Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

Shamsirband, Shahab; Khansari, Nabi Mehri

doi:10.1631/jzus.a2000408

Cited by 13 publications

(5 citation statements)

References 152 publications

(179 reference statements)

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“…Recent advances in machine learning in Materials Science and Engineering have provided material scientists with new tools to expedite the collection of microstructure properties. These advancements include deep learning, programming question generation, and automated control and calibration [78][79][80][81][82][83][84][85][86][87]. HPC also continues to expand along with computing offload [88].…”

Section: Future Improvementsmentioning

confidence: 99%

Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures

Arndt,

Crusenberry,

Heng

et al. 2023

Modelling

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Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization. To overcome this curse of dimensionality and to minimize computational resource requirements, this research demonstrates a user-friendly approach to formulating a reduced-dimension surrogate model that represents a high-dimensional, high-fidelity source model. This approach was developed specifically for a non-expert using commercially available tools. In this approach, the complex physical behavior of the high-fidelity source model is separated into individual, interacting physical behaviors. A separate reduced-dimension surrogate model is created for each behavior and then all are summed to formulate the reduced-dimension surrogate model representing the source model. In addition to a substantial reduction in computational resources and comparable accuracy, this method also provides a characterization of each individual behavior providing additional insight into the source model behavior. The approach encompasses experimental testing, finite element analysis, surrogate modeling, and sensitivity analysis and is demonstrated by formulating a reduced-dimension surrogate model for the damage tolerance of an aluminum plate reinforced with a co-cured bonded E-glass/epoxy composite laminate under four-point bending. It is concluded that this problem is difficult to characterize and breaking the problem into interacting mechanisms leads to improved information on influential parameters and efficient reduced-dimension surrogate modeling. The disbond damage at the interface between the resin and metal proved the most difficult mechanism for reduced-dimension surrogate modeling as it is only engaged in a small subspace of the full parameter space. A binary function was successful in engaging this damage mechanism when applicable based on the values of the most influential parameters.

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Section: Future Improvementsmentioning

confidence: 99%

Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures

Arndt,

Crusenberry,

Heng

et al. 2023

Modelling

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show abstract

“…For the future research the comparative analysis with other machine learning methods, e.g., (Asadi et al 2019;Ghalandari et al 2019a;Ghorbani et al 2020c;Joloudari et al 2020;Mosavi et al 2020;Sadeghzadeh et al 2020;Shabani et al 2020;Abdali et al 2021;Mosavi and Safaei-Farouji 2021) would be essential to bring an insight into the true potential of the proposed method. To improve the accuracy and the performance of the proposed method further deep learning, ensemble and hybrid methods for instance, those suggest in (Band et al 2020b;Dehghani et al 2020;Ghorbani et al 2020a;Mosavi et al 2020;Nabipour et al 2020;Mousavi et al 2021;Shamsirband and Mehri Khansari 2021) can come to the consideration.…”

Section: Recommendations For Future Research Workmentioning

confidence: 99%

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Rajabi

Hazbeh

Davoodi

et al. 2022

J Petrol Explor Prod Technol

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Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

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“…These researchers showed that the mean squared error of the ML prediction of the mechanical properties is several orders of magnitude smaller than the actual value of each attribute, indicating that the model has good training results. Shamsirband and Khansari (2021) believed that the most powerful methods for detecting damage are ML and deep learning. They discussed advanced ML methods and their applications in detecting and predicting material damage.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Transmission Model and Numerical Simulation Based on Machine Learning

Zhang

2023

International Journal of Information Technologies and Systems Approach

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Mechanical transmission is one of the earliest transmission modes in human society. With the continuous progress of science and technology, effective simulation and calculation research on mechanical transmission has gradually become an important link in the study of mechanical transmission. In the actual engineering practice, reliable and accurate data are difficult to obtain due to the complexity and low accuracy of the traditional mechanical transmission process. Machine learning (ML), a model trained by data, was used to analyze the response of the system through different parameters and drew scientific and reasonable conclusions. ML is more intuitive, easier to operate, and faster in calculation than the traditional methods. In many mechanical structures, due to the large number of processing parts and data, numerical simulation of this important equipment requires a considerable time to adjust and optimize accordingly.

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Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

Cited by 13 publications

References 152 publications

Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures

Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Mechanical Transmission Model and Numerical Simulation Based on Machine Learning

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