Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

Abstract

The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score > 97%).

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

“…A systematic optimization approach for improving the geometric accuracy of AM parts is motivated by the experimental response data collected for a benchmark part. The presented experimental data in this work are generated in the authors' previous research [2][3][4][5]. The so-called circle-diamond -square part is designed as the benchmark part of interest for geometrical optimization ( Fig.…”

“…Indeed, there should be tradeoffs for the different responses. Tootooni et al [2,3] and Rao et al [17] used a spectral graph theory methodology to quantify and assess the geometric accuracy of FFF parts using deviations of 3D point cloud coordinate measurements from design specifications. Although the proposed indicator facilitates comparing the geometric accuracy of parts, it does not propose a relationship between process parameters and geometric accuracy in terms of GD&T characteristics.…”

“…The motivation for this work, stemming from our previous studies, is demonstrated in Fig. 1 [2][3][4][5]. At the outset, we note that the intent of this example is not to claim that there is an inadequacy with the standardized geometric GD&T characteristics but to illustrate the difficulty in balancing between several tight geometric accuracy requirements for an AM part (in terms of GD&T characteristics).…”

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

“…(3) Algorithm trend Currently, the learning-based point cloud processing algorithms are the mainstream trend. Machine learning methods [77,[125][126][127] train efficient classifiers by designing an effective feature descriptor. It highly relies on the prior knowledge of the human operators, which is a challenging task in the complex urban environments.…”

Application of Machine Learning Methods to Improve Dimensional Accuracy in Additive Manufacturing

“…The main focus is set on application of ANN, genetic algorithms (GA), support vector machines (SVM). Fewer articles used deep neural networks, principal component analysis (PCA) and particle swarm optimization (PSO) [8][9][10]. While ANN is used to optimize process parameters, predict mechanical properties and porosity of the object, deep learning techniques were already applied in order "to identify styles of 3D models" based on 2D images rendered from digital 3D models [7].…”