1-20 of 33
Keywords: machine learning
Close
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Proc. ASME. IMECE2021, Volume 2B: Advanced Manufacturing, V02BT02A009, November 1–5, 2021
Paper No: IMECE2021-69993
... a step-by-step design process for an intelligent gripper and discusses how to develop intelligence utilizing key components of Industry 4.0 (Internet of Things, machine learning, and cloud manufacturing). This method was analyzed in a case study of a low-level intelligent vacuum gripper design...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 2B: Advanced Manufacturing, V02BT02A046, November 1–5, 2021
Paper No: IMECE2021-71966
... with a guideline to understand multimodal defects interrelation and fabrication of thin walls with minimal defects. Corresponding author. Email: farhad.imani@uconn.edu. Keywords: Additive Manufacturing, Machine Learning, Uncertainty Characterization, Multi-response Modeling, Design 1 Introduction One key driver...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 2B: Advanced Manufacturing, V02BT02A016, November 1–5, 2021
Paper No: IMECE2021-71546
.... A degree of smart integrated manufacturing is presented according to industry 4.0 trends. In this research a digital twin is explored incorporating some new manufacturing paradigms such as Industrial Internet of Things (IIoT), Cloud Manufacturing (CM) and Machine Learning (ML) in the creation of new...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 1: Acoustics, Vibration, and Phononics, V001T01A012, November 1–5, 2021
Paper No: IMECE2021-72746
... length. Furthermore, it is also concluded that for the medium filled with a relatively low viscous fluid such as air the longitudinal waves alone is able to estimate the biomarkers, which reduce significantly the computational efforts. acoustic waves biomimetic porous scaffold machine learning...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 2B: Advanced Manufacturing, V02BT02A002, November 1–5, 2021
Paper No: IMECE2021-68686
... manufacturing decision systems require complex infrastructures that make advanced feedback control possible. The motivation of this paper is exploring the paradigms such as Industrial Internet of Things (IIoT), Big Data collection, Cloud Manufacturing (CM), and Machine Learning (ML) to provide better...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 12: Mechanics of Solids, Structures, and Fluids; Micro- and Nano- Systems Engineering and Packaging, V012T12A011, November 1–5, 2021
Paper No: IMECE2021-70543
...Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-70543 APPLIED MACHINE LEARNING METHOD TO PREDICT CRACK PROPAGATION PATH IN POLYCRYSTALLINE GRAPHENE SHEET Mohan S. R. Elapolu , Md. Imrul Reza Shishir...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 12: Mechanics of Solids, Structures, and Fluids; Micro- and Nano- Systems Engineering and Packaging, V012T12A057, November 1–5, 2021
Paper No: IMECE2021-72222
...Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-72222 A NONLINEAR FINITE ELEMENT-BASED SUPERVISED MACHINE LEARNING APPROACH FOR EFFICIENTLY PREDICTING COLLAPSE RESISTANCE OF WIRELINE TOOL HOUSINGS...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters, V013T14A003, November 1–5, 2021
Paper No: IMECE2021-73153
... ultrasonic sensors and from noncontinuous nondestructive testing (NDT) based eddy current measurements is demonstrated. Keywords: Digital twin, Artificial intelligence, Machine learning, Relational database management systems, Structured Query Language (SQL), NoSQL ABBREVIATION: AI-ML: Artificial...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters, V013T14A038, November 1–5, 2021
Paper No: IMECE2021-69395
... complex CPS. Recently, Deep Learning (DL) and Machine Learning (ML) anomaly detection methods became more popular, and numerous practical applications have been presented in many industrial scenarios. Most of the modern DLbased anomaly detection methods use the prediction approach and LSTM architecture...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 8B: Energy, V08BT08A031, November 1–5, 2021
Paper No: IMECE2021-71174
...Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-71174 SOLAR DISTILLATION SYSTEMS ENRICHED WITH MACHINE LEARNING TECHNIQUES: A REVIEW 1Y S Prasanna, 2Sandip S Deshmukh Department of Mechanical...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 10: Fluids Engineering, V010T10A062, November 1–5, 2021
Paper No: IMECE2021-69933
... mesh-based Navier-Stoke equation estimation which is prominent in many CFD softwares. machine learning fluid mechanics generative models auto-encoders turbulence flow generation Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 11: Heat Transfer and Thermal Engineering, V011T11A078, November 1–5, 2021
Paper No: IMECE2021-69657
... models as submodels and augment them with existing physical models. For example, one can use a neural network submodel to represent the unknown chemical reaction pathways and exploit various scienti c machine learning methods to train the neural network models, such as neural ordinary differential...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 11: Heat Transfer and Thermal Engineering, V011T11A003, November 1–5, 2021
Paper No: IMECE2021-70639
... systems. Our nonlinear timevariant ROM is an extension of sparse identification of nonlinear dynamical systems, first proposed in 2016. Three machine learning methods are developed for automatically deriving a thermal network model from time series data. Link relationships between temperature nodes...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 7A: Dynamics, Vibration, and Control, V07AT07A050, November 1–5, 2021
Paper No: IMECE2021-69994
...Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition IMECE2021 November 1-5, 2021, Virtual, Online IMECE2021-69994 AN EMPIRICAL STUDY OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS ON BEARING FAULT DIAGNOSIS BENCHMARKS Behnoush Rezaeianjouybari Department...
Proceedings Papers

Proc. ASME. IMECE2021, Volume 8A: Energy, V08AT08A021, November 1–5, 2021
Paper No: IMECE2021-70253
... prediction steps is best, with a root mean square error of 0.114°C. It reaches high prediction accuracy and can be used to guide the SLST control and primary loop valve opening (PLVO) regulation of heating substations. data-driven model machine learning secondary loop supply temperature prediction...
Proceedings Papers

Proc. ASME. IMECE2020, Volume 12: Mechanics of Solids, Structures, and Fluids, V012T12A052, November 16–19, 2020
Paper No: IMECE2020-23515
.... In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit. nickel base superalloys γ′ precipitate microstructure artificial neural network (ANN) machine learning BRIDGING PRECIPITATE...
Proceedings Papers

Proc. ASME. IMECE2020, Volume 12: Mechanics of Solids, Structures, and Fluids, V012T12A047, November 16–19, 2020
Paper No: IMECE2020-24014
... by using state of the art Machine Learning techniques, and attempts to answer several scientific questions: (i) Which ML algorithm is capable and is more efficient to learn such a complex and nonlinear problem? (ii) Is it possible to artificially reproduce structural brace seismic behavior that can...
Proceedings Papers

Proc. ASME. IMECE2020, Volume 8: Energy, V008T08A021, November 16–19, 2020
Paper No: IMECE2020-23949
... the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods...
Proceedings Papers

Proc. ASME. IMECE2020, Volume 13: Micro- and Nano-Systems Engineering and Packaging, V013T13A001, November 16–19, 2020
Paper No: IMECE2020-24408
... that this framework is capable of producing designs with desirable vibrational features, encouraging further research in this topic. generative adversarial networks microresonators microsystems deep neural networks machine learning APPLICATION OF DEEP NEURAL NETWORK IN THE DESIGN OF MICRORESONATORS...
Proceedings Papers

Proc. ASME. IMECE2020, Volume 13: Micro- and Nano-Systems Engineering and Packaging, V013T13A015, November 16–19, 2020
Paper No: IMECE2020-24269
... of the particle in wall detection, so new techniques that do not follow the assumptions will need to be investigated. More work will be completed to implement machine learning or deep learning to assist in the development of DLD devices. Keywords: deep learning, deterministic lateral displacement, high throughput...