Experimental Analysis of Search-Based Selection of Sample Points for Straightness and Flatness Estimation

In earlier work [Bader et al., ASME J. Manuf Sci. Eng. 125(2), pp. 263-271 (2003); Int. J. Mach. Tools Manuf. 45(1), pp. 63-75 (2005)] the authors have presented an adaptive sampling method utilizing manufacturing error patterns and optimization search techniques for straightness and flatness evaluation. The least squares method was used to compute a tolerance zone. In this paper, experimental analysis is performed to verify the sturdiness of the adaptive sampling procedure. Experiments are carried out to investigate the effects of different factors on the sample size and absolute percent error of the estimated tolerance from that of a large population sample. Twelve 7075-T6 aluminum plates are end-milled and 12 cast iron plates are face-milled. Two sets of four plates from each lot are selected randomly, one each for straightness and flatness estimation. Factor A used in both straightness and flatness analyses is manufacturing process (i.e., surface error profile). Factor B for straightness is step size whereas for flatness it is search strategy (i.e., number of bad moves and restart allowed). Factor C for flatness is search algorithm (i.e tabu and hybrid). Plates are nested within the levels of manufacturing process. The results have been analyzed and compared with other sampling methods. The analyses reveal that the current approach is more efficient and reliable.