X-DoF Improves Your Blocked Forces: Paper Published

In inverse Blocked Force characterization, selecting the correct set of Degrees of Freedom (DoF) is crucial. The main issues are:

1. Overfitting: Including too many DoF can lead to overfitting, where the model fits the noise in the data rather than the actual signal. This results in inaccurate force estimations and poor prediction quality.
2. Underfitting: Selecting too few DoF can lead to underfitting, where the model fails to capture the necessary dynamics, leading to incomplete or incorrect force characterizations.
3. Manual Selection Challenges: Manually selecting the appropriate DoF is a tedious and error-prone process, especially as the number of possible subsets increases exponentially.

The X-DoF procedure offers significant advantages over traditional methods:

1. Automatic DoF Selection: X-DoF automatically identifies the relevant subset of Blocked Force DoF, eliminating the need for manual selection and reducing the risk of human error.
2. Reduced Overfitting: By selecting only the necessary DoF, X-DoF minimizes overfitting, leading to more accurate force estimations and better prediction quality.
3. Improved Robustness to Noise: The method enhances the robustness of the solution to measurement noise, ensuring that the force estimations are not influenced by errors in the data.
4. Enhanced Predictive Quality: Experimental results show that X-DoF provides better predictive quality compared to traditional methods, making it a valuable tool for Blocked Force characterization and component TPA.

Overall, X-DoF addresses the key challenges in inverse Blocked Force characterization by providing a systematic and automated approach to DoF subset selection, leading to more accurate and robust Blocked Forces that generalize well to unseen data.