☆ 4.5 Article

Implementation of a probabilistic machine learning strategy for failure predictions of adhesively bonded joints using cohesive zone modeling

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Mechanics

Development of aviation industry-oriented methodology for failure predictions of brittle bonded joints using probabilistic machine learning

Yuval Freed et al.

Summary: The bonding assembly concept of cured aerospace composite parts is considered efficient in terms of design simplification, weight reduction, and cost reduction. However, low bond-line strengths, known as kissing bonds, can still occur even with strict quality assurance. This study proposes an approach for determining the residual strength of bonded joints and analyzes predictions using a machine learning strategy. The accuracy of the optimal failure parameters is evaluated, and the parameters are statistically adjusted to consider prediction variability.

COMPOSITE STRUCTURES (2022)

添加到收藏夹

Article Mechanics

Machine learning assisted characterisation and simulation of compressive damage in composite laminates

Johannes Reiner et al.

Summary: The study proposed a data-rich framework to characterize the macroscopic strain-softening response of laminated composites under compressive loading using machine learning models. By simulating compression tests with an efficient FE model and training two ML methods, the study successfully demonstrated the effectiveness of using ML to reduce experimental efforts for damage characterization in composites subjected to compressive loads.

COMPOSITE STRUCTURES (2021)

添加到收藏夹

Article Automation & Control Systems

A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

Navid Zobeiry et al.

Summary: A physics-informed neural network is developed to solve conductive heat transfer PDEs with convective boundary conditions, improving the speed and accuracy of thermal analysis in manufacturing and engineering applications. By using physics-informed activation functions, heat transfer beyond training zone can be accurately predicted, making it a useful tool for real-time evaluation of thermal responses in a wide range of scenarios.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming

Zewen Gu et al.

Summary: This study presents the development of black-box and grey-box machine learning models for accurately predicting the failure load of adhesive joints, validates the accuracy of the models through experiments, and explores the impact of design variables on the mechanical performance of the joints.

COMPOSITES PART B-ENGINEERING (2021)

添加到收藏夹

Article Mechanics

Predicting the effects of microstructure on matrix crack initiation in fiber reinforced ceramic matrix composites via machine learning

Dipen K. Patel et al.

COMPOSITE STRUCTURES (2020)

添加到收藏夹

Article Mechanics

Prediction of failure behavior of adhesively bonded CFRP scarf joints using a cohesive zone model

Ligang Sun et al.

ENGINEERING FRACTURE MECHANICS (2020)

添加到收藏夹

Article Mechanics

Theory -guided machine learning for damage characterization of composites

Navid Zobeiry et al.

COMPOSITE STRUCTURES (2020)

添加到收藏夹

Article Materials Science, Composites

Damage Detection in Composites By Artificial Neural Networks Trained By Usingin SituDistributed Strains

America Califano et al.

APPLIED COMPOSITE MATERIALS (2020)

添加到收藏夹

Article Engineering, Mechanical

An experimental study of the interfacial fracture behavior of Titanium/CFRP adhesive joints under mode I and mode II fatigue

Lucas Adamos et al.

INTERNATIONAL JOURNAL OF FATIGUE (2020)

添加到收藏夹

Article Mechanics

Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection

Christopher Sacco et al.

COMPOSITE STRUCTURES (2020)

添加到收藏夹

Article Mechanics

Strength and cohesive behavior of thermoset polymers at the microscale: A size-effect study

Yao Qiao et al.

ENGINEERING FRACTURE MECHANICS (2019)

添加到收藏夹

Article Materials Science, Composites

Failure load prediction for fiber-reinforced composites based on acoustic emission

Markus G. R. Sause et al.

COMPOSITES SCIENCE AND TECHNOLOGY (2018)

添加到收藏夹

Article Materials Science, Multidisciplinary

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Zijiang Yang et al.

COMPUTATIONAL MATERIALS SCIENCE (2018)

添加到收藏夹

Article Engineering, Chemical

Adhesive thickness influence on the shear fracture toughness measurements of adhesive joints

J. C. P. Figueiredo et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2018)

添加到收藏夹

Article Engineering, Chemical

Development of design allowables for the design of composite bonded double-lap joints in aerospace applications

M. A. Khan et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2018)

添加到收藏夹

Article Engineering, Industrial

Predicting shim gaps in aircraft assembly with machine sparse sensing

Krithika Manohar et al.

JOURNAL OF MANUFACTURING SYSTEMS (2018)

添加到收藏夹

Article Chemistry, Multidisciplinary

Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

Grace X. Gu et al.

MATERIALS HORIZONS (2018)

添加到收藏夹

Article Engineering, Mechanical

De novo composite design based on machine learning algorithm

Grace X. Gu et al.

EXTREME MECHANICS LETTERS (2018)

添加到收藏夹

Article Engineering, Multidisciplinary

Mode-I, mode-II and mixed-mode I plus II fracture behavior of composite bonded joints: Experimental characterization and numerical simulation

I. S. Floros et al.

COMPOSITES PART B-ENGINEERING (2015)

添加到收藏夹

Article Engineering, Chemical

Influence of surface preparation methods on moisture-related performance of structural hardwood-adhesive bonds

Markus Knorz et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2015)

添加到收藏夹

Article Mechanics

Cohesive zone models and impact damage predictions for composite structures

S. Abrate et al.

MECCANICA (2015)

添加到收藏夹

Article Engineering, Chemical

Crack propagation analysis in composite bonded joints under mixed-mode (I+II) static and fatigue loading: experimental investigation and phenomenological modelling

P. A. Carraro et al.

JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY (2013)

添加到收藏夹

Article Engineering, Chemical

Investigation of structural bond lines in wind turbine blades by sub-component tests

Florian Sayer et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2012)

添加到收藏夹

Review Mechanics

Cohesive Zone Models: A Critical Review of Traction-Separation Relationships Across Fracture Surfaces

Kyoungsoo Park et al.

APPLIED MECHANICS REVIEWS (2011)

添加到收藏夹

Article Engineering, Chemical

Influence of adhesive bond line thickness on joint strength

P. Davies et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2009)

添加到收藏夹

Article Materials Science, Multidisciplinary

A unified potential-based cohesive model of mixed-mode fracture

Kyoungsoo Park et al.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2009)

添加到收藏夹

Article Mechanics

A new cohesive zone model for mixed mode interface fracture in bimaterials

Yuval Freed et al.

ENGINEERING FRACTURE MECHANICS (2008)

添加到收藏夹

Article Mechanics

An. improved description of the exponential Xu and Needleman cohesive zone law for mixed-mode decohesion

MJ van den Bosch et al.

ENGINEERING FRACTURE MECHANICS (2006)

添加到收藏夹

Article Engineering, Chemical

Mixed mode failure criteria for a glass/epoxy composite and an adhesively bonded composite/composite joint

F Ducept et al.

INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES (2000)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.