Prediction Of Material Performance Research Articles

A FEM-based model for failure initiation in the microstructure of gray cast iron under uniaxial compression

PurposeThe purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.Design/methodology/approachThis paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.FindingsThe findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.Originality/valueThis study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

Exploration of chemical changes in bituminous mastics induced by aging: insights from FTIR spectroscopy, DSR measurements, and machine learning

ABSTRACT The aging phenomenon in bituminous mastics leads to distinct chemical changes that affect their mechanical and rheological properties. Experimental testing like Fourier Transform Infrared (FTIR) spectroscopy and Dynamic Shear Rheometer (DSR) often require advanced post-processing to reveal underlying patterns. This research employs advanced pattern recognition algorithms and chemometric analyses to investigate aging-induced chemical changes in bituminous mastics. The study seeks to establish a rational relationship between their chemical and rheological properties. FTIR data were analyzed using Partial Least Squares Regression-Linear Discriminant Analysis (PLSR-LDA) to derive normalised Variable Importance in Projection (VIP) scores. Post-processing of PLSR-LDA results, including Pearson correlation testing and classification through data mining algorithms, identified carbonyl groups, alcohols, and carboxylic acids as key indicators of aging. Significant mineral vibrations in the Si-O band and carbonate-related vibrations were also important for differentiating various mineral compositions in the mastics. The study demonstrated that combining PLSR with CatBoost regression analysis effectively predicts rheological behaviour based on chemical composition. These findings underscore the non-linear relationship between their chemical and rheological properties and provide a framework for predicting material performance based on chemical characteristics.

Innovative PEEK in Dentistry of Enhanced Adhesion and Sustainability through AI-Driven Surface Treatments.

This research investigates using Polyether ether ketone (PEEK) in dental prosthetics, focusing on enhancing the mechanical properties, adhesion capabilities, and environmental sustainability through AI-driven data analysis and advanced surface treatments. The objectives include improving PEEK's adhesion to dental types of cement, assessing its biocompatibility, and evaluating its environmental impact compared to traditional materials. The methodologies employed involve surface treatments such as plasma treatment and chemical etching, mechanical testing under ASTM standards, biocompatibility assessments, and lifecycle analysis. AI models predict and optimize mechanical properties based on extensive data. Significant findings indicate that surface-treated PEEK exhibits superior adhesion properties, maintaining robust mechanical integrity with no cytotoxic effects and supporting its use in direct contact with human tissues. Lifecycle analysis suggests PEEK offers a reduced environmental footprint due to lower energy-intensive production processes and recyclability. AI-driven analysis further enhances the material's performance prediction and optimization, ensuring better clinical outcomes. The study concludes that with improved surface treatments and AI optimization, PEEK is a promising alternative to conventional dental materials, combining enhanced performance with environmental sustainability, paving the way for broader acceptance in dental applications.

Simulation of Localized Stress Impact on Solidification Pattern during Plasma Cladding of WC Particles in Nickel-Based Alloys by Phase-Field Method

As materials science continues to advance, the correlation between microstructure and macroscopic properties has garnered growing interest for optimizing and predicting material performance under various operating conditions. The phase-field method has emerged as a crucial tool for investigating the interplay between microstructural characteristics and internal material properties. In this study, we propose a phase-field approach to couple two-phase growth with stress–strain elastic energy at the mesoscale, enabling the simulation of local stress effects on the solidified structure during the plasma cladding of WC particles and nickel-based alloys. This model offers a more precise prediction of microstructural evolution influenced by stress. Initially, the phase field of WC-Ni binary alloys was modeled, followed by simulations of actual local stress conditions and their impacts on WC particles and nickel-based alloys with ProCAST and finite element analysis software. The results indicate that increased stress reduces grain boundary migration, decelerates WC particle dissolution and diffusion, and diminishes the formation of reaction layers and Ostwald ripening. Furthermore, experimental validation corroborated that the model’s predictions were consistent with the observed microstructural evolution of WC particles and nickel-based alloy composites.

Leveraging experimental and computational tools for advancing carbon capture adsorbents research.

CO2 emissions have been steadily increasing and have been a major contributor for climate change compelling nations to take decisive action fast. The average global temperature could reach 1.5°C by 2035 which could cause a significant impact on the environment, if the emissions are left unchecked. Several strategies have been explored of which carbon capture is considered the most suitable for faster deployment. Among different carbon capture solutions, adsorption is considered both practical and sustainable for scale-up. But the development of adsorbents that can exhibit satisfactory performance is typically done through the experimental approach. This hit and trial method is costly and time consuming and often success is not guaranteed. Machine learning (ML) and other computational tools offer an alternate to this approach and is accessible to everyone. Often, the research towards materials focuses on maximizing its performance under simulated conditions. The aim of this study is to present a holistic view on progress in material research for carbon capture and the various tools available in this regard. Thus, in this review, we first present a context on the workflow for carbon capture material development before providing various machine learning and computational tools available to support researchers at each stage of the process. The most popular application of ML models is for predicting material performance and recommends that ML approaches can be utilized wherever possible so that experimentations can be focused on the later stages of the research and development.

Assessment of Cement Mortar Strength Mixed with Waste Copper Mine Tailings (CT) by Applying Gradient Boosting Regressor and Grid Search Optimization Machine Learning Approach

Growing environmental concerns and resource scarcity, the construction industry must investigate sustainable materials that reduce waste while improving building material properties. This study investigates the viability of using waste copper mine tailings (CT) as a partial replacement for river sand in cement mortar, assessing the impact on mechanical strength and developing a predictive model using a Gradient Boosting Regressor (GBR) and Grid Search Optimization (GSO). Copper tailings mix designs ranging from 0% to 50% replacement by volume (river sand) were developed, with a constant cement quantity while varying the proportions of sand and tailings. The experiments were carried out at Poornima University in Jaipur, using standard protocols to prepare specimens and measure their compressive strength for 0% CT to 50% CT as volume percent in the mixture. The results showed that the addition of copper tailings up to 20% (3CT2) had highly increased the mortar strength, while mix design 6CT3 showed the best strength at 30% CT. Beyond this threshold, strength declined, thus indicating an optimal replacement level. In the final step of the research, the GBR-GSO-based machine learning approach was employed for developing the predictive model for compressive strength in mortar with different contents of copper tailing for mix types 3CT and 6CT. The predictions obtained by the developed model were in very good agreement with empirical data, thus supporting the potential of machine learning to predict material performance for guiding the use of unconventional additives in construction. It could be said that this work not only represents the material properties of copper-tailing-infused mortar but also showcases state-of-the-art machine learning techniques in the building materials science domain while opening new paths for more innovative and sustainable building practices.

Ac/dc conductivity and ML-based evaluation of electric characteristics of methylene blue solution

Utilizing a precision LCR meter, the complex permittivity ε*(f) of methylene blue (MB) solutions in distilled water (DW) was determined at various temperatures and concentrations in the lower frequency range from 20 Hz to 2 MHz. The complex ac conductivity σ*(f) of the liquid samples was calculated using ε*(f). DC conductivity, σdc of the samples was also calculated. The effect of changes in temperature and concentration on the σ*(f) and ε*(f) of solutions are examined. A solution of MB in DW, ranging from diluted to concentrated, greatly enhances the electrical conductivity σdc. The empirical Casteel-Amis (CA) formula was employed to fit the experimental σdc values at different concentrations. Accurate measurement of complex permittivity of liquid solutions over wide range of concentration at broad range of frequencies and temperature is laborious and uneconomical. Therefore, we performed the complex permittivity measurement of the aqueous solution of MB over a limited concentration and temperature range over 201 frequency point. Then we used this experimental data in CatBoost regression model to predict the dielectric properties at intermediate frequencies and various concentrations. This model was employed to assess the performance of material characteristics using R-squared score. The study demonstrated that the CatBoost regression model can predict material performance at intermediate frequencies and concentrations while decreasing measurement resource needs by 60 %.

Prediction of compressive strength of FRP-confined concrete using machine learning: A novel synthetic data driven framework

FRP-confined concrete is an effective approach to enhance mechanical properties of concrete columns. Hence, accurate prediction of the compressive strength of FRP-confined concrete has the significant importance. Machine learning is a technology for achieving high-precision prediction of material performance. However, constrained by the lack of abundant experimental data in engineering, the reliability and robustness of models are often questionable. To address the issue, a novel synthetic data driven framework was developed in this study. The latest and most extensive experimental dataset was collected firstly. Subsequently, the state-of-the-art conditional tabular generative adversarial network (CTGAN) algorithm was employed to construct the model for generating FRP-confined concrete data for the first time. Finally, by adopting the “Train on Real, Test on Synthesized” and " Train on Real, Test on Real” approaches, the models for predicting FRP-confined concrete strength were developed. The results indicate that the 3508 data generated by the developed CTGAN model exhibit high quality. The models exhibit the high prediction accuracy, strong generalization capability and robustness, achieving a coefficient of determination of 0.972, which significantly surpasses the prediction accuracy of existing models. Therefore, the model for predicting the compressive strength of FRP-confined concrete developed in this study can be utilized to guide the development and structural design of FRP-confined concrete, and it can also serve as an alternative to empirical models. Furthermore, the novel synthetic data driven framework provides a powerful solution for addressing the issue of the poor reliability and robustness of models resulting from the lack of abundant data in engineering problems.

Multiscale Investigation of Switchable Metallicity in Tunable Conductance Devices

The project “Multiscale Investigation Of Switchable Metallicity In Tunable Conductance Devices” en- compasses a comprehensive web application designed to revolutionize the way businesses handle MnS2 components, such as pressure switches, variable resistors, memory devices, batteries, and supercapacitors, through a series of interconnected modules. At its core, this application streamlines client interactions, optimizes MnS2 material processing, and ensures seamless application integration, all while leveraging advanced porosity analysis techniques. A significant enhancement in the Porosity Analysis module is the incorporation of the Decision Tree Regressor, a machine learning algorithm, to accurately evaluate the conductivity of materials. This addition not only boosts the precision of porosity and conductivity assessments but also empowers the system to predict material performance with higher accuracy, thereby enabling more tailored solutions for client requirements.

Machine Learning Analysis of Low-Frequency Impedance Spectra of Binary Mixtures of Polar and Non Polar Liquids

The complex dielectric permittivities were determined using a precision LCR meter of the binary mixtures of a polar liquid (methyl ethyl ketone, or MEK) and a non-polar liquid (dimethyl silicone fluid, or DMSF) at 303.15 K temperature. After determining the complex impedance (Z* (ω)) using the complex permittivity portion (ɛ* (ω)) of polar and nonpolar liquids, the complex impedance data (Z* (ω)) was fitted to the Nyquist Plot. Our research described here investigated the impact of ionic impurities in the pure MEK, DMSF and in their mixed state. The established parameters provided information on the impact of concentration changes on the electrical characteristics of the binary mixtures. Our research delved into utilizing machine learning (ML) techniques, such as random forest (RF), LightGBM, and XGBoost regressors, to improve the material design and prediction modeling. The main objective was to assess the efficacy of various regression techniques in evaluating the material characteristics performance. Experiments spanning from 30% to 50% of the data set were carried out, with performance metrics, like, R2 score and MAE, being utilized. Notably, the RF regressor demonstrated outstanding performance in these evaluations with an R2 score of 0.9999. The simulation results indicated that the ML-based techniques offer resource and time savings, serving as effective tools for predicting material performance at intermediate frequencies.

Soft computing techniques for analysing the mechanical properties of egg shell powder-based concrete

The construction industry is increasingly focused on sustainability to reduce environmental impact. Researchers are actively exploring alternative materials to replace clinker-based binders. This study specifically investigates the use of eggshell powder (ESP) as a sustainable substitute in construction. Portland slag cement (PSC) is partially replaced by ESP in concrete production for this purpose. To assess the effectiveness of ESP in enhancing binder properties, the study analyses experimental data for compressive and flexural strength. Artificial Neural Network (ANN) modelling is employed for this analysis to predict material performance. The model undergoes training and testing using input data to ensure accuracy and reliability. The success of the study is demonstrated by high R2 values, with 0,9915 for compressive strength and 0,9921 for flexural strength, indicating that the ANN model closely matches actual material performance. Additionally, error analysis confirms the model's remarkable accuracy in predicting real-world results. Furthermore, the research highlights the exceptional potential of the developed ANN model, which can effectively predict the mechanical properties of construction materials containing ESP.

Development of new materials for electrothermal metals using data driven and machine learning.

After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this research, we employed four machine learning algorithms, including linear regression, ridge regression, support vector regression, and backpropagation neural networks, to develop predictive models for the electrical performance data of titanium alloys. Our focus was on two key objectives: resistivity and the temperature coefficient of resistance (TCR). Subsequently, leveraging the results of feature selection, we conducted an analysis to discern the impact of alloying elements on these two electrical properties.The prediction results indicate that for the resistivity data prediction task, the radial basis function kernel-based support vector machine model performs the best, with a correlation coefficient above 0.995 and a percentage error within 2%, demonstrating high predictive capability. For the TCR data prediction task, the best-performing model is a backpropagation neural network with two hidden layers, also with a correlation coefficient above 0.995 and a percentage error within 3%, demonstrating good generalization ability. The feature selection results using random forest and Xgboost indicate that Al and Zr have a significant positive effect on resistivity, while Al, Zr, and V have a significant negative effect on TCR. The conclusion of the composition optimization design suggests that to achieve both high resistivity and TCR, it is recommended to set the Al content in the range of 1.5% to 2% and the Zr content in the range of 2.5% to 3%.

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks (CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of scanning electron microscopy (SEM) images and compressive strength of polymer explosive materials. Mean absolute percentage error (MAPE) of our model is 1.3% and the goodness of fit (R2 ) is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

Prediction and optimization of civil engineering material properties based on artificial intelligence

In the modern research field of predicting and optimizing the performance of civil engineering materials, the incorporation of artificial intelligence (AI) has provided a new perspective. The research is based on a thorough examination of the central role of AI technology and the application of different mathematical models in the task of material performance prediction and optimization, summarizing the current state of AI technology application in the field of civil engineering material performance prediction. Against this backdrop, the research further proposes a brand-new AI-based civil engineering material performance prediction method. This study, aiming to realize the optimization of material performance prediction tasks, utilized cutting-edge machine learning techniques and mathematical models, proposing this new AI prediction framework. Detailed discussions on design, implementation, and evaluation were conducted in the forecast framework, which includes a large number of tables, mathematical functions, and references. Through rigorous study and implementation, the prediction framework exhibited admirable performance, demonstrating extensive applicability for civil engineering material performance prediction and optimization. In conclusion, this new AI-based civil engineering material performance prediction method has demonstrated encouraging results. This study provides a new horizon, illustrating the tremendous potential and value of AI for predicting the performance of civil engineering materials, thereby improving efficiency and effectiveness in the civil engineering field. Overall, this new AI prediction method provides a fresh pathway for future research, making an essential contribution to anticipating and optimizing in civil engineering field.

Cross-scale static/dynamic shear damage evolution mechanism of 2D C/SiC ceramic matrix composite with transient hysteresis effect

The intricate woven structure of two-dimensional woven carbon fiber-reinforced silicon carbide (2D C/SiC) poses challenges to the investigation of damage failure mechanisms and the prediction of material performance, thereby limiting its further application in aerospace engineering. In this work, static/dynamic short beam shear tests with different densities of 2D C/SiC were carried out. It was observed that high-density specimens with fewer microporous defects exhibited better strength characteristics than those with lower density. Specifically, the shear strength of 2D C/SiC with a density of 2.07 g/cm³ increased by 18.6% compared to that of the specimen with a density of 1.94 g/cm³. The transient hysteresis strengthening effect of materials at high strain rates was discovered through cross scale research methods. It was found that deformation coordination ability of 2D C/SiC under dynamic loading was weaker than that under quasi-static conditions. Moreover, the deformation hysteresis in temporal and spatial distributions induced significant fiber plucking, further accelerating dislocation movement within the internal lattice and thereby enhancing strength of the material. The failure path of materials often follows the direction of pore defects, and stress concentration leads to cracking behavior of fiber bundles. Furthermore, microcracks within the matrix, caused by residual stress, resulted in localized regions of reduced strength. This induced the matrix cracking failure with further crack expansion within the material structure under external loads. To provide technical references for practical engineering applications and design, a realistic and effective finite element prediction model was developed for 2D C/SiC composites based on their structural characteristics.

Effect of hydrogen–helium interaction on their segregation and desorption at the W/HfC interfaces by first-principles calculations

Understanding the effect of the interaction between hydrogen (H) and helium (He) in tungsten (W)-based plasma-facing materials is essential for predicting materials performance in the context of fusion environments. In this work, the effect of H-He interaction on their segregation, accumulation and desorption behaviors at typical W/HfC interfaces were systematically investigated by first-principles calculations. Both H and He atoms tend to segregate at the W/HfC interfaces, where H atoms are discretely distributed and He atoms aggregate in a closely packed arrangement between W(110) planes. At the interface with a vacancy, the pre-existing He atoms can mitigate the accumulation of H atoms and promote their diffusion, which accounts for the reduction of H retention in W-carbide composites as observed in experiments. However, the pre-captured H has a minimal impact on the trapping of He. Furthermore, the maximum desorption temperatures of H and He from the interface are evaluated, ranging from 400 to 550 K and 700 to 1600 K, respectively. The pre-existing He atoms at the interfacial W vacancy also lower the desorption temperature of H. This work provides deep insight at the atomic scale towards a better understanding of the effect of H-He interaction on their retention and desorption in plasma facing-materials in the fusion environment.

Multi-physics Approach to Predict Fatigue Behavior of High Strength Aluminum Alloy Repaired via Additive Friction Stir Deposition

A smooth particle hydrodynamic (SPH) simulation of an additive friction stir deposition (AFSD) repair was used to inform a multi-physics approach to predict the fatigue life of a high strength aluminum alloy. The AFSD process is a solid-state layer-by-layer additive manufacturing approach in which a hollow tool containing feedstock is used to deposit material. While an understanding of the evolving microstructures is necessary to predict material performance, the elevated temperatures and strain rates associated with severe plastic deformation processes (SPDP) make accurate collection of experimental data within AFSD difficult. Without the ability to experimentally determine material history within the AFSD process, an SPH model was employed to predict the thermomechanical history. The SPH simulation of an AFSD repair was used to inform several microstructural models to predict material history during and after processing with AFSD and a post-processing heat treatment. These microstructure models are then used to inform a mechanistic microstructure and performance model to predict the fatigue life of an AFSD repair in AA7075.

A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling

ABSTRACT Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909.

Quantitative study on growth and porosity of C-S-H structures: Experiments and simulations

Coupling of microstructural and physico-chemical modelling of cement hydration is a key step towards prediction of material performance. In this paper, a physico-chemical model based on surface reactions and ion transports is proposed to capture the development of calcium-silicate-hydrate (C-S-H) not only at an early age but also at later ages of hydration (e.g, up to 28 days). Furthermore, we introduce a random sampling method to quantify pore size distribution and phase fractions in the 3D simulation, which are available for the comparison with experimental data. The latest experimental techniques were used to quantify the development of the C-S-H as well as its pore sizes, e.g. SEM, 1H NMR , XRD during hydration. By combining observations from experiments and simulations, we can explain the densification of C-S-H during the hydration.

Electrochemical Behavior and Electrical Stability of a Needle-Shaped Zinc Metal-Organic Framework Modified Graphite Electrode

In this paper, a novel needle-shaped Zinc organic framework was formed on the surface of a functionalized graphite electrode (Zn-MOF/CE) to create a methodical alternative electrode. The characterization of the Zn-MOF modifier was investigated via XRD, FT-IR, FESEM-EDS mapping, and N2 sorption. The redox behavior of the modified electrode was examined, showing high stability and conductivity under a diffusion-controlled process as a desirable working electrode. Also, the redox process of hydroquinone, as an organic molecule model, at the surface of the modified electrode was investigated and its operation was optimized and modeled by the central composite design (CCD) method. Based on the obtained results and CCD model, the process exhibited the best performance in scan rate 560 mV/s, pH= 9.9. Moreover, the frequency domain using bode plots, and two identical electrical models were evaluated by CCD results that could be employed in stability and electrode material performance prediction.