Assessing the Geometric Integrity of Additive Manufactured Parts From Point Cloud Data Using Spectral Graph Theoretic Sparse Representation-Based Classification

Abstract

This work proposes a novel approach for geometric integrity assessment of additive manufactured (AM, 3D printed) components, exemplified by acrylonitrile butadiene styrene (ABS) polymer parts made using fused filament fabrication (FFF) process. The following two research questions are addressed in this paper: (1) what is the effect of FFF process parameters, specifically, infill percentage (If) and extrusion temperature (Te) on geometric integrity of ABS parts?; and (2) what approach is required to differentiate AM parts with respect to their geometric integrity based on sparse sampling from a large (∼ 2 million data points) laser-scanned point cloud dataset? To answer the first question, ABS parts are produced by varying two FFF parameters, namely, infill percentage (If) and extrusion temperature (Te) through design of experiments. The part geometric integrity is assessed with respect to key geometric dimensioning and tolerancing (GD&T) features, such as flatness, circularity, cylindricity, root mean square deviation, and in-tolerance percentage. These GD&T parameters are obtained by laser scanning of the FFF parts. Concurrently, coordinate measurements of the part geometry in the form of 3D point cloud data is also acquired. Through response surface statistical analysis of this experimental data it was found that discrimination of geometric integrity between FFF parts based on GD&T parameters and process inputs alone was unsatisfactory (regression R2 < 50%). This directly motivates the second question. Accordingly, a data-driven analytical approach is proposed to classify the geometric integrity of FFF parts using minimal number (< 2% of total) of laser-scanned 3D point cloud data. The approach uses spectral graph theoretic Laplacian eigenvalues extracted from the 3D point cloud data in conjunction with a modeling framework called sparse representation to classify FFF part quality contingent on the geometric integrity. The practical outcome of this work is a method that can quickly classify the part geometric integrity with minimal point cloud data and high classification fidelity (F-score > 95%), which bypasses tedious coordinate measurement.

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.…”

“…The aim is to minimize the magnitude of deviations within these five GD&T characteristics of parts from the targeted design specifications. A NextEngine (Santa Monica, CA) HD 3D laser scanner was used to capture part geometric data, and the QA SCAN 4.0 software (Santa Monica, CA) 2 was used to estimate the deviations from the targeted design specifications [2][3][4][5]. shows examples of contour plots of absolute value for deviations within flatness and concentricity versus two controllable FFF process parameters: infill percentage (I f ) and extruder temperature (t e ).…”

“…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.…”

“…11. Based on the initial screening designs, we take two important controllable process parameters including percentage infill (I f ) and extruder temperature (t f ) [2][3][4][5]. Extruder temperature is the temperature at which the filament is heated in the extruder.…”

“…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).…”