The paper deals with an approximate method for calculating elastic-plastic stresses and strains on the surface of notched samples. The method is based on the Abdel-Karim-Ohno cyclic plasticity model. The plane stress condition is considered within the evaluation. The output of the approximation on several multiaxial axial-torsion load paths is compared to our own experimental results. Experiments were carried out on samples of two notch types manufactured from the 2124-T851 aluminum alloy. Strain distribution in the notch area was measured by digital image correlation. The comparison between computational solution and measured response shows that the new method allows for obtaining reasonably good approximation, even for relatively complicated multiaxial load cases.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876333PMC
http://dx.doi.org/10.3390/ma15041432DOI Listing

Publication Analysis

Top Keywords

approximate method
8
method calculating
8
calculating elastic-plastic
8
elastic-plastic stress
4
stress strain
4
strain notched
4
notched specimens
4
specimens paper
4
paper deals
4
deals approximate
4

Similar Publications

Investigating time-independent and time-dependent diffusion phenomena using steady-state diffusion MRI.

Sci Rep

January 2025

Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure.

View Article and Find Full Text PDF

Large-scale optimal design problems involving nonlinear differential equations are widely applied in modeling such as craft manufacturing, chemical engineering and energy engineering. Herein we propose a fast and flexible holomorphic embedding-based method to solve nonlinear differential equations quickly, and further apply it to handle the industrial application of reverse osmosis desalination. Without solving nonlinear differential equations at each discrete point by a traditional small-step iteration approach, the proposed method determines the solution through an approximation function or approximant within segmented computational domain independently.

View Article and Find Full Text PDF

Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting.

Sci Rep

January 2025

Department of Industrial Engineering/Graduate School of Data Science/Research Center for Electrical and Information Science, Seoul National University of Science and Technology, Seoul, South Korea.

Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets.

View Article and Find Full Text PDF

This study endeavors to develop a predefined-time adaptive neural network decentralized controller for large-scale interconnected nonlinear systems with input hysteresis. Within the framework of the backstepping technique, the proposed control scheme guarantees that the tracking error converges to a small bounded set within a predefined settling time. The upper limit of this convergence time is determined by a single adjustable control parameter.

View Article and Find Full Text PDF

In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spectral bias of fully connected deep neural networks in approximating oscillatory functions. The diffusion models are obtained from the spectral form of the error equation of the MscaleDNN, derived with a neural tangent kernel approach and gradient descent training and a sine activation function, assuming a vanishing learning rate and infinite network width and domain size.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!