Simulated variogram-based error inspection of manufactured parts

Vicario, Grazia; Pistone, Giovanni

doi:10.1007/s00362-018-1030-0

Cited by 3 publications

(10 citation statements)

References 14 publications

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“…The scatter plot of the table is called variogram cloud and any regression method could be used to produce an estimate of 𝛾. 35,41 The plot of the variogram cloud in a proper scale will provide us with a neat summary statistics of the data, see Figures 7 and 8. The Matheron's solution is the computation of mean value for each distance value, that is, it is a conditional expectation.…”

Section: Matheron's Variogrammentioning

confidence: 99%

“…We conclude this discussion by observing that the use of a quasi-distance appears in applications where the directional G-variogram is the index of interest. 41 For example, d((x 1 , y 2 ), (x 2 , y 2 )) = |x 1 − x 2 | allows to bring to light variations in one direction, here the first coordinate direction. This case is of high practical interest as when anisotropy occurs.…”

Section: Definitionmentioning

confidence: 99%

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Inference on errors in industrial parts: Kriging and variogram versus geometrical product specifications standard

Maculotti

Pistone

Vicario

2021

Appl Stoch Models Bus & Ind

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This article focuses on the inference on the errors in manufactured parts controlled by using measurements devices. The characterization of the part surface topographies is core in several applications. A broad set of properties (tribological, optical, biological, mechanical, etc.) depends on the micro-and macrogeometry of the parts. Moreover, parts usually show typical deterministic geometric deviation pattern, referred to as manufacturing signatures, due to the specific manufacturing processes and process setup parameters adopted for their production. In several situations, the measurements may also be affected by systematic errors due to the measurement process, that might be caused, for example, by a poor part alignment during the measurement process. Measurement techniques and characterization methods have been standardized in the International Standard ISO 25178, defining parameters characterizing the surface topography and supplying methods and formula adapt to deal with this issue computationally. In the present article, we consider a type of spatial dependence between measured values at different points that suggest the use of the variogram to identify patterns in the parts. We offer a comparison, based on a real set of measures, between the latter approach and the conventional as a test of the efficient performance of our findings.

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Section: Matheron's Variogrammentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Inference on errors in industrial parts: Kriging and variogram versus geometrical product specifications standard

Maculotti

Pistone

Vicario

2021

Appl Stoch Models Bus & Ind

Self Cite

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“…As far as the Gaussian process regression‐approach is concerned, in order to manage measurement disturbances, the training set was defined by a random subsampling of the surfaces. The ratio between sampled and measured points was set to 0.4% from prior experiences 79,82 …”

Section: Materials and Methods—case Studiesmentioning

confidence: 99%

“…The ratio between sampled and measured points was set to 0.4% from prior experiences. 79,82 Conventional disturbances management methods and surface topography parameter evaluations have been performed by means of state-of-the-art commercial software, MountainsMap v8.0. 39 The software allows computing standard topographical parameters defined in Section 2.2 and the application of disturbances management technique that are reported in literature, as discussed in Section 1.…”

Section: Materials and Methods-case Studiesmentioning

confidence: 99%

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Gaussian process regression‐based detection and correction of disturbances in surface topography measurements

Maculotti

Genta

Quagliotti

et al. 2021

Quality & Reliability Eng

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Modern smart and intelligent manufacturing is characterised by an increasing use of highly engineered surfaces and quasi‐free form geometries, for example, by additive manufacturing, and the requirement for fast and informative measurement tools for quality controls in production. These have lately pulled towards the adoption of optical surface topography measuring instruments to qualify technological surfaces, which are core to being assessed to characterise the product and optimise the manufacturing process. Surface topography measurements performed by optical instruments are known to suffer from disturbances, such as non‐measured points and spikes. These are due to complex interactions between the measurand and the instrument and may result in poor measurement quality and biased characterisation results. Currently, the correction of measurement disturbances is carried out by empirical approaches which mostly involve thresholding and interpolations, whose sensitivity is often devolved to the operator. In this work, a Gaussian process regression‐based approach is outlined to identify and correct measurement disturbances exploiting spatial correlation properties of the measurements to provide a formal, robust, and univocally defined approach to manage measurement disturbances. The proposed approach, differently from currently available alternatives, is capable of managing at once the different type of disturbances by means of a supervised machine learning technique. The formal and practical advantages of the proposed method are discussed exploiting case studies of industrial interest applied on quasi‐flat surfaces to stress the disturbances effect.

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