Framework for Automated GD&amp;T Inspection Using 3D Scanner

Guardiola

et al. 2020

Sensors

Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.

Section: State Of the Artmentioning

confidence: 99%

Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

Guardiola

et al. 2020

Sensors

“…We do not address in this paper the defect detection process which has to be applied after reconstruction to compare the test object against a reference model. Recent works in this area, like [ 20 ] or [ 21 ], show promising results even in difficult tasks.…”

Section: Introductionmentioning

confidence: 99%

A System for In-Line 3D Inspection without Hidden Surfaces

Pérez-Cortés

Sáez

et al. 2018

Sensors

This work presents a 3D scanner able to reconstruct a complete object without occlusions, including its surface appearance. The technique presents a number of differences in relation to current scanners: it does not require mechanical handling like robot arms or spinning plates, it is free of occlusions since the scanned part is not resting on any surface and, unlike stereo-based methods, the object does not need to have visual singularities on its surface. This system, among other applications, allows its integration in production lines that require the inspection of a large volume of parts or products, especially if there is an important variability of the objects to be inspected, since there is no mechanical manipulation. The scanner consists of a variable number of industrial quality cameras conveniently distributed so that they can capture all the surfaces of the object without any blind spot. The object is dropped through the common visual field of all the cameras, so no surface or tool occludes the views that are captured simultaneously when the part is in the center of the visible volume. A carving procedure that uses the silhouettes segmented from each image gives rise to a volumetric representation and, by means of isosurface generation techniques, to a 3D model. These techniques have certain limitations on the reconstruction of object regions with particular geometric configurations. Estimating the inherent maximum error in each area is important to bound the precision of the reconstruction. A number of experiments are presented reporting the differences between ideal and reconstructed objects in the system.

“…[2][3][4] But nowadays, use of noncontact 3D scanner is increasing for CAD modeling, drawing of physical components and part inspection purposes. [5][6][7] The laser-scanning technology has an inbuilt capacity to capture the pro¯le of a component, and therefore, has received considerable appreciation in the industry. It can capture data with fast speed and can acquire thousands to millions of 3D point data.…”

Section: Introductionmentioning

confidence: 99%

Form Error Evaluation of Noncontact Scan Data Using Constriction Factor Particle Swarm Optimization

Pathak

Singh

2017

J. Adv. Manuf. Syst.

Self Cite

Form error evaluation of manufactured parts is one of the crucial aspects of precision coordinate metrology. With the advent of technology, the noncontact data acquisition techniques are replacing the conventional machines like coordinate measuring machine (CMM). This paper presents an optimization technique to evaluate minimum zone form errors, namely straightness, circularity,°atness and cylindricity using constriction factor-based particle swarm optimization (CFPSO) algorithm. Addition of constriction factor helps in accelerating the convergence property of CFPSO. Initially, a simple minimum zone objective function is formulated mathematically for each form error and then optimized using the proposed CFPSO. Primarily, the results of the proposed method for form error evaluation are compared with the literature results. Furthermore, the data obtained from noncontact 3D scanner is processed and the results of form error evaluation using CFPSO algorithm are compared with Steinbichler's INSPECT PLUS software results. It was found that the results obtained using the proposed CFPSO algorithm are fast and better as compared with other evolutionary techniques like genetic algorithm (GA), previous literatures and software results. Furthermore, to ensure e®ectiveness of the proposed method statistical analysis (t-test) was performed. CFPSO results for large dimension of problem show signi¯cant di®erence in computation time as compared with GA. The CFPSO algorithm provides 27.25%, 7.5% and 6.38% improvements in circularity,°atness and cylindricity, respectively, in comparison to RE software results, for determination of minimum zone error. Thus, the methodology presented helps in improving the accuracy and for speeding up of the automated inspection process generally performed by CMMs in industries.