Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

Bozorg, Neda Madadian; Leclercq, Mickaël; Lescot, Théophraste; Bazin, M.; Gaudreault, Nicolas; Dikpati, Amrita; Fortin, Marc‐André; Droit, Arnaud; Bertrand, Nicolas

doi:10.1016/j.bioadv.2023.213533

Cited by 2 publications

(2 citation statements)

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“…The main advantage of this approach is the minimization of harmful effects during the production of certain materials. 181,182 Therefore, research on machine learning and materials optimization is rapidly developing, especially in the last 10 years, and is already for design of 2D materials, 183–185 hydrogels, 186–189 supramolecular structures, 190 processes of photocatalysis, 191–194 energy storage, 195–197 layer-by-layer structures 198,199 and others (Fig. 7b).…”

Section: Machine Learning For the Optimisation Of The Materialsmentioning

confidence: 99%

Layered nanomaterials for renewable energy generation and storage

Nikitina,

Lavrentev,

Yurova

et al. 2024

Mater. Adv.

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Section: Machine Learning For the Optimisation Of The Materialsmentioning

confidence: 99%

Layered nanomaterials for renewable energy generation and storage

Nikitina,

Lavrentev,

Yurova

et al. 2024

Mater. Adv.

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“…DOE has its roots in the work of Sir Ronald Fisher, who developed the basic principles of DOE in the early twentieth century. Fisher's work was initially applied to agricultural research, but it was soon adapted for use in other fields, including manufacturing, engineering, and medicine [9]. Since then, many scientists and statisticians have contributed to DOE development and its application in different fields [9][10][11][12].…”

Section: History Of Doementioning

confidence: 99%

Design of Experiments (DOE): Applications and Benefits in Quality Control and Assurance

Lamidi,

Olalere,

Yekinni

et al. 2024

Quality Control and Quality Assurance - Techniques and Applications

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This chapter explores the applications and benefits of Design of Experiments (DOE) in the context of quality control and quality assurance. DOE is a statistical methodology that enables researchers and practitioners to systematically investigate and optimize processes, identify critical factors affecting quality, and reduce variability and waste. This chapter begins by introducing the overview and definitions of DOE, covering topics such as the history of DOE, types of DOE, steps involved in conducting DOE, and key components of DOE. The specific applications of DOE in quality control and quality assurance were explored, highlighting their importance across various industries. It demonstrates how DOE can be effectively applied to optimize products and processes, reduce defects and variation, improve quality, implement Six Sigma, and validate and verify processes. It then delves into the specific applications of DOE in quality control and assurance, highlighting its significance in various industries and sectors. Furthermore, the book addresses challenges and considerations in implementing DOE in real-world scenarios, such as resource constraints, experimental constraints, and data analysis complexities. It provides basic information on software tools commonly used in DOE.

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Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

Cited by 2 publications

References 50 publications

Layered nanomaterials for renewable energy generation and storage

Layered nanomaterials for renewable energy generation and storage

Design of Experiments (DOE): Applications and Benefits in Quality Control and Assurance

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