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.

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.

A Fracture Mechanics Approach to the Prediction of Tool Wear in Dry High Speed Machining of Aluminum Cast Alloys—Part 2: Model Calibration and Verification

2006 ◽  
Vol 129 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Alexander Bardetsky ◽  
Helmi Attia ◽  
Mohamed Elbestawi
Keyword(s):  
Tool Wear ◽  
Automotive Industry ◽  
Model Calibration ◽  
High Speed ◽  
Cutting Tool ◽  
Tool Material ◽  
Cutting Conditions ◽  
High Speed Machining ◽  
Wear Model ◽  
Cast Alloys

Background. Aluminum alloys are extensively used in the automotive industry and their utilization continues to rise because of the environmental, safety and driving performance advantages. Experimental study has been carried out in this work to establish the effect of cutting conditions (speed, feed, and depth of cut) on the cutting forces and time variation of carbide tool wear data in high-speed machining (face milling) of Al–Si cast alloys that are commonly used in the automotive industry. Method and Approach. The experimental setup and force measurement system are described. The cutting test results are used to calibrate and validate the fracture mechanics-based tool wear model developed in part 1 of this work. The model calibration is conducted for two combinations of cutting speed and a feed rate, which represent a lower and upper limit of the range of cutting conditions. The calibrated model is then validated for a wide range of cutting conditions. This validation is performed by comparing the experimental tool wear data with the tool wear predicted by calibrated cutting tool wear model. Results and Conclusions. The maximum prediction error was found to be 14.5%, demonstrating the accuracy of the object oriented finite element (OOFE) modeling of the crack propagation process in the cobalt binder. It also demonstrates its capability in capturing the physics of the wear process. This is attributed to the fact that the OOF model incorporates the real microstructure of the tool material. The model can be readily extended to any microstructure of Al–Si workpiece and carbide cutting tool material.

Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life

2013 ◽  
Vol 717 ◽  
pp. 390-395 ◽  
Author(s):  
Lin Jiang ◽  
Wei Ming Xian ◽  
Bin Long ◽  
Hou Jun Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Particle Filtering ◽  
Linear Trend ◽  
Lithium Ion ◽  
Single Step ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Data Set ◽  
Useful Life

As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i.e., Support Vector Machine (SVM), Autoregressive Moving Average (ARMA), and Particle Filtering (PF) are presented for RUL prediction. The lithium-ion battery aging experiment data set has been trained to implement simulation. Based on the RUL prediction result, we can conclude that: (1) ARMA model achieved better result than SVM, however, the result shows a linear trend, which fail to properly reflect the degradation trend of the battery; (2) SVM often suffers from over fitting problem and is more suitable for single-step prediction; and (3) PF approach achieved a better prediction and reflected the trends of degradation of the battery owing to its combined with specific model.

scholarly journals Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4217
Author(s):  
Peihong Li ◽  
Xiaozhi Liu ◽  
Yinghua Yang
Keyword(s):  
Historical Data ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Excellent Performance ◽  
Hybrid Features ◽  
Temporal Dimension ◽  
Spatial Graph ◽  
Useful Life ◽  
The Time Domain

As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional methods based on physical models. In this paper, we propose a novel data-driven method for predicting the remaining useful life of bearings based on a deep graph convolutional neural network with spatiotemporal domain convolution. This network uses the average sliding root mean square (ASRMS) as the health factor to identify the healthy and degraded states, and then uses correlation coefficient analysis on the hybrid features of the degraded data to construct a spatial graph according to the strength of the correlation between the obtained features. In the time domain, we introduce historical data as the input to the temporal convolution. After the data are processed by the spatial map and the temporal dimension, we perform the prediction of the remaining useful life. The experimental results show the accuracy of the method.

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

2018 ◽  
Vol 929 ◽  
pp. 93-102
Author(s):  
Didik Djoko Susilo ◽  
Achmad Widodo ◽  
Toni Prahasto ◽  
Muhammad Nizam
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Degradation Data ◽  
Starting Point ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

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.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

scholarly journals Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5011
Author(s):  
Yuanxing Huang ◽  
Zhiyuan Lu ◽  
Wei Dai ◽  
Weifang Zhang ◽  
Bin Wang
Keyword(s):  
Surface Roughness ◽  
Failure Criterion ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Model Parameters ◽  
Inverse Gaussian ◽  
Wear Process ◽  
Useful Life ◽  
Dynamic Wear

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

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