This paper, written by Dr. Arman Sabbaghi and Dr. Qiang Huang, has been recently published by the Annals of Applied Statistics. It presents a strategy based on the engineering effect equivalence principle to address the fundamental challenge in model transfer of handling lurking variables across different environments. The link for the published article follows below.
https://projecteuclid.org/euclid.aoas/1542078050
Comments and discussions are most welcome!
Sunday, November 25, 2018
Thursday, November 8, 2018
Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement -- Available online
http://manufacturingscience.asmedigitalcollection.asme.org/article.aspx?articleid=2707894
Co-authored by He Luan, Marco Grasso, Bianca M. Colosimo and Qiang Huang, this study develops a data-driven prescriptive modeling approach as a promising solution for geometric accuracy improvement in Laser powder bed fusion (LPBF) processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.
Our modeling work is applicable to relatively repeatable LPBF processes where there are no large build-to-build variations. Machine-to-machine variation is not considered in this study. Though the proposed data-analytical black-box modeling framework can be applicable to the production of other geometries, further experimentation and analysis is needed to investigate the LPBF process performance when building larger products with more complicated shapes
Co-authored by He Luan, Marco Grasso, Bianca M. Colosimo and Qiang Huang, this study develops a data-driven prescriptive modeling approach as a promising solution for geometric accuracy improvement in Laser powder bed fusion (LPBF) processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.
Our modeling work is applicable to relatively repeatable LPBF processes where there are no large build-to-build variations. Machine-to-machine variation is not considered in this study. Though the proposed data-analytical black-box modeling framework can be applicable to the production of other geometries, further experimentation and analysis is needed to investigate the LPBF process performance when building larger products with more complicated shapes
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