Residual flatness and scale calibration for a point autofocus surface topography measuring instrument

Maculotti, Giacomo; Feng, Xiaobing; Su, Rong; Galetto, Maurizio; Leach, Richard

doi:10.1088/1361-6501/ab188f

Cited by 31 publications

(21 citation statements)

References 24 publications

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“…A wide set of different technologies have been developed to enable surface topography measurements. 15,16 Among the most widely used technologies for measuring surfaces, we mention contact probes (e.g., CMM and contact stylus instruments 15 ), optical probes (point autofocus instruments 42,43 ), and surface topography optical instruments (like focus variation microscopes or coherence scanning interferometers 15 ). They measure a cloud of points, resulting in a set of surface heights as a function z(x, y) of plane coordinates (x, y).…”

Section: Standard Characterization and Protocolmentioning

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

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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: Standard Characterization and Protocolmentioning

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

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“…No filtering or standard operator was applied, to avoid the removal of structure and enhance disturbances management method differences. 89 Average and confidence interval, at 95% confidence level, of the parameters were computed, to test…”

Section: Materials and Methods-case Studiesmentioning

confidence: 99%

“…This involved a prior levelling, the management of measurement disturbances and the evaluation of the surface topography parameter presented in Equation (). No filtering or standard operator was applied, to avoid the removal of structure and enhance disturbances management method differences 89 . Average and confidence interval, at 95% confidence level, of the parameters were computed, to test both the robustness of the disturbance management method to replicated measurements and to assess the presence of significant differences amongst the methods.…”

Section: Materials and Methods—case Studiesmentioning

confidence: 99%

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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“…Precise surface topography measurement and data analysis are reasonably crucial to evaluate the mechanical behavior of “engineering” surfaces [ 1 ] and are often an integral part of process control [ 2 ]. Surface topography is of great importance, as an example, when the oil consumption mainly caused by the engine’s piston–piston rings cylinder liner system is taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

Podulka

2021

Materials

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The influence of errors in the processes of detection and then reduction of surface topography measurement noise is of great importance; many research papers are concerned with the definition of this type of measurement error. This paper presents the influence of high-frequency measurement noise, defined for various types of surface textures, e.g., two-process plateau-honed, turned, ground, or isotropic. Procedures for the processing of raw measured data as a detection of the high-frequency errors from the results of surface topography measurements were proposed and verified (compared) according to the commonly used (available in the commercial software of the measuring equipment) algorithms. It was assumed that commonly used noise-separation algorithms did not always provide consistent results for two process textures with the valley-extraction analysis; as a result, some free-of-dimple (part of the analyzed detail where dimples do not exist) areas were not carefully considered. Moreover, the influence of measured data processing errors on surface topography parameter calculation was not comprehensively studied with high-frequency measurement noise assessments. It was assumed that the application of the Wavelet Noise Extraction Procedure (WNEP) might be exceedingly valuable when the reduction of a disparate range of measured frequencies (measurement noise) was carefully considered.

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Residual flatness and scale calibration for a point autofocus surface topography measuring instrument

Cited by 31 publications

References 24 publications

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

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

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

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

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