A data driven design strategy to improve quality in additive manufacturing

Additively manufactured end part has been gaining lots of attention and interest in the recent years in aviation industry, especially parts manufactured using Fused Filament Fabrication (FFF). However, there still exist significant barriers to adopt AM parts for loaded components. End use part requi...

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Main Author: Zhang, Yongjie
Other Authors: Moon Seung Ki
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/157350
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1573502023-03-11T18:10:06Z A data driven design strategy to improve quality in additive manufacturing Zhang, Yongjie Moon Seung Ki School of Mechanical and Aerospace Engineering ST Engineering Aerospace Ltd Singapore Centre for 3D Printing skmoon@ntu.edu.sg Engineering::Manufacturing::Flexible manufacturing systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Additively manufactured end part has been gaining lots of attention and interest in the recent years in aviation industry, especially parts manufactured using Fused Filament Fabrication (FFF). However, there still exist significant barriers to adopt AM parts for loaded components. End use part requires assurance of part mechanical strength, especially for high criticality parts and parts that requires certification. Extensive testing is required to take into account confounding and interactions between the process parameters on the responses of interest, due to property structure process linkage. As such, if AM designers were to understand and characterise the design space through conventional trial and error methods, in order to produce optimised AM components, the cost is extremely high. Thus, to alleviate the situation, a data-driven design strategy is proposed, that aid designers to characterise the design space and optimise AM component designs efficiently and effectively. In the proposed data-driven design framework, the following is proposed: 1) An overall data driven framework that is efficient and effective in characterisation of design space, prediction, and optimisation of FFF parts responses of interest. 2) Establishing a methodology for FFF part mechanical properties and aesthetic prediction and optimisation. The surrogate model utilises Gaussian Process Regression to model the responses of interest and using multi-objective optimisation to trade of conflicting response of interest and obtain the optimal FFF part design 3) Understanding the PSP linkage, deposition strategy, and their impact on mechanical properties of FFF parts. This enables greater understanding and prediction of localised features found in FFF parts. 4) Data- driven, physics-based prediction of mechanical properties of FFF parts. Bayesian hierarchical modelling is proposed to model the influence of process variability and uncertainty propagation on the mechanical performance. To demonstrate the effectiveness of the framework, the methodology is validated against a case study, which predicted and optimised the FFF part properties in both conflicting response of mechanical strength and surface roughness requirements. Further, framework enables the AM designers to visualise the variation in the response of interest due to changes in the process parameters. Limitations of current proposed approach includes technology specificity (i.e. it is only currently applicable to FFF) and the framework can be integrated with the toolpath slicing software for improved prediction. Although there are limitations to the proposed framework, the methodology forms the fundamentals which AM designers can utilise to efficiently and effectively characterise the design space, predict and optimise FFF part performance. Doctor of Philosophy 2022-05-12T01:38:22Z 2022-05-12T01:38:22Z 2021 Thesis-Doctor of Philosophy Zhang, Y. (2021). A data driven design strategy to improve quality in additive manufacturing. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157350 https://hdl.handle.net/10356/157350 10.32657/10356/157350 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Manufacturing::Flexible manufacturing systems
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Manufacturing::Flexible manufacturing systems
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Zhang, Yongjie
A data driven design strategy to improve quality in additive manufacturing
description Additively manufactured end part has been gaining lots of attention and interest in the recent years in aviation industry, especially parts manufactured using Fused Filament Fabrication (FFF). However, there still exist significant barriers to adopt AM parts for loaded components. End use part requires assurance of part mechanical strength, especially for high criticality parts and parts that requires certification. Extensive testing is required to take into account confounding and interactions between the process parameters on the responses of interest, due to property structure process linkage. As such, if AM designers were to understand and characterise the design space through conventional trial and error methods, in order to produce optimised AM components, the cost is extremely high. Thus, to alleviate the situation, a data-driven design strategy is proposed, that aid designers to characterise the design space and optimise AM component designs efficiently and effectively. In the proposed data-driven design framework, the following is proposed: 1) An overall data driven framework that is efficient and effective in characterisation of design space, prediction, and optimisation of FFF parts responses of interest. 2) Establishing a methodology for FFF part mechanical properties and aesthetic prediction and optimisation. The surrogate model utilises Gaussian Process Regression to model the responses of interest and using multi-objective optimisation to trade of conflicting response of interest and obtain the optimal FFF part design 3) Understanding the PSP linkage, deposition strategy, and their impact on mechanical properties of FFF parts. This enables greater understanding and prediction of localised features found in FFF parts. 4) Data- driven, physics-based prediction of mechanical properties of FFF parts. Bayesian hierarchical modelling is proposed to model the influence of process variability and uncertainty propagation on the mechanical performance. To demonstrate the effectiveness of the framework, the methodology is validated against a case study, which predicted and optimised the FFF part properties in both conflicting response of mechanical strength and surface roughness requirements. Further, framework enables the AM designers to visualise the variation in the response of interest due to changes in the process parameters. Limitations of current proposed approach includes technology specificity (i.e. it is only currently applicable to FFF) and the framework can be integrated with the toolpath slicing software for improved prediction. Although there are limitations to the proposed framework, the methodology forms the fundamentals which AM designers can utilise to efficiently and effectively characterise the design space, predict and optimise FFF part performance.
author2 Moon Seung Ki
author_facet Moon Seung Ki
Zhang, Yongjie
format Thesis-Doctor of Philosophy
author Zhang, Yongjie
author_sort Zhang, Yongjie
title A data driven design strategy to improve quality in additive manufacturing
title_short A data driven design strategy to improve quality in additive manufacturing
title_full A data driven design strategy to improve quality in additive manufacturing
title_fullStr A data driven design strategy to improve quality in additive manufacturing
title_full_unstemmed A data driven design strategy to improve quality in additive manufacturing
title_sort data driven design strategy to improve quality in additive manufacturing
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157350
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