The manufacturing and development of automated production lines for wooden doors have emerged within paint-free wooden door manufacturers.

In our country, the development of automated production lines for wooden doors has been driven by manufacturers of paint-free wooden doors. As wood door companies have embraced automated manufacturing—particularly as the competitive edge of automated production on these lines has become increasingly evident—the need for automation among numerous paint‑finished door manufacturers has come to the fore.

Compared with paint-free wooden doors, painted doors involve a more complex manufacturing process, require more advanced craftsmanship, and place higher demands on the level of system automation as well as on logistics, information flow, and quality‑control management.

Based on a comprehensive, custom‑designed precise localization algorithm for binocular vision in wooden doors, this work explains the principles of binocular stereo imaging and, drawing on these principles along with the Zhang Zhengyou calibration method, conducts experiments to calibrate the two cameras. The resulting calibration parameters for the ZED lens are obtained, enabling the projection of pixel coordinates in the wooden door’s image plane onto the world coordinate system and thereby determining the spatial layout of the customized door within that global frame. To address the poor matching performance in edge regions and areas with deep occlusions, an improved semi‑global matching algorithm is augmented with an image segmentation technique. Functional tests conducted using the Middlebury benchmark dataset demonstrate that the proposed algorithm outperforms the baseline approach and delivers robust results in generating disparity maps for stacked wooden doors, yielding accurate depth information that meets the real‑time matching requirements of automated door‑manufacturing lines.

The feasibility of the overall certification scheme and the performance of the custom wooden‑door identification and precise positioning algorithm on a wooden‑door manufacturing assembly line were evaluated through experimental analysis of an automated recognition and mobile‑phone‑based positioning system. First, based on the results of binocular vision calibration, calibration tests were conducted for both the visual recognition system and the stacking robot’s end effector, enabling the projection of the customized wooden‑door’s core coordinates from the world coordinate system to the intelligent robot’s base plane. Subsequently, the user interface for the automated recognition and mobile‑phone positioning system was designed and developed, with communication protocols established between the host computer software and the robot controller. Finally, experiments were carried out to investigate the identification and precise positioning algorithms during the wooden‑door stacking process, accompanied by an analysis of their accuracy levels. Comprehensive stacking trials were performed for four distinct types of wooden doors, and the results demonstrated that the custom‑designed automated recognition and precise positioning algorithms meet the required hit rate and practicality standards in the stacking operation.

To further enhance the level of automation in wooden door manufacturing, early single‑machine automation has paved the way for fully automated production lines. This has addressed longstanding challenges such as low efficiency, inconsistent quality, and high labor intensity. By integrating fully automated machining with automated material handling and auxiliary processes like loading and unloading, the assembly‑line approach has, to a significant extent, reduced reliance on manual labor.

The precision soft‑wood door production line is equipped with a Mitsubishi CNC system from Japan as its powerful control center, using machinery to replace manual labor in the machining and manufacturing of all door components. High‑precision CNC equipment handles cutting, hand carving, edge banding, and other processes, thereby reducing labor costs and human error while boosting operational efficiency.