Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Yang, Hui; Mei, Xuesong; Jiang, Gedong; Tao, Tao; Pei, Changyu; Ma, Ziwei

doi:10.1155/2019/7386523

Cited by 33 publications

(14 citation statements)

References 41 publications

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“…Yang Hui et.al used Support Vector Machine (SVM) algorithm to extract the features from the vibration signals are sensed from a milling tool. The stacked generalization (SG) ensemble model based on SVM, decision tree (DT), Naive Bayes (NB) algorithms are used to recognize the tool wear state of the milling tool [9]. Benjamin Neef et.al used Support Vector Machine (SVM) and random forest ensemble (RSE) algorithms to analyse the high frequency current samples of a CNC turning machine terminal to estimate of the tool wear.…”

Section: Literature Reviewmentioning

confidence: 99%

Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Sk¹,

Jd²

2020

Preprint

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In automated manufacturing systems, most of the manufacturing processes including machining are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Ceramic cutting tools are used to machine hard materials. Ti[C,N] mixed alumina ceramic cutting tools are widely used to machine hardened steel and Stainless Steel due to its superior mechanical properties. Martensitic stainless steel has wide applications in screws, bolts, nuts and other engineering applications. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Using the dataset obtained from experimental machining tool life model has been developed using Gradient Descent algorithm. The model was validated using co-efficient of determination. The accuracy of the machine learning model was tested using the test data and 99.83% accuracy was obtained. Tool life model based on Gradient Descent Algorithm was successfully implemented for the tool life of Ti[C,N] mixed alumina ceramic cutting tool.Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article; yet reasonably common within the subject discipline.)

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Section: Literature Reviewmentioning

confidence: 99%

Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Sk¹,

Jd²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence, the indirect method of acquiring physical information by sensors and analyzing the relationship with the tool state is usually applied to develop tool wear monitoring systems, such as vibration signals, sound signals, and cutting forces [8][9][10][11][12][13][14]. The acquired signals are then analyzed to extract features based on the time and frequency domain [15][16][17][18]. Certain methods are introduced to ensure that the input features are highly related to the tool state, which is helpful for building models with lower computation costs or reducing overfitting problems.…”

Section: Introductionmentioning

confidence: 99%

Robust tool wear monitoring system development by sensors and feature fusion

Lin

Lee

2022

Asian Journal of Control

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This study introduces a tool wear monitoring system that uses multiple sensors and a feature fusion technique. To improve the robustness of the system, different tightening torque and spindle speed conditions were considered in the experimental design stage. Vibration signals in three coordinates and sound signals were collected and transformed by fast Fourier transform for feature extraction. Two types of features in the frequency domain were used to establish the proposed system, including the mean value features selected by the class mean scatter feature selection criterion and statistical features by Gradient Class Activation Mapping++ (Grad-CAM++). The proposed monitoring system was established using a hierarchical neural network structure and feature fusion of the sensors. Cross-validation was introduced to demonstrate the performance and effectiveness of our approach under various tightening torque values and spindle speeds.

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“…To overcome this problem, ensemble learning methods are proposed to accomplish a high-precision classification task by ensembling multiple weak classifiers into a robust classifier. The most commonly used ensemble learning methods include parallelized ensemble of bagging [ 20 ], sequential integration of boosting [ 21 ], and multilayer classification stacking [ 22 ]. Stacking ensemble learning (SEL) could significantly improve the predictive force of models compared with bagging and boosting ensemble learning.…”

Section: Introductionmentioning

confidence: 99%

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Jia

Sun

et al. 2022

Sensors

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Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.

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Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model

Cited by 33 publications

References 41 publications

Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Robust tool wear monitoring system development by sensors and feature fusion

Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

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