A laser scanning posture optimization method to reduce the measurement uncertainty of large complex surface parts

3D scanning is a crucial step to ensure the machining quality of the workpiece and is an essential part of intelligent manufacturing. However, existing scanning systems usually have only one profiler, which must be combined with a dynamic tracking system to achieve a complete scan of a workpiece. This scanning method has low efficiency and complicated path planning for ring-shaped workpieces. Therefore, in this article, an efficient and high-accuracy 3D scanning system is built, which is composed of a linear translation stage and four uniformly distributed laser profilers, and its extrinsic calibration method is studied. At first, based on the working parameters and spatial layout of multiple profilers, a stereoscopic calibrator composed of three non-collinear target balls (TBs) is designed. Then, a multi-profiler data fusion method is proposed, which utilizes a linear encoder to trigger the four profilers synchronously. Finally, by simultaneously using all data from the multiple profilers and the spherical constraint of each TB, all extrinsic parameters are accurately calibrated at the same time. Experimental results show that the average probing size error of the TB with a 38.1mm diameter is stable at about 0.007mm, and its extended uncertainty is about 0.100mm (k=2). In addition, standard cylinders and bend tubes are scanned, the results show that the proposed method can meet the high-accuracy calibration requirements of the tube bending deformation detection system.

Section: Frame-by-frame Fusion Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extrinsic calibration method for 3D scanning system with four coplanar laser profilers

Zou¹,

Xia²,

Zhao³

et al. 2022

“…Yuncai [8] based on the linear structured light measurement system analysis of camera calibration, repeatability, and other errors, the analysis and calculation of the gear tooth profile orientation extended the uncertainty. Li et al [9] proposed a posture optimization method to effectively reduce the uncertainty when laser scanning the surfaces but did not consider the error caused by the surrounding environment and the object itself, which led to the imperfect uncertainty evaluation. Popov et al [10] analyzed the error sources of a Digihot laser scanner and believed that the scanning error included random error and system error, and that the random error could be reduced by eliminating abnormal values.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty modeling and evaluation of profile measurement by structured light scanner

zhao¹,

Cheng²,

Xu³

et al. 2022

A structured light scanner is mainly used to collect three-dimensional (3D) data of an object; however, the uncertainty evaluation of a specific measurement task is not perfect. This study discusses the source of the uncertainty component of the measurement system of a structured blue-light scanner, including the instrument itself, data acquisition, data processing, and other factors. Using the surface contour as the evaluation object, Polyworks software was used to perform the data processing of the point cloud denoising, splicing, and registration. An uncertainty evaluation is performed based on the black-box evaluation model. The experimental results show that the uncertainty components introduced by the instrument indication error, point cloud stitching, and point cloud registration are the main components of the entire measurement system. The Bootstrap method does not need to consider the probability distribution type of the error sources, which improves computational efficiency. This method has several advantages when the distribution state is unknown.This work is conducive to improving the task-oriented uncertainty evaluation of 3D scanners.

“…A study by Gestel et al [5] proves the great influence of scan depth on the SD. A study by Li et al [19] presents a scanner posture optimisation method for reducing the measurement uncertainty of laser scanning data for complex surfaces. The method optimises the pitch and deflection angle and finds an optimal scanning posture within the posture adjustment interval so that the measurement uncertainty in the scanning area is minimised.…”

Section: Introductionmentioning

confidence: 99%

Machine learning method for predicting the influence of scanning parameters on random measurement error

Urbas

Vlah

Vukašinović

2021

Measurements of technical objects can be done with contact and non-contact approaches. Contact methods are accurate but slow. On the other hand, non-contact methods deliver rapid point acquisition and are increasingly being used as their precision mounts. However, multiple scanning parameters such as the incident angle, object colour and scanning distance influence the measurement error and uncertainty when capturing the geometry of the object. With the aim of creating a generalised model that considers the influence of the aforementioned scanning parameters with satisfactory accuracy, a model for predicting the random measurement error based on machine learning (ML) is proposed in this study. Data acquired from measurements with varying scanning distances, incident angles and surface colours were used to train ML models. The tested ML methods included linear regression, support vector machine, neural network, k-nearest neighbour, AdaBoost and random forest. The best-performing trained model was the random forest, with a standard deviation of relative differences of 1.46% for the case of red surfaces, and 5.2% for the case of an arbitrarily coloured surface, which is comparable to results achieved with model-based methods. The trained models and the data are available online.