A White-Box SVM Framework and its Swarm-Based Optimization for Supervision of Toothed Milling Cutter through Characterization of Spindle Vibrations

Deo, Tejas Y.; Patange, Abhishek D.; Pardeshi, Sujit S.; R., Jegadeeshwaran,; Khairnar, Apoorva N.; Khade, Hrushikesh S.

doi:10.48550/arxiv.2112.08421

Cited by 13 publications

(13 citation statements)

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“…The models utilized in this paper, including KNN, ridge regression, and lasso regression, are generally considered to be white-box models [ 66 ], meaning that they are relatively easy to interpret and understand. These models make their predictions based on a set of weights or coefficients that are applied to the input features and can be examined, to understand how the model is making its decisions.…”

Section: Resultsmentioning

confidence: 99%

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Omar

Khan

Starr

2023

Sensors

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Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model’s predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.

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Section: Resultsmentioning

confidence: 99%

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Omar

Khan

Starr

2023

Sensors

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“…We used the swam optimizer for hyperparameter tuning to select the best hyperparameters in comparison with our selected hyperparameters. For this purpose, we used the mealpy 1.0.2 library [ 42 , 43 ]. We imported a particle swarm optimization (PSO) model named BasePSO, and tuned the best performer RF with its two hyperparameters, n_estimators and max_depth.…”

Section: Resultsmentioning

confidence: 99%

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

et al. 2022

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White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

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“…In the future, we will study the defect detection method for wind turbine blades from two aspects: first, we want to obtain acoustic signals through artificially produce damage. Second, we want to try tree-based algorithms [30] for WTB defect detection.…”

Section: Discussionmentioning

confidence: 99%

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

et al. 2022

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Wind power has become an important source of electricity for both production and domestic use. However, because wind turbines often operate in harsh environments, they are prone to cracks, blisters, and corrosion of the blade surface. If these defects cannot be repaired in time, the cracks evolve into larger fractures, which can lead to blade rupture. As such, in this study, we developed a remote non-contact online health monitoring and warning system for wind turbine blades based on acoustic features and artificial neural networks. Collecting a large number of wind turbine blade defect signals was challenging. To address this issue, we designed an acoustic detection method based on a small sample size. We employed the octave to extract defect information, and we used an artificial neural network based on model-agnostic meta-learning (MAML-ANN) for classification. We analyzed the influence of locations and compared the performance of MAML-ANN with that of traditional ANN. The experimental results showed that the accuracy of our method reached 94.1% when each class contained only 50 data; traditional ANN achieved an accuracy of only 85%. With MAML-ANN, the training is fast and the global optimal solution is automatic searched, and it can be expanded to situations with a large sample size.

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A White-Box SVM Framework and its Swarm-Based Optimization for Supervision of Toothed Milling Cutter through Characterization of Spindle Vibrations

Cited by 13 publications

References 0 publications

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images

Wind Turbine Blade Defect Detection Based on Acoustic Features and Small Sample Size

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