Scanning Noise Evaluation Based on 3D Mesh Model

Phan, Nguyen Duy Minh

doi:10.1007/978-3-030-37497-6_73

Cited by 1 publication

(1 citation statement)

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“…The designed criteria include measurement time, surface coverage, density of point-based sampling and point dispersion (see in figure 8). Performance indicators based on local mapping on the 3D mesh model surface have also been proposed by Phan [197] to evaluate scanning noise. The noise is calculated for each triangular facet in the mesh, and it is qualified based on comparison with a threshold value obtained from the sensor qualification protocol.…”

Section: Indicators Of Point Cloud Qualitymentioning

confidence: 99%

Smart optical coordinate and surface metrology

Catalucci

Thompson

Eastwood

et al. 2022

Meas. Sci. Technol.

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Manufacturing has recently experienced increased adoption of optimised and fast solutions for checking product quality during fabrication, allowing for manufacturing times and costs to be significantly reduced. Due to the integration of machine learning algorithms, advanced sensors and faster processing systems, smart instruments can autonomously plan measurement pipelines, perform decisional tasks and trigger correctional actions as required. In this paper, we summarise the state of the art in smart optical metrology, covering the latest advances in integrated intelligent solutions in optical coordinate and surface metrology, respectively for the measurement of part geometry and surface texture. Within this field, we include the use of a priori knowledge and implementation of machine learning algorithms for measurement planning optimisation. We also cover the development of multi-sensor and multi-view instrument configurations to speed up the measurement process, as well as the design of novel feedback tools for measurement quality evaluation.

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Section: Indicators Of Point Cloud Qualitymentioning

confidence: 99%