4.7 Article

Discovering the mechanics of artificial and real meat

Related references

Note: Only part of the references are listed.
Article Engineering, Multidisciplinary

Automated model discovery for skin: Discovering the best model, data, and experiment

Kevin Linka et al.

Summary: Choosing the best constitutive model and parameters in continuum mechanics has traditionally relied on user experience and preference. This paper proposes a new method that autonomously discovers the best model and parameters to explain experimental data using a neural network. The method is robust and satisfies physical constraints, and has the potential to revolutionize the field of constitutive modeling. Evaluation: 8 points

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2023)

Article Engineering, Biomedical

Automated model discovery for human brain using Constitutive Artificial Neural Networks

Kevin Linka et al.

Summary: The brain is an extremely soft and vulnerable organ, and understanding its physics is crucial but challenging. This study proposes a new strategy that combines thermodynamics and machine learning to build an artificial neural network for automated model discovery. The results demonstrate the potential of this method to shift from user-defined model selection to automated model discovery.

ACTA BIOMATERIALIA (2023)

Article Engineering, Multidisciplinary

A new family of Constitutive Artificial Neural Networks towards automated model discovery

Kevin Linka et al.

Summary: For over a century, scientists from various fields have proposed different models to characterize the behavior of materials under mechanical loading. However, classical neural networks fail to consider previous research in constitutive modeling and have limitations in predicting behavior beyond the training data. In this study, a new family of Constitutive Artificial Neural Networks (CANN) is developed to overcome these limitations and satisfy physical constraints. CANN shows promise in automating model discovery and has the potential to revolutionize constitutive modeling.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2023)

Article Engineering, Biomedical

Automated model discovery for muscle using constitutive recurrent neural networks

Lucy M. Wang et al.

Summary: The stiffness of soft biological tissues depends on both the applied deformation and the deformation rate. A new trend suggests using machine-learning to simultaneously discover the best model and parameters to explain the data. By combining feed-forward and recurrent neural networks, a novel architecture is proposed to discover the time-dependent behavior of soft tissues, which outperforms other models in terms of prediction accuracy.

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS (2023)

Editorial Material Food Science & Technology

Health and sustainability of everyday food

Chloe Clifford Astbury

Summary: By analyzing recipes, we can gain insights into the effects of food preparation and consumption in various geographical contexts.

NATURE FOOD (2023)

Article Materials Science, Multidisciplinary

Polyconvex anisotropic hyperelasticity with neural networks

Dominik K. Klein et al.

Summary: In this paper, two machine learning based constitutive models for finite deformations are proposed. They are hyperelastic, anisotropic, fulfill the polyconvexity condition, and have excellent predictive and generalization capabilities. The models are calibrated with challenging simulation data and show good results, demonstrating their reliability and applicability.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2022)

Article Mechanics

Intrinsic mechanical properties of food in relation to texture parameters

N. Jonkers et al.

Summary: This study introduces the texture profile analysis test for determining food texture properties and discusses the relationships between texture parameters, test conditions, and material mechanical properties, as well as the effects of different material properties on texture parameters.

MECHANICS OF TIME-DEPENDENT MATERIALS (2022)

Article Engineering, Multidisciplinary

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

Jan N. Fuhg et al.

Summary: This paper presents a physics-informed and data-driven constitutive modeling approach for isotropic and anisotropic hyperelastic materials at finite strain. The approach trains surrogate models that respect physical principles, and demonstrates surprising accuracy even beyond the limits of the training domain. The paper also introduces a sampling technique that generates space-filling points in the invariant space, improving the efficiency and reliability of the constitutive models.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

Article Engineering, Multidisciplinary

A mechanics-informed artificial neural network approach in data-driven constitutive modeling

Faisal As'ad et al.

Summary: This paper proposes a mechanics-informed artificial neural network approach for learning constitutive laws of complex, nonlinear, elastic materials. The approach captures highly nonlinear strain-stress mappings while preserving fundamental principles of solid mechanics. It enforces physical constraints and demonstrates potential for multi-scale applications.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2022)

Article Mechanics

The Valanis-Landel strain energy function Elasticity of incompressible and compressible rubber-like materials

Kirk C. Valanis

Summary: The paper revisits the original article by Valanis and Landel on the strain energy density function, and presents new extensions and findings including the applicability to compressible materials, the analytical form of the Cauchy-Green stress tensor, determination of the constitutive equation from a single experiment, and comparison with experimental data.

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2022)

Article Engineering, Chemical

Anisotropic mechanical properties of Selective Laser Sintered starch-based food

N. Jonkers et al.

Summary: The layer-by-layer structure of Selective Laser Sintered food products leads to anisotropic mechanical properties that can be customized through adjusting laser sintering parameters. Increasing the area energy density results in significant changes in stiffness and fracture stress in the build direction, while ductility decreases. Furthermore, in-situ compression tests revealed heterogeneous crack propagation in the material.

JOURNAL OF FOOD ENGINEERING (2022)

Article Engineering, Multidisciplinary

Data-driven tissue mechanics with polyconvex neural ordinary differential equations

Vahidullah Tac et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

Article Materials Science, Multidisciplinary

Learning hyperelastic anisotropy from data via a tensor basis neural network

J. N. Fuhg et al.

Summary: In this study, a tensor-basis neural network model is proposed to accurately predict the mechanical response of materials, especially those with microstructure and anisotropy. Classical representation theory, novel neural network layers, and L1 regularization are utilized to construct the model.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2022)

Article Engineering, Multidisciplinary

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method

Shahed Rezaei et al.

Summary: Physics Informed Neural Networks (PINNs) can find solutions to boundary value problems by minimizing a loss function that incorporates governing equations, initial and boundary conditions. This study proposes an improved method that uses the spatial gradient of the primary variable as an output and applies the strong form of the equation as a physical constraint. By comparing with finite element methods, it is shown that this approach has advantages, and the potential of combining PINN with physical FE simulations for designing composite materials is discussed.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

Article Engineering, Multidisciplinary

Model-free data-driven computational mechanics enhanced by tensor voting

Robert Eggersmann et al.

Summary: The data-driven computing paradigm is extended by incorporating locally linear tangent spaces and tensor voting method, improving the learning of the underlying structure of a data set. The resulting method is a straightforward plug-in approach for distance-minimizing and entropy-maximizing data-driven schemes, with efficient implementation and demonstrated convergence properties for ideal and noisy data sets.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning

Kevin Linka et al.

Summary: Constitutive artificial neural networks (CANNs) are a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. By incorporating information from stress-strain data, materials theory, and additional information, CANNs can efficiently learn the constitutive behavior of complex materials with minimal training data. The ability to predict properties of new materials without existing stress-strain data makes CANNs potentially useful for in-silico material design in the future.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

Article Materials Science, Multidisciplinary

Thermodynamics-based Artificial Neural Networks for constitutive modeling

Filippo Masi et al.

Summary: TANNs is a physics-based neural network method for constitutive modeling, where thermodynamic principles are encoded into the network architecture using automatic differentiation. Its advantages include efficiency, robustness, accuracy in predictions, and maintaining thermodynamic consistency.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)

Article Engineering, Multidisciplinary

Unsupervised discovery of interpretable hyperelastic constitutive laws

Moritz Flaschel et al.

Summary: The proposed method allows for unsupervised discovery of isotropic hyperelastic constitutive laws, delivering interpretable models through sparse regression and automatically determining penalty parameters in the regularization term.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Multidisciplinary Sciences

Predictive constitutive modelling of arteries by deep learning

Gerhard A. Holzapfel et al.

Summary: The constitutive modeling of soft biological tissues has attracted attention in the past 20 years. A novel hybrid modeling framework combining advanced theoretical concepts with deep learning has been introduced to predict the mechanical properties of tissues from microstructural information. This framework, trained with data from only 27 tissue samples, was able to accurately predict the stress-stretch curves of tissue samples, indicating the transformative potential of deep learning in modeling soft biological tissues.

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2021)

Review Food Science & Technology

A review of research on plant-based meat alternatives: Driving forces, history, manufacturing, and consumer attitudes

Jiang He et al.

COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY (2020)

Review Food Science & Technology

Scientific, sustainability and regulatory challenges of cultured meat

Mark J. Post et al.

NATURE FOOD (2020)

Review Veterinary Sciences

Cooking of meat: effect on texture, cooking loss and microbiological quality - a review

Frantisek Jezek et al.

ACTA VETERINARIA BRNO (2019)

Article Engineering, Biomedical

Biomechanical characterization of human dura mater

Dries De Kegel et al.

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS (2018)

Review Food Science & Technology

Structuring processes for meat analogues

Birgit L. Dekkers et al.

TRENDS IN FOOD SCIENCE & TECHNOLOGY (2018)

Review Food Science & Technology

Consumer perception and behaviour regarding sustainable protein consumption: A systematic review

Christina Hartmann et al.

TRENDS IN FOOD SCIENCE & TECHNOLOGY (2017)

Article Engineering, Biomedical

Mechanical characterization of human brain tissue

S. Budday et al.

ACTA BIOMATERIALIA (2017)

Review Chemistry, Physical

Food structure: Roles of mechanical properties and oral processing in determining sensory texture of soft materials

Yvette Pascua et al.

CURRENT OPINION IN COLLOID & INTERFACE SCIENCE (2013)

Article Engineering, Biomedical

Arterial clamping: Finite element simulation and in vivo validation

Nele Famaey et al.

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS (2012)

Article Mechanics

A view on anisotropic finite hyper-elasticity

A Menzel et al.

EUROPEAN JOURNAL OF MECHANICS A-SOLIDS (2003)

Article Mechanics

Parameter estimation of hyperelasticity relations of generalized polynomial-type with constraint conditions

S Hartmann

INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2001)