Neural networks and deep learning

López-Monroy, Adrián Pastor; García-Salinas, Jesús S.

doi:10.1016/b978-0-12-820125-1.00021-x

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Here, simple ML (or numeric ML) is essentially utilized in order to treat numeric data such as tables. Deep machine learning (DML) (or deep learning) [93] is mainly utilized in order to deal with images. In our previous work [19], a diagram to implement the DML technique, with the objective of treating several failure images in AM, was established considering the failure images for a single failure mode.…”

Section: Am-pla Using Fff For Dentistry With Consideration For Artifi...mentioning

confidence: 99%

“…For the second failure mode (Mode 2), a comparison with the slicing model can be carried out in order to identify the deviation levels. As illustrated in Figure 10, when introducing a new image (image (c)) as input to test, neural networks (several types of layers [93][94][95] are used to make a prediction to identify what the failure mode is). So, for the input image (c), the first test is to identify the density.…”

Section: Am-pla Using Fff For Dentistry With Consideration For Artifi...mentioning

confidence: 99%

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Challenges and Future Perspectives for Additively Manufactured Polylactic Acid Using Fused Filament Fabrication in Dentistry

Kharmanda

2023

JFB

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Additive manufacturing (AM), which is also called rapid prototyping/3D printing/layered manufacturing, can be considered as a rapid conversion between digital and physical models. One of the most used materials in AM is polylactic acid (PLA), which has advantageous material properties such as biocompatibility, biodegradability, and nontoxicity. For many medical applications, it is considered as a leading biomaterial. In dentistry, in addition to its uses in dental models (education, teaching, simulation needs), it can be used for therapeutic objectives and tissue engineering. The fused filament fabrication (FFF) technique, also called fused deposition modeling (FDM), is widely used as an AM technique to perform complex and functional geometries directly from CAD files. In this review, the objective was to present the different challenges and future perspectives of this additively manufactured material by using FFF in dentistry areas. Some suggestions for future directions to extend to more dental applications (support structures, lattice structures, etc.) and to consider more criteria (sustainability, uncertainty etc.) will be discussed. Advanced studies such as machine learning (ML) techniques will be suggested to reduce the failure cases when using the additively manufactured PLA by FFF in dentistry.

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Section: Am-pla Using Fff For Dentistry With Consideration For Artifi...mentioning

confidence: 99%

Section: Am-pla Using Fff For Dentistry With Consideration For Artifi...mentioning

confidence: 99%