This paper presents a methodology for optimization of the scanning path of iterative adaptive grid generation algorithms in metrological scanning probe microscopes (SPM). This method equips the traditional non-equidistant (adaptive) grid point generation algorithm for the measurement of large smooth optical workpieces with a space-filling curve solution of the traveling salesman problem. We will show that the combined algorithm can reduce the total measurement time efficiently and enables faster surface scanning speed for large industrial workpieces while preserving adequate surface information for performance evaluation after surface reconstruction comparing to contemporary paths. The path generation algorithm also promises necessary information locality and better surface reconstruction completeness. The algorithm is not subject to instrument restrictions and can be implemented in any coordinate measurement systems with xy scanners. Experimental verifications are conducted on a typical parametric surface to show the efficiency of the proposed algorithm.
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