Torus Form Inspection Using Coordinate Sampling

Aguirre-Cruz, Juan A.; Raman, S. Ganesh Sundara

doi:10.1115/1.1842134

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The surface and centre of round workpieces should be considered when verifying the tolerance zones. The Hammersley method has proven to be reliable in its ability to verify a surface, [6,12,17,18]. However, it does not place enough emphasis on the centre of the workpiece.…”

Section: Hamspi Methodsmentioning

confidence: 99%

“…Using the aligned systematic method, the surfaces were divided into equidistant areas resembling slices of a pie, such as in Prakasvudhisarn [17]. The regions contained in these areas were sampled by the random sampling method, such as in Aguirre-Cruz [18]. This was supposed to allow for more coverage of the surface than the Aligned Systematic.…”

Section: Overview Of Present Workmentioning

confidence: 99%

“…Roundness measurements with a CMM were evaluated by Chan [16] to see how many points are required to truly determine circularity. Conical surfaces were studied by Prakasvudhisarn [17] and toroidal surfaces by Aguirre-Cruz [18]. Both of these studies utilized the least squares and linear optimization fitting algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Alternate methods for sampling in coordinate metrology

Collins

Fay

Aguirre-Cruz

et al. 2007

Proceedings of the Institution of Mechanical Engineers, Part B:

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A variety of inspection equipment exists in industry to capture the points on a surface that will envisage manufacturing form errors. Manufacturing inspection faces a problem of finding optimal methods to capture an evenly spread distribution of points on the surface. Sampling does not yield complete information about a surface. Each material removal process leaves a unique pattern on the surface of the workpiece, which has to be taken into consideration while developing the sampling strategy. For instance, round patterns left by face milling at low feeds on flat surfaces, and spiral patterns left during surface milling and turning operations on surfaces of revolution. Two new methods have been developed to improve sampling using the coordinate measuring machine (CMM) for inspection of flat and revolved surfaces. These are the Spiral, and Hamspi sampling methods.The Spiral method focuses on the centre of the area and uses the Archimedean spiral. Hamspi is a method that combines both the Spiral and randomized Hammersley to measure points in the middle as well as the outer zone of the workpiece. Mathematical comparisons of these methods have been made to establish feasibility. An experiment was performed to determine the accuracy of these models using two dependent variables: inspection time and minimum zone. The minimum zone was (statistically) significantly affected by only two factors: sample size and workpiece shape. The sampling time was however affected by sample size, workpiece shape, and the interaction between them. This study observed that the beginning and ending cutting zones of spherical surfaces were the most significant regions to verify. It was found that the Spiral and Hamspi methods had similar point distributions as the Hammersley method while placing more emphasis on the origin of the workpiece.

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Section: Hamspi Methodsmentioning

confidence: 99%

Section: Overview Of Present Workmentioning

confidence: 99%

See 1 more Smart Citation

Alternate methods for sampling in coordinate metrology

Collins

Fay

Aguirre-Cruz

et al. 2007

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Their interesting results were efficiently presented as sort of a guide to help the operator to reach those two objective setting the parameters in appropriate way. Starting to explore the world of unusual surfaces, Aguirre et al [29] worked on torus developing a 6 factors factorial plan. They demonstrated that fitting algorithm, and approach of evaluating the characteristics of torus, were the only main effects that were significant, as well as the interactions between the sample size and the fitting algorithm; type of torus (male, female) and approach; sample size, fitting algorithm and approach.…”

Section: Variability and Uncertainty In Cmmmentioning

confidence: 99%

CMM Measurement Variability Analysis: A Comparison Between Two Metrological Laboratories Measuring Three Industrial Workpieces

Aggogeri

Barini

Levi

2008

Volume 1: Advanced Energy Systems; Advanced and Digital Manufacturing; Advanced Materials; Aerospace

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Quality may be defined as a set of requirements a system should satisfy in order to meet customer's needs. Control of these requirements assures satisfaction of relevant standards, and consequently the performance levels of a manufacturing/transactional stream. In this context it is fundamental to define control procedures and reliable measurement systems adequate for adopting improvement action as soon as anomalies and dysfunctions are detected. This paper deals with a study of measurement variability occurring during practical exploitation of CMMs (Coordinate Measuring Machines).These measurement systems are designed to probe selected points of workpiece surface, and compare the relevant coordinates or derived quantities with specified values; capability and versatility of CMMs justify their widespread use in industry. Evaluation of CMM measurement variability is however often awkward owing to a number of factors, such as e.g. measurement task, environment, operator and measurement procedures.A round robin exercise involving two industrial laboratories was planned in order to address these issues. Three typical machine tool parts were circulated among participants, who were asked to measure linear dimensions as well as tolerances at specified locations, according to an agreed upon schedule.Results of measurements, performed by experienced CMM industrial users, were analyzed in order to bring out discrepancies, and suggest remedial actions in the light of information gathered. Several factors involving metrological as well as other aspects were observed to cause major discrepancies, yielding in turn information on where to look for potential sources of trouble. Conclusions were drawn in terms of operating procedure, leading to improved information on origin and components of variability.

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