Machine Learning of Phases and Mechanical Properties in Complex Concentrated Alloys

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

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A machine learning case study with limited data for prediction of carbon fiber mechanical properties

Computers in Industry ◽

10.1016/j.compind.2018.11.004 ◽

2019 ◽

Vol 105 ◽

pp. 123-132 ◽

Cited By ~ 8

Author(s):

Gelayol Golkarnarenji ◽

Minoo Naebe ◽

Khashayar Badii ◽

Abbas S. Milani ◽

Reza N. Jazar ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Carbon Fiber ◽

Limited Data

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Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Materials ◽

10.3390/ma14216713 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6713

Author(s):

Omid Khalaj ◽

Moslem Ghobadi ◽

Ehsan Saebnoori ◽

Alireza Zarezadeh ◽

Mohammadreza Shishesaz ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Mechanical Alloying ◽

Fuzzy Inference ◽

Oxide Dispersion Strengthened ◽

Machine Learning Techniques ◽

Support Vector ◽

Anfis Model ◽

Inference Systems ◽

The Impact

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

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Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽

10.3390/polym13183100 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3100

Author(s):

Anusha Mairpady ◽

Abdel-Hamid I. Mourad ◽

Mohammad Sayem Mozumder

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Genetic Algorithm ◽

Machine Learning Techniques ◽

Hybrid Technique ◽

Learning Techniques ◽

Artificial Neural Network Ann ◽

Major Factors ◽

The Cost ◽

Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

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Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

10.20944/preprints201903.0051.v1 ◽

2019 ◽

Author(s):

Ivanna Baturynska

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Additive Manufacturing ◽

Linear Regression ◽

Prediction Accuracy ◽

Regression Models ◽

Tensile Modulus ◽

Machine Learning Techniques ◽

Linear Regression Models ◽

Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

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Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽

10.3390/ma15020647 ◽

2022 ◽

Vol 15 (2) ◽

pp. 647

Author(s):

Meijun Shang ◽

Hejun Li ◽

Ayaz Ahmad ◽

Waqas Ahmad ◽

Krzysztof Adam Ostrowski ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Mean Square Error ◽

Coarse Aggregate ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Environmental Damage ◽

Fine Aggregate ◽

Mean Square ◽

The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

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Machine-learning assisted additive manufacturing of a TiCN reinforced AlSi10Mg composite with tailorable mechanical properties

Materials Letters ◽

10.1016/j.matlet.2021.131018 ◽

2021 ◽

pp. 131018

Author(s):

Peidong He ◽

Qian Liu ◽

Jamie J. Kruzic ◽

Xiaopeng Li

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Additive Manufacturing

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RESERVOIR SCALE CHEMOSTRATIGRAPHY AND FACIES MODELING USING HIGH SAMPLE RATE GEOPHYSICAL SCANS OF WHOLE CORE

10.30632/spwla-2021-0051 ◽

2021 ◽

Author(s):

John J. Degenhardt ◽

◽

Safdar Ali ◽

Mansoor Ali ◽

Brian Chin ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Resolution ◽

Operating Characteristics ◽

Eagle Ford Shale ◽

Eagle Ford ◽

Vertical Heterogeneity ◽

Austin Chalk ◽

Sample Rate ◽

Petrophysical Logs

Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.

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