Profile tracking for multi-axis ultrasonic inspection of model-unknown free-form surfaces based on energy concentration

Zhang, Yang; Chen, Kai; Guo, Peng; Li, Fan; Zhu, Jie; Zhu, Limin

doi:10.1016/j.measurement.2020.108867

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…The Kriging model has also been used to identify the minimum number of checkpoints to reduce measurement errors (Dumas et al, 2013) and estimate the uncertainty to improve measurement accuracy (Fei et al, 2019). In the inspection of an unknown freeform surface, a few initial points are sampled manually (Zhang et al, 2021) or by other means (Chen and Peng, 2017; Lu et al, 2019; Sun et al, 2017b). Then, the next optimal sample point is automatically determined in the following adaptive sampling process.…”

Section: Introductionmentioning

confidence: 99%

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Gao,

Zheng,

et al. 2023

Transactions of the Institute of Measurement and Control

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Owing to the lack of prior knowledge, the accurate reconstruction of surfaces with high uncertainty is dependent on the reasonable real-time selection of the next best point (NBP) during the sampling process. In this study, a new informative criterion called the MaxCWVar weighting shape effect is proposed for NBP selection. The responses to the geometric features of the candidate locations are predicted by a back propagation neural network (BPNN), which is then used in combination with the jackknife method to estimate the candidate uncertainty. The blade cross-section sampling case is considered to validate the flexibility and effectiveness of the proposed method. A comparison with other adaptive sampling strategies shows that BPNN-based response prediction is well-suited for allocating sample points. In contrast to other NBP selection criteria, the sample point distribution recommended by the MaxCWVar criterion is preferable as it improves the reconstruction accuracy and modeling efficiency. This study promotes the exploration of metrological methods for the fast and intelligent reconstruction of complex surfaces with high uncertainty.

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Section: Introductionmentioning

confidence: 99%

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Gao,

Zheng,

et al. 2023

Transactions of the Institute of Measurement and Control

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show abstract

“…Therefore, the best method is to use a probe for flaw detection to directly measure the surface. This method is divided into manual operation and an automated system marking sampling points on the workpiece [ 22 ] and probe movement strategy to allow the inspection system to measure the entire workpiece by itself [ 23 , 24 , 25 , 26 ]. An automated probe will consume less time and labor costs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Ultrasonic Full Matrix Capture Process for the Global Imaging of Complex Components with Curved Surfaces

Miao,

Liu,

Huang

et al. 2023

Sensors

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This work proposes a new global FD-RTM method to solve the problem of ultrasonic inspection of parts with complex geometric shapes. With this method, the frequency domain reverse time migration (FD-RTM) algorithm is used to adapt to the complex refraction of ultrasonic waves by the surface, while an interface solution algorithm based on tangent fitting is used to solve the interface position with high precision through the full matrix reception data. Based on high-precision interface information, a hybrid extrapolation algorithm and a situation-specific probe movement strategy are used to enable the probe to find the next sampling point according to the direction of the workpiece surface, allowing complex surface topography features to be identified without relying on the workpiece CAD drawing. This makes it possible to achieve the automated inspection of workpieces. To verify the proposed method’s effectiveness, an aluminum alloy model with side-drilled holes (SDH) is used. The geometry of the model consists of multiple convex and concave surfaces. By comparing the local FD-RTM imaging with images synthesized using the entire scan path, it is shown that gFD-RTM improved the imaging performance. Compared with FD-RTM, the average signal-to-noise ratio of gFD-RTM was increased by 20%, and the array performance index (API) was reduced by 70%, indicating effective detection coverage.

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“…CMMs utilising limited field-of-view sensors for fullsurface profiling have also been thoroughly investigated [6]. Their use has relied on spline-surface approximations to predict surface positions [7][8][9], or planar raster-tangent path planning [10]. These methods all require saturation of user-sampled positions, user input to define surface tangents, or rely on tangents defined by a gantry constrained rasterization pattern.…”

Section: Introductionmentioning

confidence: 99%

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

Poole

Sutcliffe

Pierce

et al. 2021

Sensors

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Robotised Non-Destructive Testing (NDT) has revolutionised the field, increasing the speed of repetitive scanning procedures and ability to reach hazardous environments. Application of robot-assisted NDT within specific industries such as remanufacturing and Aersopace, in which parts are regularly moulded and susceptible to non-critical deformation has however presented drawbacks. In these cases, digital models for robotic path planning are not always available or accurate. Cutting edge methods to counter the limited flexibility of robots require an initial pre-scan using camera-based systems in order to build a CAD model for path planning. This paper has sought to create a novel algorithm that enables robot-assisted ultrasonic testing of unknown surfaces within a single pass. Key to the impact of this article is the enabled autonomous profiling with sensors whose aperture is several orders of magnitude smaller than the target surface, for surfaces of any scale. Potential applications of the algorithm presented include autonomous drone and crawler inspections of large, complex, unknown environments in addition to situations where traditional metrological profiling equipment is not practical, such as in confined spaces. In simulation, the proposed algorithm has completely mapped significantly curved and complex shapes by utilising only local information, outputting a traditional raster pattern when curvature is present only in a single direction. In practical demonstrations, both curved and non-simple surfaces were fully mapped with no required operator intervention. The core limitations of the algorithm in practical cases is the effective range of the applied sensor, and as a stand-alone method it lacks the required knowledge of the environment to prevent collisions. However, since the approach has met success in fully scanning non-obstructive but still significantly complex surfaces, the objectives of this paper have been met. Future work will focus on low-accuracy environmental sensing capabilities to tackle the challenges faced. The method has been designed to allow single-pass scans for Conformable Wedge Probe UT scanning, but may be applied to any surface scans in the case the sensor aperture is significantly smaller than the part.

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Profile tracking for multi-axis ultrasonic inspection of model-unknown free-form surfaces based on energy concentration

Cited by 9 publications

References 31 publications

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

A back propagation neural network-based adaptive sampling strategy for uncertainty surfaces

Adaptive Ultrasonic Full Matrix Capture Process for the Global Imaging of Complex Components with Curved Surfaces

A Novel Complete-Surface-Finding Algorithm for Online Surface Scanning with Limited View Sensors

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