Artificial Intelligence: The Future Landscape of Genomic Medical Diagnosis: Dataset, In Silico Artificial Intelligent Clinical Information, and Machine Learning Systems

Baker, D.

doi:10.1016/b978-0-12-809414-3.00011-5

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…ML is a subset of Artificial Intelligence (AI) that builds predictive or decision-making models using historical data and improves upon these models through experience [29][30][31][32]. Like 3DP, ML is impacting several sectors [33][34][35], such as outperforming clinicians in diagnosis in the medical sector [36] and streamlining the drug to market process in the pharmaceutical sector [37,38].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

et al. 2021

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Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

et al. 2021

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“…An integrated ecosystem for computer-aided process planning and execution in sheet metal cutting operations is formed by these technologies and software applications. Manufacturers may cut costs and lead times while optimizing their operations, increasing productivity, and improving product quality by skillfully utilizing these technologies [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

Amol Rasane, Chandrmani Yadav, Ravi Shankar Rai

2024

jes

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This paper describes computer-aided process planning for sheet metal cutting operations. The two designs to production stages that is highlighted are sheet metal processing and machining. There are four modules in this object's system. Virtual factory environments, feature-based designs, process planning, and process-based feature mapping. Feature-based design is utilized for the conception, modeling, and representation of the components for manufacturing applications. Whenever it involves sheet metal cutting operations in the manufacturing sector, computer-aided process planning, is extremely important for streamlining manufacturing procedures. The provides a general introduction to computer-aided process planning as it relates to sheet metal cutting, emphasizing its importance in boosting productivity, saving costs, and raising product quality. The main goal of computer-aided process planning for sheet metal cutting is to integrate computer technology, CAD/CAM systems, and sophisticated algorithms to automate and streamline the planning process. Based on design requirements, material characteristics, and production limitations, this method enables manufacturers to produce exact and ideal cutting plans. To create blanking and piercing holes, stamped or punched die are utilized in generative shape design; the generative computer-aided process planning system is created in C++ and used in various case studies presented in the present work. The application of computer-aided process planning for sheet metal cutting has several advantages, including greater output, less material waste, shortened lead times, and improved competitiveness in the industrial sector. The potential of computer-aided process planning to revolutionize sheet metal cutting operations, making them more efficient and cost-effective while ensuring high-quality final products is highlighted in the present research.

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Stereolithography 3D printing technology in pharmaceuticals: a review

Deshmane¹,

Kendre²,

Mahajan

et al. 2021

Drug Development and Industrial Pharmacy

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Artificial Intelligence: The Future Landscape of Genomic Medical Diagnosis: Dataset, In Silico Artificial Intelligent Clinical Information, and Machine Learning Systems

Cited by 3 publications

References 60 publications

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

Stereolithography 3D printing technology in pharmaceuticals: a review

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