Wear 261 (2006) 269–282
Prediction of automotive friction material characteristics using artificial neural networks-cold performance ˇ Duboka D. Aleksendric ∗ , C. Automotive Department, Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade 35, Serbia and Montenegro Received 3 March 2005; received in revised form 28 September 2005; accepted 24 October 2005 Available online 15 December 2005
Abstract In this study, an artificial neural network technique was used to predict the cold performance of the automotive friction material. Cold performance was predicted for two cases: (i) before and (ii) after fading and recovery tests. Predictions were related to the brake factor C values versus 26 input parameters. The input parameters are defined by the friction material formulation (18 parameters), manufacturing conditions (5 parameters), and testing conditions (3 parameters). For these predictions, the five types of the friction materials were produced and tested. The quality of prediction has been evaluated by comparison of the real results obtained during testing on the single-end full-scale inertia dynamometer and predicted ones. The 15 different architectures of the artificial neural networks have been investigated. The five training algorithms have been employed for the artificial neural networks training. © 2005 Elsevier B.V. All rights reserved. Keywords: Artificial neural network; Friction characteristics; Cold performance; Formulation; Manufacturing; Testing
1. Introduction In the automotive industry today, the brake performance of a vehicle is decisively influenced by the friction system of brake pad against a cast iron disc regarding: (i) safety, (ii) sophisticated braking comfort (noise, vehicle vibration, pedal feel, smoke development, etc.), and (iii) long serviceable life. The demands imposed to the friction system and particularly on the brake pad behaviour under wide range of operating conditions are high and manifold. Different vehicle’s weights, four-wheel drive vehicles, and different vehicle’s maximum speeds causing that braking systems capabilities have to be constantly improved. It is additionally complicated by introducing add-on systems like ABS, ASR, ESP, Brake assist, etc. It should be obvious that different vehicles have different requirements imposed to the friction material as a part of braking system. One or two friction formulations cannot meet aforementioned requirements for different vehicles family. Therefore, it is necessary to develop the friction material that can be able to satisfy predetermined requirements for the specific vehicles families. It is especially related to: (i)
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short bedding period, (ii) stability of cold friction coefficient, (iii) stability of friction coefficient during speed changes, (iv) stability of friction coefficient under temperature load, (v) stability of friction coefficient after temperature load, (vi) stability of friction coefficient under pressure activation changes, (vii) static and dynamic friction coefficient, (viii) friction coefficient under wet conditions, (viii) mechanical characteristics (compressibility, shear strength, bending strength, etc.). There is a wide range of ingredients that have been used by friction material manufacturers. According to [1], more than 2000 different raw materials and their variants are now used. However, friction material manufacturers to produce friction materials for braking systems currently use approximately 150 different ingredients. The types and relative amounts of the ingredients in a commercial brake friction material are determined by considering many performance-related issues such as friction force, noise propensity, aggressiveness against gray cast iron rotors, brake-induced vibration, wear, etc. [2]. The automotive brake friction material usually contains 10–25 different raw material ingredients [3,4] to meet requirements for reliable and comfortable brake performance at wide range of applied pressures, temperatures, humidity, and sliding speeds [3]. Since, the number of requirements imposed to the friction materials and number of ingredients used in their production have been
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increasing all the time, capabilities for the automotive friction materials characteristics predicting have to be improved. Prediction on the automotive friction material behaviour under different operating conditions is complicated by the fact that braking process has stochastic nature affected by changing of real contact area size, transfer layer existence between friction pair, changing pressure, temperature, speed, deformation, and wear. Performance of a brake system depends on the interaction of gray iron rotors with brake linings at their sliding interfaces, involving complicated mechanical and chemical actions [5]. The size of the area of real contact between the pad and the disc is far from constant [4], very small compared to the total contact area [6], and highly dependent to changes of pressure, temperatures, deformation, and wear. These micro points are dynamically changing from place to place in fraction of seconds during braking according to [7]. Due to these very complex contact conditions, effects of material properties on sliding contact-braking applications need to be investigated, [8] for example. Besides a complex contact situation, it is established in [9] that the formation and stability of transfer films on the counterface play important role in the friction and wear behaviour of polymers or polymers composites sliding against a metal surface. Furthermore, according to [10], the durability of the friction film at the friction interface above the decomposition temperature of the binder resin appears very important in the brake performance and wear resistance of the friction material. The friction film composition mainly influences the friction characteristics, but is not clear, according to [11], why transfer films are selectively formed. The thickness and surface morphology of the transfer film are highly dependent on the temperature, applied pressure, and chemical state of the ingredients in the brake lining because cohesion of constituents in the film and film rheology are different at different sliding conditions is claimed in [12]. It is important because the temperature at the friction interface strongly affects the properties of the transfer film, resulting in continuous variation of its thickness and composition as a function of sliding time. According to [13], friction performance was found to be independent of transfer film thickness but sensitive to transfer film composition. It is obvious that the mechanism of the friction film formation is very complicated and strongly depends on the thermal history of the sliding interface (organic constituents, fibrous materials, and solid lubricants play important role in establishing the transfer layer at the friction interface), which is concluded in [14]. It is evident according to Refs. [3,4,9,15], for example that friction and wear processes are highly dependent on ingredients in the friction material, and due to special demands, as it is pointed out in [16], friction materials have evolved into very complex structures. However, scientific approaches to obtain an optimum formulation for an enhanced brake performance are difficult to find in the literature. As it is explained in [17], this is partly due to the difficulties of handling huge amounts of experiments to obtain reliable friction properties as a function of the amount of each ingredient. Hence, performance assessment and optimization is a conflicting decision making task. That is why, in [18] attention has been paid on the use of multiple criteria
optimization by ranking and balancing method for the selection and design of an optimal composite. On the other hand, optimizations with respect to friction material composition have to be done by trial and error method has been suggested in [19]. Reason for this, lies in the fact that the connection between component mixture and friction layer and the connection between friction layer and friction behaviour of the system is complex and not known yet, according to [19,20]. Thus, the basic problem is how to develop advancedengineered friction formulation, define the most appropriate manufacturing parameters under reduced time and cost conditions, and increased the number of requirements related to the final friction material performance. Resolving of this problem needs modelling, i.e. prediction of effects influenced by friction formulation changes and/or production process changes and/or testing condition changes. It is evident that a method for simultaneous predicting on influences of the friction material formulations, manufacturing and testing conditions is needed. According to [21,22], modelling of material properties generally involves the development of a mathematical model derived from experimental data. For this reason, the method of artificial neural networks (ANN) was recently introduced into the filed of material science. For instance, in [23] artificial neural networks have been applied as a tool for systematic parameter studies in the optimum design of composite material. ANN technique has been applied on different engineering problems. For example, modelling of the wear of polymer composites in [21], analysis of dynamic mechanical properties of PTFE in [22], or for predictions on erosive wear of polymers described in [24]. Heretofore, not including all influencing factors (composition, manufacturing, and testing) which affecting the properties of polymer composites can be considered as the lack of investigations in the field of predicting of the automotive friction material characteristics. That is why, in this paper an attempt has been made to use artificial neural networks as a tool for modelling and prediction of the automotive friction materials characteristics taking into consideration all relevant influences. It is important that in this study predictions have been done by integrating influences of the complete friction material formulations, the most important manufacturing parameters, and testing conditions simultaneously. Predictions are related to the cold performance of the friction material before and after fading and recovery tests. 2. Experiment As it was explained, in the field of tribology very complex and highly non-linear phenomena are involved. This is the reason why analytical models are difficult, even impossible to obtain. That is why artificial neural networks, characterized as “computational models” [25], have been introduce to learn and generalize experimental data based on parallel processing. The process of artificial neural model developing, is not trivial and involves many critical issues: (i) select data generator, (ii) data generation (define the range and distribution of the training data in the model input parameter space where training data needs
ˇ Duboka / Wear 261 (2006) 269–282 D. Aleksendric, C.
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Fig. 1. Artificial neural network for friction material’s behaviour prediction.
to be generated), (iii) perform data generation, (iv) data preprocessing, (v) neural network structure selection, (vi) training algorithm selection, (vii) neural network training, (viii) validation accuracy evaluation, and (ix) testing of the artificial neural networks [26]. At the beginning, the neural model of friction material behaviour does not know any information about friction material performance under different operating regimes. In order to be learned about the friction material behaviour versus different formulation, manufacturing, and testing conditions (Fig. 1), the artificial neural network has to be trained with corresponding data. Therefore, the preliminary step in a neural model development is the identification of model inputs and outputs. Input/output identification depends on model objectives and choice of the data generator. According to objectives of this paper, the input parameters are defined by the friction material formulations, used manufacturing conditions, and testing conditions (see Fig. 1) against changes of the friction coefficient or the brake factor C (one-dimensional output parameter). The type of data generator depends on application and the availability. In this case, as a data generator, single-end full-scale inertia dynamometer has been used (see Fig. 2), developed at laboratory for friction mechanism and braking systems-FRIMEKS
Fig. 2. Single-end full-scale inertia dynamometer.
(Automotive Department) Faculty of Mechanical Engineering, University of Belgrade. Since, the brake performance resulting from the complex interrelated triboprocesses occurring during braking in the contact of the friction pair affected by the physicochemical properties of the friction materials ingredients. In order to establish relationships between input and output parameters space, the role of data generator is important from the point of view repeatability of the testing conditions. That is why, it has been decided to perform testing of the friction materials under strictly controlled conditions related to changes of pressure line application, initial speed, initial temperature, and inertia of revolving masses. These testing conditions are chosen in order to simulate the real operating regimes by full-scale inertia dynamometer shown on Fig. 2. The DC motor (1) drives, via coupling (2), a set of six flywheels (3) providing in such way different inertia from 10 to 200 kg m2 independently mounted on the driving shaft (4). The flange (5) firmly jointed to the shaft (4), bears rotating part of the tested brake (disc) while immobile flange (6), being firmly connected to the foundation (7) is used for mounting stationary parts of the tested brake (calliper) [27]. The full-scale inertia dynamometer is equipped by PC-based automatic control and data acquisition system of pressure, speed, temperature, and braking torque at a sampling rate of 50 Hz. The brake factor C is calculated from the average values of friction coefficient in the range of speed changes between 0.8 and 0.1 V. Therefore, by inertia dynamometer as a data generator shown on Fig. 2, the friction material mounted on the specified brake can be tested according to adopted testing methodology. Obviously, testing methodology needs to be chosen according to range and distribution of data that are going to be collected. It is evident from Table 1 that, in this case, there are four different tests (cold performance 1, fading, recovery, and cold performance 2) for friction performance evaluating. In this paper, our attention will be paid on cold performance tests before and after thermal load. Testing methodology, according to Table 1, has been divided into five tests: (i) burnishing
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272 Table 1 Testing methodology Test no.
Test
Pressure line application (bar)
1 2 3
Burnishing Cold performance 1 Fading
4
Recovery
40 20, 40, 60, 80, 100 Correspond to the 3 m/s2 deceleration on first braking 20, 40, 60, 80, 100
5
Cold performance 2
20, 40, 60, 80, 100
Initial speed (km/h)
Temperature (◦ C)
Braking number
90 20, 40, 60, 80, 100 90
