Estimation of the braking torque for MotoGP class motorcycles with carbon braking systems through machine learning algorithms

Bonini, Federico; Manduchi, Gionata; Mancinelli, Nicolò; Martini, Alberto

doi:10.1109/metroautomotive50197.2021.9502878

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…As for the ANN, in [4] the most promising results were provided by a four-layered network with two hidden layers. The first layer consisted of the input signals, while both hidden layers had ten neurons (each one with the hyperbolic tangent as activation function).…”

Section: Fig 2: Mccp Subtrees Creation (L) and Qualitative Trend Of T...mentioning

confidence: 99%

“…To the Authors' best knowledge, no studies can be found concerning carbon brakes in real operating condition. A preliminary investigation [4] permitted to identify Artificial Neural Networks (ANN) and Decision Trees as the most promising algorithms. In particular, they could provide similar accuracy, with decision trees appearing faster, but also more prone to overfitting.…”

Section: Introductionmentioning

confidence: 99%

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Braking torque estimation through machine learning algorithms

Bonini

2023

Theoretical and Applied Mechanics - AIMETA 2022

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Abstract. MotoGP class motorcycles rely on carbon braking system to cope with their incredible acceleration capability and high speed. Hence, assessing the torque generated by the front discs is a key to improve the vehicle performance. As direct measurement of the braking torque is not allowed during races, its value may be estimated through a physical model, using as inputs the brake fluid pressure (monitored on board), the braking system geometry and the friction coefficient (μ). However, the results obtained with this method are highly limited by the knowledge of the instantaneous friction coefficient between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model appears impractical to establish. This work aims to implement an innovative algorithm, based on machine learning, for determining μ from the signals regularly available in races, to enable accurate breaking torque computation. The proposed method consists of two main tools. An artificial neural network (ANN) is developed to approximate the unknown function that relates the input variables to μ, while a Kalman filter (KF) is implemented to estimate the real temperature distribution on the disc surface that constitutes one of the most important ANN inputs. The proposed algorithm has been successfully validated with real data collected from extensive tests in racetracks, with a special sensor setup.

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Section: Fig 2: Mccp Subtrees Creation (L) and Qualitative Trend Of T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Braking torque estimation through machine learning algorithms

Bonini

2023

Theoretical and Applied Mechanics - AIMETA 2022

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“…In recent years, automatic inspection based on deep learning has been developed for industrial inspection applications such as steel [ 4 ], fabrics [ 5 ], solar batteries [ 6 ], etc. Many scholars have applied neural networks to traffic cars [ 7 , 8 , 9 ]. Specific to tire production scenarios, there are more and more tire defect detection methods based on deep learning [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

Yang

Jiao

Wang

et al. 2022

Sensors

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With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.

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