Brake Noise CAE Enhancement with the Synergy between Design of Experiments and Machine Learning

<div class="section abstract"><div class="htmlview paragraph">The digitalization of industrial systems has led to increased data availability. Machine learning (ML) methodologies are now commonly used for data analysis in industrial contexts. Not all contexts have abundant data; sometimes data collection can be scarce or expensive. Design of Experiments (DOE) is a technique that provides an informative dataset for ML analysis when data are limited. It involves systematically designing experiments to collect relevant data points with regression models.</div><div class="htmlview paragraph">Disc brake noise is a challenging problem in vehicle noise, vibration, and harshness (NVH). Different noise events occur under various operating conditions and across frequencies (1-16 kHz).</div><div class="htmlview paragraph">To enhance computer-aided engineering (CAE) techniques for brake noise, ML is used to generate additional data. Sequential experimentation in DOE aligns well with ML’s ability to continuously learn and improve as more data become available. DOE is applied in CAE to collect data for training ML models. ML helps find patterns in high-dimensional data by modeling relationships between operating conditions and related variables, which are then validated and correlated with CAE results. In summary, combining DOE with ML can address data scarcity and enhance predictive models, especially in complex industrial scenarios like brake noise analysis, and makes it valuable for industrial experiments.</div></div>