Prediction Of Tensile Strength Research Articles

Online tensile strength prediction considering geometric and physical characteristics during FSW 2219-T8 aluminum alloy plate

Tensile strength of joints of friction stir welded (FSW) thick 2219-T8 aluminum alloy plates, is affected by multiple geometric and physical characteristics, such as axial force, welding temperature, gap, and mismatch of the butt face, which makes it difficult to predict the tensile strength with high precision on line. At present, simple welding process prediction based on temperature or force is difficult to achieve accurate control of complex welding process. In this paper, a multi-information fused one-dimensional Convolutional Neural Network (1DCNN), combining geometric and physical characteristics of welding process, was proposed to predict the tensile strength of joints. Firstly, the experiments of FSW thick 2219-T8 aluminum alloy plates were conducted and the axial force, welding temperature, gap and mismatch of the butt face are measured. Then, the measured multi-source data was fused through down-sampling technology. Before training, the Particle Swarm Optimization (PSO) and Long Short-Term Memory (LSTM) were adopted to optimize the initial learning rate and layer structure of the model. The experiment results showed that the proposed model can accurately and quickly predict the tensile strength of thick 2219-T8 aluminum alloy joints with the mean absolute percentage error less than 2%. In addition, metallographic analysis revealed that the joint tensile strength is interlocked with weld defects and fine grain strengthening. The research lays a foundation for welding quality control of FSW.

Machine learning-driven prediction of tensile strength in 3D-printed PLA parts

Additive manufacturing (AM) has become a transformative technology in modern production, enabling complex geometric designs with minimal material waste. A significant aspect of AM, particularly in fused deposition modeling (FDM), is the need for precise prediction of mechanical properties, such as ultimate tensile strength (UTS), which is crucial for industrial applications. This study examines whether simple machine learning (ML) algorithms can accurately predict the UTS of 3D-printed polylactic acid (PLA) parts, and evaluates the effectiveness of ML techniques, especially ensemble methods, in enhancing prediction accuracy. To this end, the study compares simple ML algorithms to identify the most accurate model for predicting the UTS of 3D-printed PLA parts. Subsequently, an average ensemble technique combines four ML algorithms, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and light gradient boosting machine (LGBM), to predict UTS. In this technique, the average predicted UTS values of CatBoost, XGBoost, GBM, and LGBM are taken as the final predicted UTS value. Additionally, 11 ensemble configurations of these algorithms are analyzed to determine the optimized ensemble configuration. The results show that the CatBoost algorithm, with an R2 of 94.46%, achieved the highest predictive accuracy among individual ML algorithms. Moreover, the CatBoost-XGBoost-GBM-LGBM ensemble was the most accurate configuration, achieving an R2 of 98.05% with less than 10% error in predicting 37 external data points not included in the training and testing sets. This study advances predictive modeling in AM by demonstrating that ML, particularly ensemble techniques, can reliably predict material properties, paving the way for more robust applications of AM in industry

Fracture Characteristics and Tensile Strength Prediction of Rock–Concrete Composite Discs Under Radial Compression

To investigate the fracture mechanism of rock–concrete (R–C) systems with an interface crack, Brazilian splitting tests were conducted, with a focus on understanding the influence of the interface crack angle on failure patterns, energy evolution, and RA/AF characteristics. The study addresses a critical issue in rock–concrete structures, particularly how crack propagation differs with varying crack angles, which has direct implications for structural integrity. The experimental results show that the failure paths in R–C disc specimens are highly dependent on the interface crack angle. For crack angles of 0°, 15°, 30°, and 45°, cracks initiate from the tips of the interface crack and propagate toward the loading ends. However, for angles of 60°, 75°, and 90°, crack initiation shifts away from the interface crack tips. The AE parameters RA (rise time/amplitude) and AF (average frequency) were used to characterize different failure patterns, while energy evolution analysis revealed that the highest percentage of energy consumption occurs at a crack angle of 45°, indicating intense microcrack activity. Moreover, a novel tensile strength prediction model, incorporating macro–micro damage interactions caused by both microcracks and macrocracks, was developed to explain the failure mechanisms in R–C specimens under radial compression. The model was validated through experimental results, demonstrating its potential for predicting failure behavior in R–C systems. This study offers insights into the fracture mechanics of R–C structures, advancing the understanding of their failure mechanisms and providing a reliable model for tensile strength prediction.

Optimization of a tensile strength prediction model for compacted ribbons using NIR-HIS analysis

The tensile strength (TS) of compacted ribbon is a critical quality attribute in the roller compaction process that impacts the quality of the finished product. This study investigated the use of Near Infrared Hyperspectral Imaging Spectroscopy (NIR-HIS) technology for predicting TS of compacted ribbons, considering the effects of surface curvature, different spectral preprocessing methods, and variable selection methods on a predictive model based on Partial Least Squares regression (PLSr). The spectral preprocessing methods evaluated were Mean Centering (MC) and Standard Normal Variate (SNV). The variable selection methods were Filter by Regression Coefficient (REG), Variable Importance in Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Genetic Algorithm (GA). The results indicated that curved surfaces had no significant impact on the predictive performance of the model (p-value of 0.39 for RMSEP). The PLSr-CARS method, combined with MC spectral preprocessing, was successful in selecting and reducing the number of wavelengths from 182 to 5, as indicated by high values of R2pred and RPD, and a low RMSEP value (0.97, 5.75, and 7.60 %, respectively). An MLR model using the 5 wavelengths was also developed, showing similar performance to the PLSr model. Both the MLR and PLSr models demonstrated high predictive accuracy and reliability. These models can perform well even when developed using only a few wavelengths, leading to significant reductions in processing time and measurement costs, making them valuable tools for quality control in the pharmaceutical industry.

Optimizing tensile strength and failure prediction in hemp fiber composites: Coupling effects of microfibril angle and fiber orientation under off-axis loading

In response to environmental concerns associated with synthetic fiber-reinforced materials, plant fiber-reinforced composites are increasingly recognized as a more sustainable alternative. However, predicting the mechanical properties of these composites remains challenging due to the unique microstructure of plant fibers. This study proposes an optimized Tsai-Hill failure criterion that incorporates the microfibril angle (MFA) and fiber orientation to enhance failure predictions. The MFA of hemp fibers was measured using X-ray diffraction (XRD), and the failure mechanisms of unidirectional hemp fiber composites under off-axis tensile stresses were thoroughly analyzed. Experimental results reveal a 10.69 % increase in tensile strength at a 5° off-axis angle compared to the fiber direction. As the off-axis angle increases, the failure mode transitions from fiber fracture and pull-out to matrix tearing and interlayer shear failure. For angles above 10°, the failure stress aligns with both the maximum stress and Tsai-Hill criteria. For angles below 10°, integrating MFA into the Tsai-Hill criterion significantly improves prediction accuracy. These findings offer critical insights for optimizing the tensile performance of hemp fiber composites and predicting their behavior under off-axis loading conditions. Consequently, this study can support the use of hemp fiber composites in a broader range of applications.

Prediction of Mechanical Properties of Rare-Earth Magnesium Alloys Based on Convolutional Neural Networks.

Rare-earth magnesium alloys exhibit higher comprehensive mechanical properties compared to other series of magnesium alloys, effectively expanding their applications in aerospace, weapons, and other fields. In this work, the tensile strength, yield strength, and elongation of a Mg-Gd-Y-Zn-Zr rare-earth magnesium alloy under different process conditions were determined, and a large number of microstructure observations and analyses were carried out for the tensile specimens; a prediction model of the corresponding mechanical properties was established by using a convolutional neural network (CNN), in which the metallographic diagram of the rare-earth magnesium alloy was taken as the input, and the corresponding tensile strength, yield strength, elongation, and three mechanical properties were taken as the output. The stochastic gradient descent (SGD) algorithm was used for parameter optimization and experimental validation, and the results showed that the average relative errors of the tensile strength and yield strength prediction results were 1.90% and 3.14%, respectively, which were smaller than the expected error of 5%.

Splitting behavior of polyvinyl alcohol fiber reinforced iron ore tailings concrete

To promote solid waste reutilization and reduce natural river sand consumption, iron ore tailings (IOT) are expected to be used in concrete production as a potential substitute for river sand under the premise of comparable material properties with ordinary concrete. In this study an IOT concrete reinforced by Polyvinyl Alcohol (PVA) fiber has been proposed and the slump and splitting behavior under various IOT substitution rates and PVA fiber contents has been experimentally investigated. Slump tests reveal that the optimal PVA fiber content is 0.1 % and IOT substitution rate is 60 % from the view of workability. Splitting tensile tests show a monotonous strength growth with PVA fiber content increase. The incorporation of IOT has increased concrete splitting tensile strength but the strength is smaller than that of normal concrete as soon as IOT substitution rates exceed 50 %. A regression model and Back Propagation (BP) neural network model have been adopted to establish splitting tensile strength prediction model. Compared to regression model, the BP model has exhibited better accuracy which is suitable to predict the splitting tensile strength of PVA-IOT concrete with PVA content less than 0.3 %.

Prediction of tensile strength using machine learning algorithms in fused deposition modeling

Prediction of tensile strength using machine learning algorithms in fused deposition modeling

Tensile strength prediction in monel 400 weldments using classification and regression algorithms in machine learning

Abstract Selecting the most effective welding settings impacts mechanical strength and weld quality, with parameters like current, voltage, and speed playing pivotal roles. The mechanical part encompasses material properties, welding process variables, and structural integrity, all contributing to the overall weld quality and strength. By integrating these mechanical factors with predictive modelling, a comprehensive understanding of weld performance can be achieved, enabling optimized welding settings and enhanced weld quality assurance. This study assesses and compares machine learning algorithms such as a random tree, random forest, and C4.5 to determine their predictive capability regarding the tensile strength in Monel 400 Weldments. By utilizing a dataset comprising 32 instances with attributes like Current, Voltage, and Speed, models were developed and assessed using K-Fold cross-validation. Among these algorithms, the random tree models emerge as the most proficient in accurately predicting the tensile strength for Monel 400 Weldments through classification ML techniques. Similarly, regression algorithms have been deployed to assess the dataset by varying the train-test split ratio and gradient boosting, which exhibited superior performance with a higher R2 value of 0.99. Both random tree and Gradient boosting algorithms have commonly been recommended, with current being the most influential factor affecting tensile strength.

Deep Learning-Driven Prediction of Mechanical Properties of 316L Stainless Steel Metallographic by Laser Powder Bed Fusion.

This study developed a new metallography-property relationship neural network (MPR-Net) to predict the relationship between the microstructure and mechanical properties of 316L stainless steel built by laser powder bed fusion (LPBF). The accuracy R2 of MPR-Net was 0.96 and 0.91 for tensile strength and Vickers hardness predictions, respectively, based on optical metallurgy images. Feature visualisation methods, such as gradient-weighted class activation mapping (Grad-CAM) and clustering, were employed to interpret the abstract features within the MPR-Net, providing insights into the molten pool morphology and grain formation mechanisms during the LPBF process. Experimental results showed that the optimal process parameters-190 W laser power and 700 mm/s scanning speed-yielded a maximum tensile strength of 762.83 MPa and a Vickers hardness of 253.07 HV0.2 with nearly full densification (99.97%). The study marks the first application of a convolutional neural network (MPR-Net) to predict the mechanical properties of 316L stainless steel samples manufactured through laser powder bed fusion (LPBF) based on metallography. It innovatively employs techniques such as gradient-weighted class activation mapping (Grad-CAM), spatial coherence testing, and clustering to provide deeper insights into the workings of the machine learning model, enhancing the interpretability of complex neural network decisions in material science.

Tensile strength prediction of steel sheets: an insight into data-driven models, dimensionality reduction, and feature importance

Tensile strength prediction of steel sheets: an insight into data-driven models, dimensionality reduction, and feature importance

Analysis of damage evolution in single‐lap and double‐lap bolted joints of carbon fiber reinforced polymer plates based on load distribution

AbstractIt is important to study damage evolution as well as failure of carbon fiber reinforced polymer (CFRP) plates bolted joints. Therefore, the quasi‐static tensile test on single‐lap and double‐lap CFRP plates bolted joints was conducted. Meanwhile, the tensile strength prediction model for titanium alloy bolted joint of CFRP plates was established based on the improved 3D Hashin failure criterion, then a theoretical model was proposed to accurately predict the load distributions and tilt angle of bolted joints. Thus, the load–displacement curves were divided into four stages, and load distributions at different stage points as well as the relationship between tilt angle of bolt and load variation were obtained. The damage evolution of single‐lap and double‐lap at different stage points was analyzed respectively, and failure mechanisms were revealed based on load distribution. The results show that the ultimate failure of single‐lap is caused by the intrusion of bolt head into CFRP plates, while double‐lap is caused by the compression deformation of central plate. The numerical simulation works are in high agreement with the experimental results.Highlights Calculate the load distribution at single‐lap and double‐lap bolted joints. Obtain the relationship between bolts tilt angle and load variation. Analysis of damage evolution in bolt‐hole wall of carbon fiber reinforced polymer (CFRP) plates. Using loads to reveal the failure mechanisms of single‐lap and double‐lap bolted joints of CFRP plates.

A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns.

Development of machine learning regression models for the prediction of tensile strength of friction stir processed AA8090/SiC surface composites

Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and fabrication of surface composites. Machine learning approaches have garnered significant interest as prospective models for modeling various production systems. The present work aims to develop four machine learning models, namely linear regression, support vector regression, artificial neural network and extreme gradient boosting to predict the influence of FSP parameters such as tool rotational speed, tool traverse speed and groove width on ultimate tensile strength of friction stir processed AA8090/SiC surface composites. These models were developed through Python programming and the original dataset was divided into 80% for the training phase and 20% for the testing phase. The performance of the models was evaluated by root mean squared error, mean absolute error and R2. Based on the results and graphical visualization, it was observed that the XGBoost model outperformed other models with high accuracy in predicting UTS of AA8090/SiC surface composites.

Prediction and optimization of tensile strength of additively manufactured PEEK biopolymer using machine learning techniques

Prediction and optimization of tensile strength of additively manufactured PEEK biopolymer using machine learning techniques

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates.

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.

A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures

Austenitic stainless steel plays a pivotal role in the nuclear industry with exceptional mechanical properties and corrosion resistance, wherein high-temperature tensile strength (∼290 °C) is a critical factor in ensuring its performance and reliability. This study focuses on the predictive modeling of tensile strength in cast austenitic stainless steel at room and elevated temperatures by developing a transfer learning strategy. The application of a gradient-boosting decision tree algorithm, enhanced by key features derived from room-temperature analyses and augmented with partial high-temperature data, results in significantly increased accuracy. The effective selection of features underscores the stability of critical variables across different temperatures, affirming the efficacy of the proposed transfer learning strategy. The work sheds light on material selection and design for high-temperature applications, and lays the groundwork for future exploration into predictive modeling of material properties in extreme conditions.

Tensile strength prediction of crumb rubber concrete based on mesoscale modelling

ABSTRACT This study presents an experimental study and a mesoscale model for evaluating the tensile performance of crumb rubber concrete (CRC). In the experimental study, indirect tensile strengths of CRC with 6%, 12% 18% rubber contents were tested. Then the tested CRC samples were modelled as a five-phase material containing rubber, coarse aggregate, mortar, aggregate-mortar interface layer, and rubber-mortar interface layer. Numerical results from the mesoscale model agreed well with experimental tests. Parameter analysis was carried out to investigate the content, size, shape of rubber particle, thickness of interface layer on the tensile strength of CRC. Numerical results also indicated that rubber content was the governing influencing factor on the tensile strength reduction. As comparison, the influence from rubber size and rubber shape was relatively low. For a specimen with constant rubber content and size, changes in the distribution of rubber particles resulted in slight changes in the tensile strength of CRC as well. The effect of rubber particles on the tensile properties of concrete was similar to that of pores. Finally, tensile strength prediction formula was developed based on the pores model proposed in this study.

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm