Validation of machine learning approaches for estimating wheel fatigue loads at the front suspension of a race car during track driving

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.

Fatigue crack growth simulation by extended finite element method: A review of case studies

“…Since all of them are based on experimental or analytical data, they are not presented here, but some of them are referenced here for the benefit of readers. [136][137][138][139][140][141][142] In addition, many other recent papers presented also this important topic, some of them described briefly in the following text.…”

“…Excellent source for such an overview is provided by the special issue published couple of months ago in Fatigue & Fracture of Engineering Materials & Structures under the title “Data science and machine learning for fatigue and fracture assessment.” 135 As presented and explained in Editorial, 11 papers described the use of data sciences and ML in structural durability investigations with a particular emphasis on material cyclic behavior and fractures. Since all of them are based on experimental or analytical data, they are not presented here, but some of them are referenced here for the benefit of readers 136–142 …”

Fatigue & Fracture of Engineering Materials & Structures Virtual Special Issue: Data science and machine learning for fatigue and fracture assessment

“…Cortivo et al 8 estimated the wheel loads of a race car using four approaches: A geometric matrix (GM) method, a feedforward neural network (FNN) approach applied to the fully instrumented suspension (FIS), an FNN approach involving a reduced number of sensors and an inertial measurement unit (IMU), and a linear modeling approach (LM). The FNN‐based methods have been trained via suspension signals and validated by comparing the estimated loads with those estimated by the GM method.…”