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Groan is a low frequency noise generated when moderate brake pressure is applied between the surfaces of the brake disc and the brake pad at a low-speed condition. Brake groan is often very intense and can cause large numbers of customer complaints. During a groan noise event, vehicle structure and suspension components are excited by the brake system and result in a violent event that can be heard and felt during brake application. The cause of noise is friction variation of stick-slip phenomenon between friction material and disc. Creep groan is the structure-borne noise that is related to dynamic characteristic of the vehicle. However, it has been mainly improved through friction material modifications in the past. In this paper, transfer path of creep groan noise was analyzed by means TPA and structural countermeasure to creep groan noise was suggested. This paper discusses the approach for prediction and mitigation of brake groan noise for passenger vehicles having disc brakes.

In the context of vehicular safety and performance, brake pads represent a critical component, ensuring controlled driving and accident prevention. These pads consist of friction materials that naturally degrade with usage, potentially leading to safety issues like delayed braking response and NVH disturbances. Unfortunately, assessing brake pad wear remains challenging for vehicle owners, as these components are typically inaccessible from the outside. Moreover, Indian OEMs have not yet integrated brake pad life estimation features. This research introduces a hybrid machine learning approach for predicting brake pad remaining useful life, comprising three modules: a weight module, utilizing mathematical formulations based on longitudinal vehicle dynamics to estimate vehicle weight necessary for calculating braking kinetic energy dissipation; and temperature and wear modules, employing deep neural networks for predictive modeling.