Dataset Information

Standard method for microCT-based additive manufacturing quality control 3: Surface roughness.

ABSTRACT: The use of microCT of 10 mm coupon samples produced by AM has the potential to provide useful information of mean density and detailed porosity information of the interior of the samples. In addition, the same scan data can be used to provide surface roughness analysis of the as-built surfaces of the same coupon samples. This can be used to compare process parameters or new materials. While surface roughness is traditionally done using tactile probes or with non-contact interferometric techniques, the complex surfaces in AM are sometimes difficult to access and may be very rough, with undercuts and may be difficult to accurately measure using traditional techniques which are meant for smoother surfaces. This standard workflow demonstrates on a coupon sample how to acquire surface roughness results, and compares the results from roughly the same area of the same sample with tactile probe results. The same principle can be applied to more complex parts, keeping in mind the resolution limit vs sample size of microCT.

SUBMITTER: Plessis AD

PROVIDER: S-EPMC6168927 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Standard method for microCT-based additive manufacturing quality control 3: Surface roughness.

Plessis Anton du AD Sperling Philip P Beerlink Andre A Kruger Oelof O Tshabalala Lerato L Hoosain Shaik S le Roux Stephan G SG

MethodsX 20180915

The use of microCT of 10 mm coupon samples produced by AM has the potential to provide useful information of mean density and detailed porosity information of the interior of the samples. In addition, the same scan data can be used to provide surface roughness analysis of the as-built surfaces of the same coupon samples. This can be used to compare process parameters or new materials. While surface roughness is traditionally done using tactile probes or with non-contact interferometric technique ...[more]

PMID: 30294558

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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 3: Surface roughness.

Publications

Standard method for microCT-based additive manufacturing quality control 3: Surface roughness.

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