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- Title
Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving.
- Authors
Cortivo, Davide; Campagnolo, Alberto; Meneghetti, Giovanni
- Abstract
The wheel loads of a race car have been estimated in view of structural durability assessments. First, the front left double‐wishbone suspension of a rear‐wheel‐drive race vehicle has been instrumented; then, wheel loads have been estimated by means of four approaches: (i) a geometric matrix (GM) method, (ii) a feedforward neural network (FNN) approach applied to the fully instrumented suspension (FIS), (iii) a FNN approach involving a reduced number of sensors (the partially instrumented suspension (PIS)) and an inertial measurement unit (IMU), and (iv) a linear modeling approach (LM). After having trained the FNNs by using suspension signals acquired in a racetrack as inputs and related tire forces measured with the GM method as targets, the FNN‐based methods have been validated on three different racetracks by comparing the estimated loads with those estimated by means of the GM method. According to the results achieved, the FNN approaches are effective for the estimation of the wheel forces. Highlights: A double‐wishbone suspension was instrumented to measure the wheel fatigue loads.A geometric matrix method was formulated to measure the loads simply from equilibrium.Two NNs were formulated, with fully and partially instrumented suspension, respectively.Both NNs were trained and successfully estimated the wheel loads in track driving.
- Subjects
RACING automobiles; MATRIX analytic methods; FEEDFORWARD neural networks; MACHINE learning; WHEELS; AUTOMOBILE racetracks
- Publication
Fatigue & Fracture of Engineering Materials & Structures, 2022, Vol 45, Issue 11, p3447
- ISSN
8756-758X
- Publication type
Article
- DOI
10.1111/ffe.13821