Experimental Analysis of Search-Based Selection of Sample Points for Straightness and Flatness Estimation
Author:
Affan Badar M.1, Raman Shivakumar2, Simin Pulat P.2, Shehab Randa L.2
Affiliation:
1. Indiana State University, Dept. of IMT, College of Technology, Terre Haute, IN 47809 2. University of Oklahoma, School of Industrial Engineering, Norman, OK 73019
Abstract
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.
Publisher
ASME International
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering
Reference25 articles.
1. ASME Y14.5M, 1994, Dimensioning and Tolerancing, The American Society of Mechanical Engineers, New York, NY. 2. Dowling, M. M., Griffin, P. M., Tsui, K.-L., and Zhou, C., 1997, “Statistical Issues in Geometric Feature Inspection Using Coordinate Measuring Machines,” Technometrics, 39(1), pp. 3–17. 3. Yau, H.-T., and Menq, C.-H., 1996, “A Unified Least-Squares Approach to the Evaluation of Geometric Errors Using Discrete Measurement Data,” Int. J. Mach. Tools Manuf., 36(11), pp. 1269–1290. 4. Menq, C.-H., Yau, H.-T., Lai, G.-Y., and Miller, R. A., 1990, “Statistical Evaluation of Form Tolerances Using Discrete Measurement Data,” ASME Advances in Integrated Product Design and Manufacturing, P. H. Cohen and S. B. Joshi, eds., PED–Vol. 47, pp. 135–149. 5. Zhang, Y. F., Nee, A. Y. C., Fuh, J. Y. H., Neo, K. S., and Loy, H. K., 1996, “A Neural Network Approach to Determining Optimal Inspection Sampling Size for CMM,” Computer-Int. Manufa. Syst., 9(3), pp. 161–169.
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|