Predicting Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data

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 torque-induced friction behavior 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 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 pad material improvements 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. Environmental conditions and brake system characteristics are hypothesized to influence mechanical behavior. Derived features exhibiting high causality to squeal occurrence were developed. A machine learning model was trained to predict squeal occurrence probability from these engineered features. The model's predictive performance was validated by comparing squeal probability predictions from independent torque performance evaluation data against actual squeal evaluation results. This validation demonstrates that the model successfully predicts squeal probability from dynamometer torque performance data alone. Consequently, this approach enables squeal prediction in early development phases before formal noise assessment is conducted, streamlining development processes and significantly reducing verification costs while delivering quieter driving experiences.