Vision-based measurement of microassembly forces

• Published in: Journal of micromechanics and microengineering 2006-08, Vol.16 (8), p.1639-1652
• Main Authors: Anis, Y H, Mills, J K, Cleghorn, W L
• Format: Article
• Language: English
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Measurement and testing methods; Mechanical engineering. Machine design; Mechanical instruments, equipment and techniques; Micromechanical devices and systems; Physics; Precision engineering, watch making; Robotics; Solid mechanics; Structural and continuum mechanics
• ISSN: 0960-1317; 1361-6439
• DOI: 10.1088/0960-1317/16/8/028

This work describes a vision-based force sensing method for measuring microforces acting upon the jaws of passive, compliant microgrippers, used to construct 3D microstructures. The importance of jaw force measurement during microassembly is to confirm that the microgripper-micropart makes a successful grasp and to protect the microparts and microgripper from excessive forces which may lead to damage during the assembly process. Finite-element analysis of the microgripper is performed to determine the relation between the displacement and the resultant forces of its jaw. The resulting nearly linear force-displacement relationship is fitted to a first-degree equation. A mathematical model of the microgripper system validated this force-displacement relation. The proposed vision-based gripper force measurement techniques determine the deflections of the microgripper jaws during the microassembly process. The deflections in the gripper jaws are measured during the microassembly processes through computation of the relative displacements of the right and left microgripper jaws with respect to the microgripper base. Two approaches are proposed. The first approach uses pattern identification to measure these relative displacements. Two-dimensional pattern identification is performed using normalized cross-correlation to estimate the degree to which the image and pattern are correlated. The second approach uses object recognition and image processing methods, such as zero-crossing Laplacian of Gaussian edge detection and region filling. Experiments performed confirm the success of both approaches in measuring the microgripper jaw deflections and therefore the assembly forces.