Advanced unconditional signal processing model for cross-section contour reconstruction using multi-channel measurements

In mechanical engineering the standard method to assess the geometric tolerances of rotational parts is by analysing parts' rotational motion in relation to the measuring system. Traditional tolerance compliance measurement models are conditional signal processing models that require pre-defined contour parameters of measured sections. However, due to part diversity and complexity, pre-determining and pre-setting the section parameters before each measurement process is very time-consuming and, in some cases, unachievable. To address this challenge, this paper proposes an advanced unconditional signal processing model, which uses multi-channel measurements for cross-section contour reconstruction that operates without predefined section parameters. This model can handle multi-point measurement signals and accurately estimate the contour shape and engineering center coordinates of measured sections through circumferential Fourier expansion and an approximated signal-contour transform matrix. An efficient iterative algorithm then reconstructs the contour and precisely locates the engineering center. The computational accuracy and robustness of the proposed model have been confirmed through rigorous theoretical analysis and comprehensive experimental validation. Due to its inherent unconditional advantage, the proposed signal processing model can achieve intelligent monitoring of rotational parts throughout their entire life, which not only adapts and remains stable across a wide variety of cross-section types but also significantly improves measurement efficiency, ensuring precise and accurate results.