| dc.description.abstract | Unlike semiconductor integrated circuits (ICs), newer microsystems combine sensors, actuators, mechanical structures, electronics, and optics on a single substrate. In such a diversified system, heterogeneous manipulation of the components becomes unavoidable. In an effort to find a solution to reduced yields and speeds in manufacturing at the micro-scale, research initiated in 1990s has sought to understand top-down aspects of micromanipulation, sensor-based precision control of robots, self alignment effects using compliant micro structure designs, and so on. From these researches, automated microassembly emerges as an enabling technology for micro manufacturing that offers well-known pathways to building heterogeneous microsystems with a higher degree of robustness and more complex designs than monolithic fabrication. The success of assembly in micro domain, however, is directly related to the level of precision design and automation. Control and planning are two defining factors for the microassembly yield and its cycle time. Assembly at the microscale harbors many difficult challenges due to scaling of physics, stringent tolerance budget, high precision requirements, limited work volumes, and so on. These difficulties warrant new control and planning algorithms, different than their macro-scale counterparts. In this research, a hybrid controller for automated MEMS assembly has been formalized using precision metrics such as resolution, repeatability and accuracy (RRA). A "high yield assembly condition (HYAC)" has been proposed as a quantitative metric to assess success or failure of microassembly. Using this quantitative tool, the precision-adjusted hybrid controller switches between open, closed, and calibrated operation in the microassembly cell. Additionally this research modifies traditional robot motion planning algorithms by introducing discontinuous sensor field measurements and proposes a planning algorithm referred to as "precise path search". Unlike conventional "star" path-planners in macro scale, this algorithm prioritizes the attained precision over distance, and hence will select more precise assembly plans than the faster ones. The instantaneous as well as cumulative intricacy in a multipart microassembly scenario is identified by flagging the subtasks with binary "complexity indices (CI)" and updating them throughout the assembly process. The proposed hybrid controller dynamically adapts to the assembly process based on these complexity indices for the subtasks.The proposed framework has been demonstrated for multiple microassembly scenarios including assembly of a MEMS optical spectrum analyzer called "Microspectrometer" and a micro robot called "ARRIpede". Both systems pose several challenges in manufacturing such as workspace identification, motion planning for robot end-effectors, optical and mechanical alignment of components, heterogeneous micro part integration and so on. Simulation and experimental results for the assemblies of these microsystems are presented to indicate that the proposed hybrid controller lead to high yields at faster cycle times than conventional precision control methods. The concepts described in this dissertation have been applied and embodied into robotic assembly cells, assembly simulators and concurrent microengineering tools, such as a reconfigurable microassembly system called mu3, programming of an extensive automation software application called "Neptune 3.0", programming of a virtual reality simulation software application called "Microsim 1.0", design, construction and packaging of miniature electronic backpack modules for untethered microsystems. | en_US |
