Cyber-enabled concurrent material and process selection in a flexible design for manufacture paradigm

Desai, Salil; Dean, Christopher F.; Desai, Yogeeta

doi:10.1007/s00170-018-2034-6

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…AI has proved its superiority in various fields, such as autonomous driving [35], natural language processing [36], medical diagnosis [37], computer vision [38], intelligent manufacturing [39], computer (imaging) vision [40], spectral informatics [41], ultrafast photonics [42], fiber lasers [43], and drug delivery [44]. AI is increasingly being integrated into various nanomanufacturing processes, such as nano assembly, the creation of nanotubes, nanomedicine, nanorobotics, and nanosensors, denoting its essential role in the advancement of nanotechnology [45][46][47][48][49][50][51]. Table 2 shows an exhaustive list of such nanomanufacturing processes that incorporate the use of AI.…”

Section: Artificial Intelligencementioning

confidence: 99%

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Nandipati,

Fatoki,

Desai

2024

Materials

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Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

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Section: Artificial Intelligencementioning

confidence: 99%

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Nandipati,

Fatoki,

Desai

2024

Materials

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“…However, there was no study investigating fabrication approaches to MNs arrays based on the manipulation of process parameters. Several types of predictive models have been used in literature to improve the performance of manufacturing processes [ 219 , 220 , 221 , 222 , 223 , 224 ] which can be extended for MN fabrication. Prior literature has demonstrated that computational modeling [ 225 , 226 , 227 , 228 , 229 , 230 ] coupled with novel manufacturing processes can deliver complex biomolecules for biomedical applications.…”

Section: Gaps In Research and Future Outlookmentioning

confidence: 99%

A Comprehensive Review of Microneedles: Types, Materials, Processes, Characterizations and Applications

2021

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Drug delivery through the skin offers many advantages such as avoidance of hepatic first-pass metabolism, maintenance of steady plasma concentration, safety, and compliance over oral or parenteral pathways. However, the biggest challenge for transdermal delivery is that only a limited number of potent drugs with ideal physicochemical properties can passively diffuse and intercellularly permeate through skin barriers and achieve therapeutic concentration by this route. Significant efforts have been made toward the development of approaches to enhance transdermal permeation of the drugs. Among them, microneedles represent one of the microscale physical enhancement methods that greatly expand the spectrum of drugs for transdermal and intradermal delivery. Microneedles typically measure 0.1–1 mm in length. In this review, microneedle materials, fabrication routes, characterization techniques, and applications for transdermal delivery are discussed. A variety of materials such as silicon, stainless steel, and polymers have been used to fabricate solid, coated, hollow, or dissolvable microneedles. Their implications for transdermal drug delivery have been discussed extensively. However, there remain challenges with sustained delivery, efficacy, cost-effective fabrication, and large-scale manufacturing. This review discusses different modes of characterization and the gaps in manufacturing technologies associated with microneedles. This review also discusses their potential impact on drug delivery, vaccine delivery, disease diagnostic, and cosmetics applications.

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“…To build polymeric parts layer-by-layer, the FDM additive manufacturing process is an efficient and costeffective process (Desai et al, 2018). In the fused deposition model (FDM) process an extrusion head is heated up to squeeze out filaments of polymeric materials.…”

Section: Fused Deposition Modeling (Fdm)mentioning

confidence: 99%

Investigating Rapid Thermoform Tooling Via Additive Manufacturing (3d Printing)

Haeberle¹,

Desai²

2019

American Journal of Applied Sciences

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Additive Manufacturing (AM) has matured from a prototyping tool to a soft production tooling in recent years. This research elaborates the role of AM technology (3D printing) in the design, prototyping and development of a rapid thermoform tooling. The Fused Deposition Modeling (FDM) 3D printer was implemented to fabricate thermoform tooling in Polypropylene Styrofoam (PPSF). We contrast the additively manufactured thermoform tooling with conventional tooling using computerized numerical control (CNC) machines. Findings showed that the additive thermoform tooling with PPSF was found to be (1) faster in development time, (2) comparable in quality and (3) lower in total cost. However, the (4) tool yield was 50% less than traditional tooling and production cycle times were 25-50% longer to allow for the additive tooling to cool. This research explores the fabrication thermoform tooling for low-volume environments using additive manufacturing (3D Printing) technology.

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Cyber-enabled concurrent material and process selection in a flexible design for manufacture paradigm

Cited by 16 publications

References 26 publications

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

A Comprehensive Review of Microneedles: Types, Materials, Processes, Characterizations and Applications

Investigating Rapid Thermoform Tooling Via Additive Manufacturing (3d Printing)

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