Machine vision system for key-cap flatness measurement of computer keyboards

Abstract

Key-cap flatness detection after assembly is one of the basic quality control (QC) indexes in computer keyboard manufacturing. A modified machine vision system based on linear structured light imaging for measuring the key-cap flatness is proposed for keyboard QC automation. After a brief introduction of the system design and principle, the pipeline of light stripe image processing, especially the removal of printed letter interference, is studied. First, the staggered reprojection of dense multiline fringes is presented using the pattern editability of the digital light processing projector to replace the conventional three-dimensional (3-D) sensor mechanical scanning and avoid the movement and cumulative error. Second, an adaptive direction operator based on a Hough transform voting is proposed. This operator is used for directional morphological filtering to remove letter noise and solve the issue of printed letter interference on the key-cap surface, thus improving the accuracy and stability of stripe centerline extraction. Finally, the nonlinear least square method is used to fit the 3-D surface of the key-cap and evaluate its flatness efficiently based on the discrete globally distributed 3-D point cloud data. The experimental result demonstrates that the proposed machine vision system can quickly detect keyboard key-cap flatness and shows superior performance to that of the previous work.