Fault Detection for CNC Machine Tools Using Auto-Associative Kernel Regression Based on Empirical Mode Decomposition

Jung, Seunghwan; Kim, Minseok; Kim, Baekcheon; Kim, Jinyong; Kim, Eunkyeong; Kim, Jonggeun; Lee, Hyeonuk; Kim, Sungshin

doi:10.3390/pr10122529

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…This is because image data can be generated simply by data augmentation and can be distinguished better than time-series data. This overcomes data deficiency and imbalance, and improves the classification accuracy of the normal and abnormal data [21,22]. To remove unnecessary data, the current data were extracted during pre-processing.…”

Section: Anomaly Detection Methods Using Puzzle-based Data Augmentati...mentioning

confidence: 99%

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Kim,

Jung,

Kim

et al. 2023

Machines

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Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.

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Section: Anomaly Detection Methods Using Puzzle-based Data Augmentati...mentioning

confidence: 99%

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Kim,

Jung,

Kim

et al. 2023

Machines

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“…• Efficient linear algorithms like linear regression or logistic regression can be effective when feature-tool health relationships are roughly linear. Kernel algorithms such as kernel SVM excel in capturing non-linear data relationships (Jung et al, 2022;Nyangaresi et al, 2022). • Ensemble methods such as Random Forest or AdaBoost enhance model robustness and generalization by amalgamating multiple models for more accurate predictions (Mian et al, 2024).…”

Section: Potential Reasons Behind the Selection Of ML Algorithms For ...mentioning

confidence: 99%

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Tambake,

Deshmukh,

Pardeshi

et al. 2024

Front. Mater.

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Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.

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“…This process involves manufacturing products by cutting or milling workpieces according to pre-designed shapes. Manufacturing products using CNC machine tools can affect the cutting tools of the CNC machine tool, as the machine tool processes the workpiece while rotating the device fixed on the spindle motor of the CNC machine tool [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks

Zhang,

Liu,

Wang

et al. 2023

Processes

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As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for studying process optimization. Better processing quality is often achieved through their optimization. Therefore, it is necessary to study the three cutting process parameters of the CNC machine tool spindle. In this paper, we proposed an improved algorithm incorporated with the beetle antennae search algorithm for the most probable explanation in Bayesian networks to achieve optimization calculation of process parameters. This work focuses on building adaptive dynamic step parameters to improve detection behavior. The chaotic strategy is discretized and used to establish the dominant initial population during the population initialization. This article uses four standard network data sets to compare the time and fitness values based on the improved algorithm. The experimental results show that the proposed algorithm is superior in time and accuracy compared to similar algorithms. At the same time, an optimization example for the actual machining of a universal CNC machine tool spindle was provided. Through the optimization of this algorithm, the true machining quality was improved.

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Fault Detection for CNC Machine Tools Using Auto-Associative Kernel Regression Based on Empirical Mode Decomposition

Cited by 6 publications

References 39 publications

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Machine learning for monitoring hobbing tool health in CNC hobbing machine

An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks

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