Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Experimental study on temperature stress calculation and temperature control optimization of concrete based on early age parameters

Temperature control curve is the key to achieving temperature control and crack prevention of high concrete dam during construction,and its rationality depends on the accurate measurement of temperature stress.With the simulation testing machine for the temperature stress,in the present study,we carried out the deformation process tests of concrete under three temperature curves:convex,straight and concave.Besides,we not only measured the early-age elastic modulus,creep parameters and stress process,but also proposed the preferred type.The results show that at early age,higher temperature always leads to greater elastic modulus and smaller creep.However,the traditional indoor experiments have underestimated the elastic modulus and creep development at early age,which makes the calculated value of temperature stress too small,thus increasing the cracking risk.In this study,the stress values of the three curves calculated based on the strain and early-age parameters are in good agreement with the temperature stress measured by the temperature stress testing machine,which verifies the method accuracy.When the temperature changes along the concave curve,the law of stress development is in consistent with that of strength.Under this condition,the stress fluctuation is small and the crack prevention safety of the concave type is higher,so the concave type is better.The test results provide a reliable basis and support for temperature control curve design and optimization of concrete dams.

Clarification of abrasive jet precision finishing with wheel as restraint mechanisms and experimental verification

According to the critical size ratio for the characteristic particle size to film thickness between grinding wheel and work, the machining mechanisms in abrasive jet precision finishing with grinding wheel as restraint can be categorized into four states, namely, two-body lapping, three-body polishing, abrasive jet machining and fluid hydrodynamic shear stress machining. The critical transition condition of two-body lapping to three-body polishing was analyzed. The single abrasive material removal models of two-body lapping, three-body polishing, abrasive jet finishing and fluid hydrodynamic shear stress machining were proposed. Experiments were performed in the refited plane grinding machine for theoretical modes verification. It was found that experimental results agreed with academic modes and the modes validity was verified.