AIMC Topic: Stress, Mechanical

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A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

Journal of the mechanical behavior of biomedical materials
The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications ...

Prediction Model of Stress Intensity Factor of Circumferential Through Crack in Elbow Based on Neural Network.

Computational intelligence and neuroscience
Using ANSYS software to establish the finite element model of crack bending tube, the SIF at the tip of the crack is calculated for the difference in the diameter of the pipe, the outer diameter of the elbow, and the bending angle of the bend pipe, a...

Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle.

Medical & biological engineering & computing
Hyperelastic constitutive laws have been commonly used to model the passive behavior of the human skeletal muscle. Despite many efforts, the use of accurate finite element formulations of hyperelastic constitutive laws is still time-consuming for a r...

Development of novel robotic platforms for mechanical stress induction, and their effects on plant morphology, elements, and metabolism.

Scientific reports
This research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical exper...

Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain).

Scientific reports
Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical...

Vital signal sensing and manipulation of a microscale organ with a multifunctional soft gripper.

Science robotics
Soft grippers that incorporate functional materials are important in the development of mechanically compliant and multifunctional interfaces for both sensing and stimulating soft objects and organisms. In particular, the capability for firm and deli...

Machine Learning Accelerated, High Throughput, Multi-Objective Optimization of Multiprincipal Element Alloys.

Small (Weinheim an der Bergstrasse, Germany)
Multiprincipal element alloys (MPEAs) have gained surging interest due to their exceptional properties unprecedented in traditional alloys. However, identifying an MPEA with desired properties from a huge compositional space via a cost-effective desi...

Predictive constitutive modelling of arteries by deep learning.

Journal of the Royal Society, Interface
The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural informa...

A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells.

PloS one
Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many p...

Inverse identification of hyperelastic constitutive parameters of skeletal muscles via optimization of AI techniques.

Computer methods in biomechanics and biomedical engineering
Studies on the deformation characteristics and stress distribution in loaded skeletal muscles are of increasing importance. Reliable prediction of hyperelastic material parameters requires an inverse process, which possesses challenges. This work pro...