Cutting Tool Condition Monitoring and Prediction Based on Dynamic Data Driven Approaches

2015 ◽  
Author(s):  
Zhenhua Wu
Keyword(s):  
Tool Wear ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Cutting Tool ◽  
Feature Fusion ◽  
Data Driven ◽  
Support Vector ◽  
Dynamic Data ◽  
Inference System ◽  
Model Average

In this paper, monitoring and prediction of cutting tool wear condition based on dynamic data driven approaches were investigated. Sensor signals obtained from the machining processes were processed through wavelet denoising to filter the noise un-related to cutting, features in time and frequency domains were extracted using classical signal processing approaches, and then were selected with Pearson correlation coefficient. The most related features were sent to the feature fusion approaches including neural network (NN), adaptive neural fuzzy inference system (ANFIS), or support vector regression (SVR) to estimate the tool wear. Statistics performance evaluation based on correlation coefficient (R2), average absolute error (AAE), and Se/Sy, as well as cross validation, selected the most proper feature fusion approach. Further, prediction models based on Bayesian model average were applied to predict the future tool wear. A case study based on the end mill experiment with signals of 3-axis cutting forces, 3-axis vibrations and acoustic emission, illustrated the proposed approach. It showed that ANFIS has the best estimation accuracy with the R2 of 0.99, AAE of 0.42, Se/Sy of 0.12, and cross validation error of 13.36. In the prediction stage, the prediction model has high prediction accuracy with all the experiment results covered by 95% confidence interval of prediction.

scholarly journals A Hybrid Remaining Useful Life Prediction Method for Cutting Tool Considering the Wear State

2022 ◽  
Author(s):  
Yifan Li ◽  
Yongyong Xiang ◽  
Baisong Pan ◽  
Luojie Shi
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Cutting Tool ◽  
Bayesian Framework ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Support Vector ◽  
Wear Model ◽  
Useful Life

Abstract Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion

Measurement ◽  
2021 ◽  
pp. 110072
Author(s):  
Xuebing Li ◽  
Xianli Liu ◽  
Caixu Yue ◽  
Shaoyang Liu ◽  
Bowen Zhang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Feature Fusion ◽  
Data Driven ◽  
Quantitative Prediction ◽  
Data Driven Approach ◽  
Radar Map

Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

2020 ◽  
Vol 37 (5) ◽  
pp. 1737-1756
Author(s):  
Zhen Yang ◽  
Kangning Song ◽  
Xingsheng Gu ◽  
Zhi Wang ◽  
Xiaoyi Liang
Keyword(s):  
Experimental Data ◽  
Selective Catalytic Reduction ◽  
Cross Validation ◽  
Activated Carbons ◽  
Catalytic Reduction ◽  
Data Driven ◽  
Support Vector ◽  
Content Type ◽  
Reduction Of No ◽  
Fold Cross Validation

Purpose Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods. Design/methodology/approach Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model. Findings The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data. Originality/value SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

scholarly journals Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Xianming Dou ◽  
Yongguo Yang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Vapor ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Group Method ◽  
Generalized Regression Neural Network ◽  
Learning Approaches ◽  
Inference System

Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

scholarly journals Estimation of Discharge and Total Water Level at Yedgaon Dam using Data Driven Techniques

2021 ◽  
Vol 1197 (1) ◽  
pp. 012021
Author(s):  
Preeti S. Kulkarni ◽  
Shreenivas Londhe ◽  
Nikita Sainkar ◽  
Sayali Rote
Keyword(s):  
Water Level ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Single Equation ◽  
Data Driven ◽  
Support Vector ◽  
Good Prediction ◽  
M5 Model Tree ◽  
Using Data

Abstract A reservoir operation planning using Data driven Techniques is gaining its momentum in hydrological area with good prediction and Estimation capabilities. The present work aims at using the 5 years data of Water Level to estimate the discharge and water level at the Yedgaon dam which is like pick up weir having its own yield and storage. It receives water from Dimbhe (though DLBC), Wadaj (through MLBC), Manikdoh (through river) and through Pimpalgaojoge (through river), in the Kukadi project of Maharashtra State, India. 4 different models were developed to estimate the water level using the Data Driven Techniques: M5 Model Tree, Support Vector Regression, Multi Gene Genetic Programming and Random Forest. The Accuracy of the developed models is assessed by the values of coefficient of correlation, coefficient of efficiency, mean absolute error and root mean squared error and comparison is done between actual values and Predicted values. The results indicated that the MGGP model was superior as compared to other techniques with correlation coefficient as 0.86 with an advantage of a single equation to estimate the water level.

scholarly journals Watershed Prioritization and Decision Making Based On Weighted Sum Analysis, Feature Ranking and Machine Learning Techniques.

2021 ◽  
Author(s):  
Kishanlal Ramlal Darji ◽  
Dhruvesh Prehladbhai Patel ◽  
Vinay Vakharia ◽  
Jaimin Panchal ◽  
Amit Kumar Dubey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Geographical Information ◽  
Support Vector ◽  
Feature Ranking ◽  
Weighted Sum ◽  
Watershed Prioritization ◽  
Weighted Sum Analysis ◽  
Fold Cross Validation

Abstract Prediction and validation of Compound factors for prioritization of watersheds is an essential application using Machine Learning (ML) Techniques in water resources engineering. In the current paper, a method is proposed to derive 14 morphometric and 3 Topo-hydrological parameters using Remote Sensing (RS) and Geographical Information System (GIS), whereas prediction and validation of compound factor using ML techniques. Compound factor (CF) values are calculated using Weighted Sum Analysis (WSA), ReliefF, correlation coefficient techniques. A ten-fold cross-validation technique is applied to two machine learning models Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). Predication accuracy of models has been further achieved by feature ranking. The accuracy of ML models is evaluated with three parameters, Mean Absolute Error (MEA), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). With the ranked features and Ten-fold cross-validation, prediction results were found to be better. The methodology will be useful for the accurate prediction of CF values and to reduce the uncertainty in watershed prioritization for conservation techniques for soil and water.

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

2017 ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Support Vector ◽  
Model Based ◽  
Smart Manufacturing Systems

Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical understanding of system behaviors is required. In practice, however, some of the distributional assumptions do not hold true. To overcome the limitations of model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While earlier work demonstrated the effectiveness of data-driven approaches, most of these methods applied to prognostics and health management (PHM) in manufacturing are based on artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to explore the ability of random forests (RFs) to predict tool wear in milling operations. The performance of ANNs, SVR, and RFs are compared using an experimental dataset. The experimental results have shown that RFs can generate more accurate predictions than ANNs and SVR in this experiment.

Tool Wear Identification in Turning Titanium Alloy Based on SVM

2014 ◽  
Vol 800-801 ◽  
pp. 446-450 ◽  
Author(s):  
Zhi Rong Liao ◽  
Sheng Ming Li ◽  
Yong Lu ◽  
Dong Gao
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Tool ◽  
Support Vector ◽  
Test Set ◽  
Tool Condition ◽  
Good Classification ◽  
Ae Signal

Titanium alloy is difficult cutting materials,the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features , SVM is trained and tested using these features ,Good classification results were achieved by using test set.

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