scholarly journals Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

2020 ◽  
Author(s):  
He Tan ◽  
Vladimir Tarasov ◽  
Vasileios Fourlakidis ◽  
Attila Dioszegi
Keyword(s):  
Experimental Data ◽  
Fuzzy Logic ◽  
Cast Iron ◽  
Materials Science ◽  
Fatigue Behavior ◽  
Relevant Literature ◽  
Data Driven ◽  
Fuzzy Logic Systems ◽  
Data Driven Approach ◽  
Data Driven Modeling

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

scholarly journals Data-driven modeling and forecasting of COVID-19 outbreak for public policy making

2020 ◽  
Author(s):  
Agus Hasan ◽  
Endah Putri ◽  
Hadi Susanto ◽  
Nuning Nuraini
Keyword(s):  
Public Policy ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Case Fatality ◽  
Data Driven ◽  
Infectious Individual ◽  
Transmission Index ◽  
Modeling And Forecasting ◽  
Data Driven Approach ◽  
Data Driven Modeling

This paper presents a data-driven approach for COVID-19 outbreak modeling and forecasting, which can be used by public policy and decision makers to control the outbreak through Non-Pharmaceutical Interventions (NPI). First, we apply an extended Kalman filter (EKF) to a discrete-time stochastic augmented compartmental model to estimate the time-varying effective reproduction number Rt. We use daily confirmed cases, active cases, recovered cases, deceased cases, Case-Fatality-Rate (CFR), and infectious time as inputs for the model. Furthermore, we define a Transmission Index (TI) as a ratio between the instantaneous and the maximum value of the effective reproduction number. The value of TI shows the disease transmission in a contact between a susceptible and an infectious individual due to current measures such as physical distancing and lock-down relative to a normal condition. Based on the value of TI, we forecast different scenarios to see the effect of relaxing and tightening public measures. Case studies in three countries are provided to show the practicability of our approach.

scholarly journals A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Balogh ◽  
John Gounley ◽  
Sayan Roychowdhury ◽  
Amanda Randles
Keyword(s):  
Experimental Data ◽  
Cancer Cells ◽  
Large Deformation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Membrane Model ◽  
Data Driven ◽  
Specific Cell ◽  
Model Based ◽  
Data Driven Approach

AbstractIn order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer’s cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

scholarly journals From mechanism-based to data-driven approaches in materials science

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Stefan Hiemer ◽  
Stefano Zapperi
Keyword(s):  
Artificial Intelligence ◽  
Materials Science ◽  
Recent Literature ◽  
Dislocation Dynamics ◽  
Data Driven ◽  
Governing Equations ◽  
Mechanics Of Materials ◽  
Data Driven Approach ◽  
Empirical Discovery ◽  
Open Issues

AbstractA time-honored approach in theoretical materials science revolves around the search for basic mechanisms that should incorporate key feature of the phenomenon under investigation. Recent years have witnessed an explosion across areas of science of a data-driven approach fueled by recent advances in machine learning. Here we provide a brief perspective on the strengths and weaknesses of mechanism based and data-driven approaches in the context of the mechanics of materials. We discuss recent literature on dislocation dynamics, atomistic plasticity in glasses focusing on the empirical discovery of governing equations through artificial intelligence. We conclude highlighting the main open issues and suggesting possible improvements and future trajectories in the fields.

scholarly journals A new reliability-based data-driven approach for noisy experimental data with physical constraints

2018 ◽  
Vol 328 ◽  
pp. 752-774 ◽  
Author(s):  
Jacobo Ayensa-Jiménez ◽  
Mohamed H. Doweidar ◽  
Jose A. Sanz-Herrera ◽  
Manuel Doblaré
Keyword(s):  
Experimental Data ◽  
Data Driven ◽  
Physical Constraints ◽  
Data Driven Approach

scholarly journals Data-Driven Model-Free Control of Torque-Applying System for a Mechanically Closed-Loop Test Rig Using Neural Networks

2020 ◽  
Vol 66 (5) ◽  
pp. 337-347
Author(s):  
Aida Parvaresh ◽  
Mohsen Mardani
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
High Performance ◽  
Data Driven ◽  
Test Rig ◽  
Operational Conditions ◽  
Model Free ◽  
Actuation System ◽  
Data Driven Approach ◽  
Learning Factors

This paper presents a data-driven approach that utilizes the gathered experimental data to model and control a test rig constructed for the high-powered gearboxes. For simulating a wide variety of operational conditions, the test rig should be capable of providing different speeds and torques; this is possible using a torque-applying system. For this purpose, Electro-Hydraulic Actuators (EHAs) are used. Since applying accurate torque is a crucial demand as it affects the performance evaluation of the gearboxes, precise modelling of the actuation system along with a high-performance controller are required. In order to eliminate the need to solve complex nonlinear equations of EHA that originate from friction, varying properties of flow and similar, a data-driven system based on neural networks is used for modelling. In this manner, the model of the system, which captures the whole dynamic of the system, can be obtained without any simplifying assumptions. The model is validated with experimental data, and the learning factors are set to zero to reduce the high computational costs. After that, another network of neurons is used as a controller. The performance of the proposed controller under normal conditions and in the presence of disturbances are investigated. The results show a good tracking of this controller for various reference inputs in different conditions with acceptable characteristics. Additionally, the obtained results are compared with a conventional proportional-integral-derivative (PID) controller results, and the superior features of the proposed scheme is concluded.

Surface Roughness Parameter and Modeling for Fatigue Behavior of Additive Manufactured Parts: A Non-destructive Data-driven Approach

2021 ◽  
pp. 102094
Author(s):  
Seungjong Lee ◽  
Behnam Rasoolian ◽  
Daniel F. Silva ◽  
Jonathan W. Pegues ◽  
Nima Shamsaei
Keyword(s):  
Surface Roughness ◽  
Fatigue Behavior ◽  
Roughness Parameter ◽  
Data Driven ◽  
Surface Roughness Parameter ◽  
Data Driven Approach ◽  
Non Destructive
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