The automotive industry's paradigm shift toward autonomous driving and electrification has introduced new competitors threatening market dominance through differentiated value propositions. In this highly competitive landscape, delivering irreplaceable customer value requires providing sustainable and authentic luxury experiences. Quiet driving represents a tangible value that customers genuinely appreciate. Brake squeal—high-frequency noise arising from friction-induced vibration during braking—negatively impacts customer satisfaction and must be suppressed.
Despite significant advances in brake squeal prediction modeling, the irregular nature of squeal generation mechanisms has prevented the development of a generalized predictive model applicable to product development processes. Development and verification remain largely experimental. This limitation constrains early-phase design validation, as brake squeal is highly sensitive to chassis and braking system design. When squeal issues emerge during post-design evaluation, fundamental improvements to pad materials become difficult. Consequently, damping characteristic tuning is employed for mitigation, incurring substantial development costs.
This study addresses this challenge through systematic feature engineering of time-series braking data—brake torque, disc rotational speed, disc temperature, and brake pressure—collected during squeal evaluation tests. Based on the hypothesis that environmental conditions and brake system characteristics influence mechanical behavior, time-series features exhibiting strong predictive association with squeal occurrence were derived, and a machine learning model was developed to predict squeal occurrence probability using these features as input variables.
The model's predictive performance was validated by comparing squeal probability predictions derived from independent torque performance evaluation data against actual squeal evaluation results. This validation confirms that the model successfully predicts squeal occurrence probability from dynamometer torque performance data alone. Consequently, this approach enables the prediction of squeal occurrence probability in early development phases before formal noise assessment is conducted, streamlining the development process and significantly reducing verification costs while contributing to quieter driving experiences.