accelerates the virtual validation. In this paper, prediction of first torsional frequency of cranktrain system using ML is presented.

Shift quality of a manual transmission is a critical characteristic that is requires utmost care by the designers while structuring the transmission. Shift quality is affected by many factors viz. synchronizer design, shift fork design, shifter design, gear design, transmission oil selection etc. Designers have realized that shift fork is critical element for improving shift feel of a transmission. This paper focuses upon the reduction in weight of the overall transmission shift system by using steel inserts in aluminum shifter forks. No compromise on the stiffness and strength of the shift fork of a manual transmission is done. Stiffness and strength of shifter fork is optimized using contact pattern analysis and stiffness analysis on MSC Nastran. All the subsystem (i.e. synchronizer and the shift system component) are constrained to optimize the shift fork stiffness. A 5-speed manual transmission is used as an example to illustrate the same.