Dataset Information

Standard method for microCT-based additive manufacturing quality control 1: Porosity analysis.

ABSTRACT: MicroCT is a well-established technique that is used to analyze the interior of objects non-destructively, and it is especially useful for void or porosity analysis. Besides its widespread use, few standards exist and none for additive manufacturing as yet. This is due to the inherent differences in part design, sizes and geometries, which results in different scan resolutions and qualities. This makes direct comparison between different scans of additively manufactured parts almost impossible. In addition, different image analysis methodologies can produce different results. In this method paper, we present a simplified 10?mm cube-shaped coupon sample as a standard size for detailed analysis of porosity using microCT, and a simplified workflow for obtaining porosity information. The aim is to be able to obtain directly comparable porosity information from different samples from the same AM system and even from different AM systems, and to potentially correlate detailed morphologies of the pores or voids with improper process parameters. The method is applied to two examples of different characteristic types of voids in AM: sub-surface lack of fusion due to improper contour scanning, and tree-like pores growing in the build direction. This standardized method demonstrates the capability for microCT to not only quantify porosity, but also identify void types which can be used to improve AM process optimization.

SUBMITTER: du Plessis A

PROVIDER: S-EPMC6159003 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Standard method for microCT-based additive manufacturing quality control 1: Porosity analysis.

du Plessis Anton A Sperling Philip P Beerlink Andre A Tshabalala Lerato L Hoosain Shaik S Mathe Ntombi N le Roux Stephan G SG

MethodsX 20180915

MicroCT is a well-established technique that is used to analyze the interior of objects non-destructively, and it is especially useful for void or porosity analysis. Besides its widespread use, few standards exist and none for additive manufacturing as yet. This is due to the inherent differences in part design, sizes and geometries, which results in different scan resolutions and qualities. This makes direct comparison between different scans of additively manufactured parts almost impossible. ...[more]

PMID: 30271722

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Project description:Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Dataset Information

Standard method for microCT-based additive manufacturing quality control 1: Porosity analysis.

Publications

Standard method for microCT-based additive manufacturing quality control 1: Porosity analysis.

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