How does the high-end gantry-type FVM-CNC imager's automatic focus and depth-of-field extension algorithm achieve clear imaging across complex 3D profiles?
Publish Time: 2025-09-17
In precision manufacturing, many critical components possess complex 3D geometries—featuring micro-steps, deep holes, curved surfaces, and stepped cavities, all on the same workpiece. Traditional 2D optical measurement systems often struggle to achieve clear imaging of such non-planar surfaces due to limited depth of field, resulting in blurred areas and compromising the accuracy of dimensional measurements and defect detection. The high-end gantry-type FVM-CNC imager overcomes this limitation by integrating an automatic focus mechanism and a depth-of-field extension algorithm, achieving clear imaging across the entire 3D profile, thus providing reliable data for high-precision non-contact measurement.
Automatic focusing is the first step to achieving clear imaging. Unlike manual focusing, which relies on operator experience, the FVM-CNC imager's automatic focus system utilizes the high-precision Z-axis control of the CNC motion platform and an image sharpness evaluation function. The system performs a vertical scan within a preset height range, capturing images layer by layer. An algorithm then calculates the sharpness of each frame—using metrics such as gradient variance, Laplacian energy, or frequency domain entropy. When the sharpness reaches its peak, the system determines that as the optimal focal plane and locks the Z-axis position. This process is fast, objective, and repeatable, eliminating human bias and ensuring optimal focus every time.
However, a single focal plane is insufficient for surfaces with significant height variations. Even with telecentric lenses or high-depth-of-field optics, the effective clear imaging range remains limited. Therefore, the depth-of-field extension algorithm was developed. Its core concept is to perform a dense step scan along the Z-axis, capturing a sequence of images with continuous focus from bottom to top, where each frame is only partially in focus. The algorithm then analyzes the entire image sequence at the pixel level, identifying the sharpest pixel in each location and stitching them together to create a single, fully clear image. This process is akin to weaving multiple "local focus" points into a single "global clear" image. Achieving high-quality depth-of-field fusion hinges on the robustness of the sharpness criterion and the smoothness of edge transitions. In areas with rich texture, algorithms easily determine sharpness; however, traditional methods may misjudge sharpness in low-contrast, smooth, or repetitive pattern areas. High-end FVM systems employ multi-dimensional fusion strategies, combining various features such as edge strength, local contrast, and frequency domain energy to improve accuracy. Simultaneously, the algorithm must precisely handle overlapping areas between adjacent focal planes, avoiding stitching artifacts or ghosting, ensuring a natural, seamless, and distortion-free composite image.
Furthermore, the system's mechanical precision and motion stability provide reliable support for the algorithm. The gantry-type structure guarantees Z-axis linearity and repeatability, ensuring consistent image acquisition across all layers. A high-rigidity frame suppresses micro-vibrations during scanning, preventing image blur. A CNC control system precisely coordinates the lens, stage, and camera movements, ensuring smooth and synchronized scanning.
The lighting system is also crucial. Uneven illumination can cause excessive reflections or deep shadows, interfering with sharpness assessment. FVM-CNC imagers typically use programmable ring lights or multi-angle LED arrays to maintain consistent illumination or dynamically adjust the lighting direction based on surface angle, minimizing the impact of highlights and shadows on the algorithm.
Ultimately, the combination of autofocus and depth-of-field extension frees FVM-CNC imagers from the limitations of traditional optics. It can "see through" complex geometries, integrating layered, blurry visual information into a single, sharp measurement image, providing high-quality data for subsequent edge detection, dimensional measurement, and surface defect identification. When a single device can accurately capture the complete details of all tooth surfaces, roots, and ends of a miniature gear without contacting the workpiece, it exemplifies the deep integration of optics, mechanics, and intelligent algorithms—redefining the boundaries of "clarity" in the microscopic world.
