# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 1 Introduction  
Owing to their low cost and high operational efficiency, dry friction clutch systems have been widely used in automotive manual transmissions, but also in Automated Manual Transmissions (AMT) [1], Dual Clutch Transmissions (DCT) [1, 2], continuously variable transmission (CVT) [3] and electric systems [4]. On the other hand, in the absence of active cooling of friction surfaces in dry clutches, they experience intense temperature variations, affecting the clutch friction lining wear [5] and clutch torque controllability [6]. The literature mostly deals with experimental analyses of wet clutch wear. Several wear mechanisms are identified in [7] for copper-based friction material in wet conditions, which are micro-plowing, plastic deformation, abrasive wear and delamination wear. In the first stage of clutch life for paper-based friction materials, the friction contact temperature is found to have dominant influence on clutch wear, while later in the clutch life mechanical parameters also become influential [8]. As different friction contact areas can experience different temperatures, the temperature distribution along the friction surface is analyzed in [9], both numerically and experimentally using a pin-on-disk tribometer. On the other hand, investigations made for organic friction materials for dry clutches are often focused on influence of individual component in composite material like silicon carbide and graphitefillers [10], different resin types [11] or possibility of replacing unsustainable fibers with rattan fibers [12]. Several wear mechanisms are also analyzed for dry friction clutches, including adhesive wear [13], oxidative wear and abrasive wear [14]. Influence of normal force and slip speed is investigated and described using a wear mechanism map in [15]. During regimes of low and mild slip speeds and pressures the tribofilm on the friction surface is naturally formed which helps to stabilize coefficient of friction and control wear [16]. During aggressive usage patterns in real-world operation rapid wear was observed in [17]. Influence of different groove patterns on clutch life is investigated in [18]. Distribution of contact pressure and wear over the friction lining surface is described in [19]. A physical model of steel-on-steel adhesive wear mechanism is developed in [13], where the processes of line sliding, accumulation and transfer of material are analyzed. A data-driven model is proposed in [20] to describe the wet clutch wear behavior depending on the number of clutch engagements. The investigation of wear processes in automotive industry is often focused on braking system. Recent research has demonstrated the potential of pyroelectric materials for detection and prediction of brake pads wear [21]. These materials also possess the ability to generate electric energy for low-power sensor supply [22]. Similar materials to those used in clutch linings are used for braking system, and it has been shown that the wear rate is strongly dependent on temperature, pressure and sliding speed [23]. This paper deals with experimental characterization and data-driven modeling of organic friction material abrasive wear for dry clutches. The core of the proposed wear model corresponds to a wear rate regression submodel with four inputs corresponding to single clutch closing cycle features: average clutch interface temperature, initial slip speed, average clutch torque, and closing time. The experimental data used for wear rate model parameterization have been collected by using a custom-designed disk-on-disk tribometer and real clutch friction materials to make the wear conditions realistic [24]. The predicted wear rate is multiplied with the dissipated friction energy to calculate the incremental worn volume, which is then integrated to predict the cumulative worn volume. As a wear process is a subject of variability, the wear expectation model is accompanied with a variability model aimed to provide worn volume variance and related 95% confidence interval (CI).