Cross-scale dimensional measurement of high-precision modules based on machine vision

Abstract High-precision modules are of paramount importance in various fields such as machinery, automotive, aerospace, and IT electronics. Specifically, magnetic plates, as a type of high-precision linear module, are frequently employed in the manufacture of linear motors. Given the stringent requirements for dimensional measurement accuracy of high-precision components, manual measurement methods are inadequate for quality inspection of finished parts. Consequently, machine vision technology is widely used for measuring the dimensions of high-precision parts. To achieve the desired measurement accuracy, often at micro-nano levels, and to ensure the measurability of the magnetic plate’s overall dimensions, which range from millimeters to centimeters, a cross-scale measurement approach is essential. This approach must take into account the effects of depth of field (DOF) and the workpiece depth. The selection of an appropriate edge contour fitting method is critical in determining measurement accuracy. To address these challenges, our team constructed a machine vision system based on a mathematical model of DOF to resolve measurement challenges caused by the magnetic plate’s DOF issues. Furthermore, to improve edge fitting accuracy, we utilized the least squares method in combination with the Tukey weight function. This statistical approach allows for robust fitting by minimizing the influence of outliers and enhancing the precision of edge detection. Finally, the overall size of the magnetic plate is obtained through cross-scale calculation. Our findings reveal that the single-hole error and margin error within the magnetic plate are confined within a range of ±10 μm, the overall size error is within the range of ±90 μm. This level of precision is crucial for ensuring the quality and performance of linear motors. Implementing this method significantly enhances product detection efficiency on actual production lines, providing substantial improvements in quality assurance processes.