A form of deviation-based method for coordinate measuring machine sampling optimization in an assessment of roundness

Rossi, Andrea

doi:10.1243/0954405011519411

Cited by 17 publications

(15 citation statements)

References 13 publications

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“…Conversely, in previous works, the authors developed a cross-validation method for small samples to assess the kind of manufacturing signature on the roundness profile in order to detect critical points such as peaks and valleys [16] [17]. They use a pre-sampling strategy to locate peaks and valleys where the sampling density is increased.…”

Section: State Of the Art On Sampling Data Points And Search Space Sizementioning

confidence: 99%

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

Meo¹,

Profumo²,

Rossi³

et al. 2013

Measurement Science Review

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Roundness is one of the most common features in machining. The minimum zone tolerance (MZT) approach provides the minimum roundness error, i.e. the minimum distance between the two concentric reference circles containing the acquired profile; more accurate form error estimation results in less false part rejections. MZT is still an open problem and is approached here by a Genetic Algorithm. Only few authors have addressed the definition of the search space center and size and its relationship with the dataset size, which greatly influence the inspection time for the profile measurement and the convergence speed of the roundness estimation algorithm for a given target accuracy. Experimental tests on certified roundness profiles, using the profile centroid as the search space center, have shown that the search space size is related to the number of dataset points and an optimum exists, which provides a computation time reduction up to an order of magnitude.

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Section: State Of the Art On Sampling Data Points And Search Space Sizementioning

confidence: 99%

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

Meo¹,

Profumo²,

Rossi³

et al. 2013

Measurement Science Review

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“…Due to the problem complexity increase with the dataset size, suitable optimization algorithms are required. In previous works, one of the authors developed a fast algorithms for small datasets to assess the kind of waviness deviation on the profile in order to detect critical points such as peaks and valleys [5] [6]. Among the fastest form error evaluation methods to implement, already available in the literature based on the MZT, are the steepest descent algorithm, which has been applied for roundness evaluation [7], and a two-dimensional simplex search method to evaluate several form features, including roundness [8].…”

Section: Introductionmentioning

confidence: 99%

Fast genetic algorithm for roundness evaluation by the minimum zone tolerance (MZT) method

et al. 2011

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The main goal of the minimum zone tolerance (MZT) method is to achieve the best estimation of the roundness error, but it is computationally intensive. This paper describes the application of a genetic algorithm (GA) to minimize the computation time in the evaluation of CMM roundness errors of a large cloud of sampled datapoints (0.2° equally spaced datapoints). Computational experiments have shown that by selecting the optimal GA parameters, namely a combination of the four genetic parameters related to population size, crossover, mutation, and stop conditions, the computation time can be reduced by up to one order of magnitude, allowing realtime operation. Optimization has been tested using seven CMM datasets, obtained from different machining features, and compared with the LSQ method. The performance of the optimized algorithm has been validated with GA from the literature using four benchmark datasets.

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“…A second approach proposed in the literature consists in adopting adaptive sampling strategies [2,3,13,21]. Adaptive sampling is a multi-step methodology, which starts with a low density, usually uniformly spaced, sampling of the feature of interest.…”

Section: Introductionmentioning

confidence: 99%

Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM

Colosimo¹,

Gutiérrez²,

Moroni³

et al. 2008

Economic Quality Control

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More and more often, procedures for profile/surface monitoring and inspection refer to geometric specifications. In fact, the number of form tolerance requirements (such as straightness, roundness, flatness, cylindricity, free-form profiles and surfaces) are increasingly appearing in technical drawings. Usually, Coordinate Measuring Machines (CMMs) are used to measure points on the specific feature under study in order to decide whether the machined item is conforming to requirements. In fact, measured points are used to compute the form error which has to be smaller than the tolerance value to avoid scrapping or reworking. The estimated form error is usually affected by uncertainty, which depends on both the sample size (number of points measured on the profile/surface) and the sampling strategy (location of the points on the profile/surface). The present research work focuses on the second issue and investigates the performance of several existing sampling strategies with the ones achieved by two newly presented approaches. These new approaches explore advantages arising by including a statistical analysis of a set of machined features in the sampling strategy design. Throughout the paper, a real case study concerning flatness tolerance is used as reference.

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A form of deviation-based method for coordinate measuring machine sampling optimization in an assessment of roundness

Cited by 17 publications

References 13 publications

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

Fast genetic algorithm for roundness evaluation by the minimum zone tolerance (MZT) method

Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM

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