This paper presents a model-based method to analyze the accuracy of Hardware-in-the-Loop (HiL) test rigs for mechatronic vehicle axles.
After completing the design, we conducted a series of experiments to determine the performance and accuracy of the hardware.
Firstly, only a small subspace dimension is required to achieve the numerical accuracy of the hardware channel simulator.
It is shown that stage and batch lengths can be estimated in real time with an average error that is not larger than 20% of the inherent batch-to-batch variability, whereas quality estimations can be provided within the accuracy of the hardware instrumentation, but 240 times faster.
Therefore, incorporating TOF information is not expected to significantly improve the robustness and accuracy of xMLAA for hardware attenuation estimation.
While phantoms can be used to test the accuracy of 3D printing hardware, to date there are no measures of quality and benchmarks for the accuracy and reproducibility of STL models generated from DICOM images for 3D printing.
Then, we compare its performance in terms of accuracy and hardware resources needed for implementation.
To ensure the profiling accuracy, we put read/write operations of hardware performance counters in the VMM time slice instead of VCPU time slice, as we can see in Figure 6.
Similarly, for 3D printing, one strategy may include carefully developed phantoms that, once printed, can undergo standardized tests and measurements to establish the accuracy of the 3D printing hardware and software pipeline.
In this work we also analyze the performance in accuracy and hardware resources of the proposed implementation.
Due to the severe resource constraints of sensor hardware, the accuracy of the tracking system could be compromised by the processing power or energy consumption.
