Dataset Information

Standard method for microCT-based additive manufacturing quality control 2: Density measurement.

ABSTRACT: MicroCT is best known for its ability to detect and quantify porosity or defects, and to visualize its 3D distribution. However, it is also possible to obtain accurate volumetric measurements from parts - this can be used in combination with the part mass to provide a good measure of its average density. The advantage of this density-measurement method is the ability to combine the density measurement with visualization and other microCT analyses of the same sample. These other analyses may include detailed porosity or void analysis (size and distribution) and roughness assessment, obtainable with the same scan data. Simple imaging of the interior of the sample allows the detection of unconsolidated powder, open porosity to the surface or the presence of inclusions. The CT density method presented here makes use of a 10 mm cube sample and a simple data analysis workflow, facilitating standardization of the method. A laboratory microCT scanner is required at 15 μm voxel size, suitable software to allow sub-voxel precise edge determination of the scanned sample and hence an accurate total volume measurement, and a scale with accuracy to 3 digits. •MicroCT-based mean density measurement method.•Accurate volume measurement and scale mass.•10 mm cube sample allows standardization and automation of workflow.

SUBMITTER: du Plessis A

PROVIDER: S-EPMC6168933 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Standard method for microCT-based additive manufacturing quality control 2: Density measurement.

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 20180926

MicroCT is best known for its ability to detect and quantify porosity or defects, and to visualize its 3D distribution. However, it is also possible to obtain accurate volumetric measurements from parts - this can be used in combination with the part mass to provide a good measure of its average density. The advantage of this density-measurement method is the ability to combine the density measurement with visualization and other microCT analyses of the same sample. These other analyses may incl ...[more]

PMID: 30294559

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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 2: Density measurement.

Publications

Standard method for microCT-based additive manufacturing quality control 2: Density measurement.

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