A software framework for predicting the maize yield using modified multi-layer perceptron

S Ahmed - Sustainability, 2023 - mdpi.com
Predicting crop yields is one of agriculture's most challenging issues. It is crucial in making
national, provincial, and regional choices and estimates the government to meet the food …

Data driven of underground water level using artificial intelligence hybrid algorithms

M Rahimi, H Ebrahimi - Scientific Reports, 2023 - nature.com
As the population grows, industry and agriculture have also developed and water resources
require quantitative and qualitative management. Currently, the management of water …

Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing

D Akhare, T Luo, JX Wang - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
Various manufacturing technologies are being developed to improve the manufacturing of
composites owing to their low weight and high performance. The mechanical properties of …

Influence of different vulcanizing agents on structures and properties of sepiolite-filled natural rubber composites

N Hayeemasae, S Soontaranon… - Express Polymer …, 2023 - search.proquest.com
This study aimed to explore the best cross-link agent for preparing natural rubber (NR)
composites containing sepiolite as filler. Three types of vulcanizing agents, namely, sulfur …

Application of generalized regression neural network and Gaussian process regression for modelling hybrid micro-electric discharge machining: a comparative study

SK Singh, HS Mali, DR Unune, S Wojciechowski… - Processes, 2022 - mdpi.com
Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied
micromanufacturing processes. However, it has several limitations, such as a low cutting …

Regional application of generalized regression neural network in ionosphere spatio-temporal modeling and forecasting

SR Ghaffari-Razin, A Rastbood, N Hooshangi - GPS Solutions, 2023 - Springer
We propose using the generalized regression neural network (GRNN) method for spatio-
temporal modeling of ionosphere total electron content (TEC). The GRNN model uses radial …

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

D Akhare, Z Chen, R Gulotty, T Luo… - npj Computational …, 2024 - nature.com
Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in
producing carbon-carbon and carbon-silicon carbide composites. These materials are …

Real-time temperature control in rubber extrusion lines: a neural network approach

M Lukas, S Leineweber, B Reitz… - The International Journal of …, 2024 - Springer
In rubber extrusion, precise temperature control is critical due to the process's sensitivity to
fluctuating parameters like compound behavior and batch-specific material variations. Rapid …

[HTML][HTML] Sensitivity analysis: A tool for tailoring environmentally friendly materials

D Seidl, I Ružiak, ZK Jančíková, P Koštial - Expert Systems with …, 2022 - Elsevier
In this article, we examine the use of sensitivity analysis for the optimization of selected
physical properties in rubber compounds and determine objective criteria which allow for the …

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing

I Kopal, I Labaj, J Vršková, M Harničárová, J Valíček… - Polymers, 2023 - mdpi.com
Modelling the flow properties of rubber blends makes it possible to predict their rheological
behaviour during the processing and production of rubber-based products. As the nonlinear …