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A conceptual framework for witness builds and witness artifacts in additive manufacturing

A conceptual framework for witness builds and witness artifacts in additive manufacturing Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.Design/methodology/approachThis conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.FindingsThe proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.Originality/valueThough variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Rapid Prototyping Journal Emerald Publishing

A conceptual framework for witness builds and witness artifacts in additive manufacturing

Rapid Prototyping Journal , Volume 27 (6): 5 – Jul 15, 2021

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References (23)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1355-2546
DOI
10.1108/rpj-10-2020-0253
Publisher site
See Article on Publisher Site

Abstract

Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.Design/methodology/approachThis conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.FindingsThe proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.Originality/valueThough variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production.

Journal

Rapid Prototyping JournalEmerald Publishing

Published: Jul 15, 2021

Keywords: Additive manufacturing; Condition-based monitoring; Machine health monitoring; Witness artifacts; Witness builds

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