國立虎尾科技大學 |

Data-Driven Process Optimization of Additive Manufacturing Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Process Optimization of Additive Manufacturing Systems./
作者:	Aboutaleb, Amirmassoud.
面頁冊數:	1 online resource (215 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355920963

Data-Driven Process Optimization of Additive Manufacturing Systems.
Aboutaleb, Amirmassoud.

Data-Driven Process Optimization of Additive Manufacturing Systems. - 1 online resource (215 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Mississippi State University, 2018.

Includes bibliographical references

The goal of the present dissertation is to develop and apply novel and systematic data-driven optimization approaches that can efficiently optimize Additive Manufacturing (AM) systems with respect to targeted properties of final parts. The proposed approaches are capable of achieving sets of process parameters that result in the satisfactory level of part quality in an accelerated manner. First, an Accelerated Process Optimization (APO) methodology is developed to optimize an individual scalar property of parts. The APO leverages data from similar---but non-identical---prior studies to accelerate sequential experimentation for optimizing the AM system in the current study. Using Bayesian updating, the APO characterizes and updates the difference between prior and current experimental studies. The APO accounts for the differences in experimental conditions and utilizes prior data to facilitate the optimization procedure in the current study. The efficiency and robustness of the APO is tested against an extensive simulation studies and a real-world case study for optimizing relative density of stainless steel parts fabricated by a Selective Laser Melting (SLM) system. Then, we extend the idea behind the APO to handle multi-objective process optimization problems in which some of the characteristics of the AM-fabricated parts are uncorrelated. The proposed Multi-objective Process Optimization (m-APO) breaks down the master multi-objective optimization problem into a series of convex combinations of single-objective sub-problems. The m-APO maps and scales experimental data from previous sub-problems to guide remaining sub-problems that improve the solutions while reducing the number of experiments required. The robustness and efficiency of the m-APO is verified by conducting a series of simulation studies and a real-world case study to minimize geometric inaccuracy of parts fabricated by a Fused Filament Fabrication (FFF) system. At the end, we apply the proposed m-APO to optimize the mechanical properties of AM-fabricated parts that show conflicting behavior, namely relative density and elongation-to-failure. Numerical studies show that the m-APO can achieve the best trade-off among conflicting mechanical properties while significantly reducing the number of experimental runs compared with existing methods.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355920963Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Data-Driven Process Optimization of Additive Manufacturing Systems.
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
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502 $a Thesis (Ph.D.)--Mississippi State University, 2018.
504 $a Includes bibliographical references
520 $a The goal of the present dissertation is to develop and apply novel and systematic data-driven optimization approaches that can efficiently optimize Additive Manufacturing (AM) systems with respect to targeted properties of final parts. The proposed approaches are capable of achieving sets of process parameters that result in the satisfactory level of part quality in an accelerated manner. First, an Accelerated Process Optimization (APO) methodology is developed to optimize an individual scalar property of parts. The APO leverages data from similar---but non-identical---prior studies to accelerate sequential experimentation for optimizing the AM system in the current study. Using Bayesian updating, the APO characterizes and updates the difference between prior and current experimental studies. The APO accounts for the differences in experimental conditions and utilizes prior data to facilitate the optimization procedure in the current study. The efficiency and robustness of the APO is tested against an extensive simulation studies and a real-world case study for optimizing relative density of stainless steel parts fabricated by a Selective Laser Melting (SLM) system. Then, we extend the idea behind the APO to handle multi-objective process optimization problems in which some of the characteristics of the AM-fabricated parts are uncorrelated. The proposed Multi-objective Process Optimization (m-APO) breaks down the master multi-objective optimization problem into a series of convex combinations of single-objective sub-problems. The m-APO maps and scales experimental data from previous sub-problems to guide remaining sub-problems that improve the solutions while reducing the number of experiments required. The robustness and efficiency of the m-APO is verified by conducting a series of simulation studies and a real-world case study to minimize geometric inaccuracy of parts fabricated by a Fused Filament Fabrication (FFF) system. At the end, we apply the proposed m-APO to optimize the mechanical properties of AM-fabricated parts that show conflicting behavior, namely relative density and elongation-to-failure. Numerical studies show that the m-APO can achieve the best trade-off among conflicting mechanical properties while significantly reducing the number of experimental runs compared with existing methods.
520 $a Keywords: additive manufacturing, process optimization, design of experiments, Bayesian updating, multi-objective optimization, geometric accuracy, principal component analysis, tensile properties.
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