Tool Wear Prediction Model Research Articles

Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining

The quality produced of the machined workpiece is affected by various machining parameters including the tool wear. The condition of tool can be monitored by analyzing various machining responses through sensors. The use of multiple sensors allows for a comparison of data and information gathered from various sources, assisting in deciding the state of the tool and the workpiece. In current work, multiple sensors are attached to a lathe during the machining of AISI 4140. This paper presents a multi-sensor data fusion method for flank wear measurement and prediction using various parameters such as vibration, power, temperature, force, and surface roughness. Dry machining is performed by using PVD coated tool insert, and the flank wear is analyzed. Experiments are performed with a full factorial experimental design L27 orthogonal array. Taguchi approach has been utilized to evaluate the effect of machining parameters on tool wear. Also, the tool wear is predicted through an artificial neural network (ANN) model, and the results are compared with manually measured data. The results showcased the effectiveness of the proposed tool wear prediction model with the least measurement error. The work presents a novel neural network approach for the accurate prediction of tool wear and hence eliminates the necessity of manual measurement of tool wear and leading towards the increased productivity and quality of the work part.

Tool Wear Prediction in the Forming of Automotive DP980 Steel Sheet Using Statistical Sensitivity Analysis and Accelerated U-Bending Based Wear Test

The forming process of ultra-high-strength steel (UHSS) may cause premature damage to the tool surface due to the high forming pressure. The damage to and wear of the tool surface increase maintenance costs and deteriorate the surface quality of the sheet products. Hence, a reliable prediction model for tool wear can help in the efficient management of the quality and productivity of formed sheet products of UHSS. In this study, a methodology is proposed for predicting the wear behavior of stamping tools that are used in the forming process of DP980 steel sheet. Pin-on-disk tests were conducted based on the Taguchi method to develop the tool wear prediction model. Using statistical analysis based on the signal-to-noise (S/N) ratio and ANOVA result, the contact pressure and the sliding distance were selected as the major contact parameters for tool wear. The Archard wear model has a limitation in predicting the nonlinear behavior of tool wear. Therefore, the power-law nonlinear regression model as a function of the contact pressure and the sliding distance was constructed. To verify the reliability of the constructed tool wear prediction model, the U-draw bending tests were designed. The modified U-draw bending test, which accelerates tool wear, is newly designed to evaluate the tool wear for different contact pressures and sliding distances. The modified die generated a contact pressure four times higher than that of the conventional die from the finite element (FE) simulation results. The tool wear prediction model was validated by comparing the predicted results with the experimental results of DP980 sheets formed using the physical vapor deposition (PVD) CrN-coated STD11 tool steel.

Tool Wear Status Recognition and Prediction Model of Milling Cutter Based on Deep Learning

In order to ensure the reliability and stability of the manufacturing process, tool wear state should be realized real-time and accurate monitoring. This paper proposes a tool wear state recognize and predictive framework model based on Stacking Sparse De-noising Auto-Encoder (SSDAE), the Particle Swarm Optimization (PSO) and the Least Squares Support Vector Machine (LSSVM). The Stacking Sparse De-noising Auto-Encoder (SSDAE) technique is utilized to realize multi-feature signal dimension reduction with the aim of improving the prediction accuracy, which reduces the dependence on the prior knowledge of feature selection and greatly improves the modeling efficiency. PSO technique is helpful for adaptive optimization of kernel parameters, which greatly improved computing power and LSSVM model prediction accuracy. A dataset from a real machining process is utilized to verify the effectiveness of proposed model in improving the prediction accuracy. The experimental results show that a high correlation coefficient greater than 0.95 is used to extract feature vector from time domain, frequency domain and time-frequency domain three directions, and the proposed SSDAE-PSO-LSSVM model performs better than Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN) and Extreme Learning Machine (ELM) in terms of prediction accuracy.

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning.

Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

Feature selection for predicting tool wear of machine tools

In this study, the vibration transmitted solely from a spindle to the worktable is proposed to be a crucial feature of wear prediction models for machine tools. To validate the effectiveness of the proposed feature, a feature ranking and screening methodology was also used for developing a tool wear prediction model. First, the features extracted from vibration signals were ranked according to their contributions to tool wear prediction. The features were then filtered through a screening process based on singular value decomposition to eliminate redundant features, which exhibited collinearity with features of higher rankings. The aim of the aforementioned steps was to use a relatively small number of highly appropriate features to create an accurate real-time tool wear prediction model. The results indicated that the accuracy of the tool wear prediction model based on the proposed feature ranking and screening methodology is higher than that of models without feature ranking or screening. Moreover, the proposed feature was proven to be more important and effective than other features.

A smart tool wear prediction model in drilling of woven composites

Undetected tool wear during drilling of woven composites can cause laminate damage and fiber pull-out and fuzzing, causing subsurface damage. This diminishes the life of the produced part under fatigue loads. Thus, the producing of proper and reliable holes in woven composites requires accurate monitoring of the cutting tool wear level to safeguard the machined parts and increase process productivity and profitability. Available tool condition monitoring (TCM) systems mainly require long development lead time and extensive experimental efforts to predict the tool wear within predefined values of cutting conditions. The changes in these values require system relearning. Therefore, developing of a smart generalized TCM system that can accurately predict tool wear based on unlearned data during drilling of woven composite plates is crucial. In this work, an attempt was presented and discussed to predict the tool wear in drilling of woven composite plates at different and wide range of cutting conditions based on the drilling forces using biased learning data. A generalized heuristic model was proposed to accurately predict tool wear value. The performance of the proposed model was benchmarked with respect to four machine learning techniques namely regression tree, support vector machine (SVM), Gaussian process regression (GPR), and artificial neural network (ANN). Extensive experimental validation tests have showed that the GPR model has offered the lowest prediction error based on a reduced biased learning dataset, which represents 50% reduction in learning efforts compared with available literature. However, the developed heuristic model showed a comparable accuracy using significantly less learning efforts.

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations.

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

Prediction of tool wear width size and optimization of cutting parameters in milling process using novel ANFIS-PSO method

In the process of intelligent manufacturing, appropriate learning algorithm and intelligent model are necessary. In this work, a novel learning algorithm named random vibration and cross particle swarm optimization algorithm was proposed. The proposed algorithm is used for the prediction and optimization of machining process. Tool wear is an important factor that affects the machined surface quality during machining process, so it is necessary to find qualified tool wear prediction model and obtain the best combination of machining parameters to prolong tool life. In this study, the adaptive network–based fuzzy inference system was established to predict the tool wear width size. The random vibration and cross particle swarm optimization algorithm was tested using benchmark functions, and the results showed that random vibration and cross particle swarm optimization algorithm is able to find global optimum. Compared with the adaptive network–based fuzzy inference system trained by particle swarm optimization algorithm and adaptive network–based fuzzy inference system trained by differential evolution models, the results showed that the adaptive network–based fuzzy inference system trained by random vibration and cross particle swarm optimization algorithm can give a more accurate predicted value for offline prediction of the tool wear width size. In order to obtain the best combinations of cutting parameters under different removal area, the multi-objective optimization based on random vibration and cross particle swarm optimization algorithm was established. The optimized cutting parameters were verified and could be accepted to prolong tool life and improve machining efficiency.

Unsupervised online prediction of tool wear values using force model coefficients in milling

Tool wear prediction is an important research in metal cutting, which aims to improve machining accuracy and production efficiency, maximize tool utilization, and reduce machining cost. However, due to high complexity and nonlinearity of tool wear process, it is difficult to establish a general tool wear prediction model, which limits its application in industrial production. To solve this problem, an unsupervised online prediction method for tool wear values is proposed. In the method, a milling force model considering tool wear is established by using analytical method, and parameters varying with tool wear in the force model are integrated into five force model coefficients. The coefficients are solved and updated continuously using the least square estimation method according to the cutting force signals collected in real time. Based on study of relationship between the coefficients and tool wear, a tool flank wear value estimation model is constructed, combined with a time series analysis model, to achieve prediction of tool flank wear values. Experiments are conducted to test the prediction accuracy of tool wear values using the proposed method, and the results show that the average online prediction accuracy reached 72.0%, without supervision. The method has the advantages of low cost and strong adaptability, and can be used for online prediction of tool wear in machining industry.

Experimental Study on Tool Wear in Cutting Superalloy under High-Pressure Cooling

Superalloy is widely used in the aerospace industry and is a typical difficult-to-cut material. PCBN tool has good cutting efficiency in processing superalloy, but it has a larger cutting force, higher cutting temperature and severe tool wear in the cutting process. High-pressure cooling has great advantages in processing high strength steels such as superalloy, which can effectively reduce cutting temperature and improve tool-chip breaking performance, thereby reducing tool wear. Therefore, it is of theoretical significance and practical value to study tool wear mechanism of PCBN tool cutting superalloy under high-pressure cooling. Firstly, the wear form of the flank is analyzed to provide theoretical support for tool wear research; Secondly, the experimental study of tool wear under high-pressure cooling is carried out. The tool wear under different cutting time, cutting speed and cooling pressure is analyzed respectively, and the influence rule of different cutting conditions on tool wear is obtained; Finally, a tool wear prediction model under high-pressure cooling is established to provide theoretical basis and technical methods for efficient processing of superalloy and optimization of cutting parameters under high-pressure cooling.

Wear Mechanism of Cemented Carbide Tool and Modeling Tool Wear in Machining Inconel 718

When machining Inconel 718, tool wear is a serious problem, which affects the quality of workpiece. In order to analyze the wear mechanism in dry machining Inconel 718 with cemented carbide tool, the wear morphology of tool was observed by CCD and scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectrometer in this paper. The results showed that at low cutting speed, the formation and shedding of the built-up-edge caused severe cutting tool breakage. When the cutting speed reached 40 m/min, the tool material fell off from the tool substrate in the form of wear debris, which was the main reason for tool wear. In addition, the elements diffusion between the tool and the workpiece and oxidation reaction accelerated the formation and falling off of wear debris. According to the tool wear mechanism, a tool wear prediction model was established. In machining Inconel 718, the excellent experimental agreement have been obtained in the prediction of wear in cemented carbide tool.

Modeling and analysis of tool wear prediction based on SVD and BiLSTM

Wear is one of the main forms of tool failure during machining. The prediction of tool wear is of great significance for ensuring the high quality of the workpiece. In order to improve prediction accuracy of tool wear, a tool wear prediction model based on singular value decomposition (SVD) and bidirectional long short-term memory neural network (BiLSTM) is proposed. The cutting force signal is taken as the monitoring signal. Firstly, the raw cutting force signal is reconstructed by Hankle matrix, and the SVD of the reconstructed matrix is performed to extract the signal features. Then, SVD features of the current sampling period and the previous four sampling periods are taken as the input, and the tool wear prediction value at the current time is obtained based on the BiLSTM. The experimental results show that the proposed SVD-BiLSTM model can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.

Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion

Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse autoencoders and a nonlinear regression function for tool wear prediction. Multiple stacked sparse autoencoders consists of two main structures. One model is designed with multidimensional stacked sparse autoencoders, which can learn more features from different feature domains in the raw vibration signal, and another single-dimensional stacked sparse autoencoders is used for feature fusion and deeper features learning. And a modified loss function is applied that improves the learning ability. In addition, due to the good properties of tool wear process in nonstationarity and complex nonlinear, a nonlinear regression function is utilized to enhance the progressive tool wear prediction tasks. A dataset from a real manufacturing process is used to evaluate the performance of the proposed modeling framework. Experimental results show that tool wear can be predicted accurately and stably by the proposed tool wear predictive model, which outperforms the already developed methods.

Tool wear monitoring using generalized regression neural network

To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learning speed and fast convergence to the optimal results whether the sample data are small or large. The low predictive accuracy and efficiency are caused by traditionally manual adjustment of the spread parameters in generalized regression neural network and then the improved fruit fly optimization algorithm is proposed to optimize the spread parameters of regression neural network automatically. Combining the improved fruit fly optimization and generalized regression neural network, the tool wear prediction method is proposed in the paper. Various experiments are carried out to validate the proposed method and the comparison results show a good agreement. In addition, the proposed method is compared to the tool wear prediction method in the literature, and the comparison results also show that the proposed method can achieve better performance.

Relevance vector machine for tool wear prediction

Abstract In order to realize real-time and accurate monitoring of the tool wear in machining process, this paper presents a tool wear predictive model based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and relevance vector machine (RVM). The traditional methods such as partial least squares regression (PLS), artificial neural network (ANN) and support vector machine (SVM) can only provide predicted values which have no probabilistic significance. As a sparse probabilistic model, RVM can provide both the predicted value and the corresponding confidence interval (CI). However, the existence of process noises and redundancy will seriously affect the prediction accuracy and the stability of CI. As a new dimension-increment technique, KPCA_IRBF helps to weaken the negative effects of process noises and redundancy by increasing the dimensionality of monitoring features. The fused features obtained by KPCA_IRBF are more sensitive to the change of tool wear. Two different cutting experiments are carried out to verify the effectiveness of KPCA_IRBF in improving the prediction accuracy and ameliorating the CI of RVM. The experimental results show that KPCA_IRBF can reduce the root mean square error (RMSE) of RVM by more than 30% and compress the average width of CI by more than 90%. To further show the advantages of RVM, the traditional methods such as PLS, ANN and SVM are also utilized to realize tool wear prediction. This paper lays the foundation for the application of RVM to industrial field.

Trade-off analysis of tool wear, machining quality and energy efficiency of alloy cast iron milling process

For many manufacturing companies, maximizing profit is a primary goal. However, the environmental impact of the manufacturing process should be considered due to government and public pressure. Cutting tool costs and electricity costs are the two main costs of manufacturing process. Tool wear conditions can be used to measure the cutting tool cost indirectly. The objective of this research is to increase the profit of an industry by identifying trade-offs between cutting tool cost, electricity cost, and machining quality. First, an on-line tool wear prediction model was designed to study the change in cutting power due to the change in tool wear. Experimental data were collected to examine the relationships between tool wear, cutting power and surface roughness. Second, multi-objective optimization functions were established to solve trade-off analysis problems. Real-time cutting power data, surface roughness, and flank tool wear data from the experiments were analyzed in the models. Finally, grey relational analysis was applied to obtain the grey relational grade of the cutting parameters on the three optimization objectives. The relationships between these parameters were evaluated using trade-off analysis. The tool wear-cutting power-surface roughness trade-off surface was used to visualize the trade-offs among the parameters. The results of this work can help decision makers or operators design cutting parameters for different goals.

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.

A novel monitoring method for turning tool wear based on support vector machines

Tool wear monitoring is critical for ensuring product quality and productivity. This article presents a novel tool wear prediction model based on improved least squares support vector machine method, combined with leave-one-out technique and Nelder–Mead technique. Leave-one-out is applied to tune the regularization factor and radial basis function kernel parameter of least squares support vector machine for enhancing the global search ability. Nelder–Mead is applied to raise the local search ability. The optimized least squares support vector machine based tool wear prediction model is constructed by learning the highly nonlinear correlationships between tool cutting conditions and actual tool wear. The effectiveness of the proposed prediction model is validated by experiments. Compared with particle swarm optimization algorithm-based least squares support vector machine and basic least squares support vector machine, Nelder–Mead-leave-one-out-based least squares support vector machine demonstrates a better performance in prediction accuracy, generalization, robustness, and convergence. The average accuracy obtained in tests for tool wear prediction is above 97%. This model provides theoretical basis for the machining condition configuration in the actual processing.

Tool wear monitoring based on kernel principal component analysis and v-support vector regression

Machined surface quality and dimensional accuracy are significantly affected by tool wear in machining process, and severe tool wear may even lead to failing of the workpieces being processed. Tool wear monitoring is highly desirable to realize automated or unmanned machining process, which can get rid of the dependence on skilled workers. This paper mainly studies on the methods and techniques of on-line tool wear monitoring through static and dynamic cutting force signals. Sensitive signals related to tool wear are preliminarily selected by using correlation coefficient method. Kernel principal component analysis (KPCA) technique is adopted to fuse these sensitive features for improving training speed and prediction accuracy. Then, the tool wear predictive model based on v-support vector regression (v-SVR) is constructed through learning correlation between the fused features and actual tool wear. The obtained result shows that the prediction accuracy of the proposed tool wear model is proved effective beyond expectation. Besides, the proposed model still has better generalization ability even in small sample size.

A Study on Tool Wear Prediction in Ultrasonic Vibration Turning of Tungsten Carbide

The prediction of tool wear can help understand the influence of tool wear on the machining process and result, and change or grind the worn tool in time. The two-dimensional ultrasonic vibration turning method can reduce the crack of tool and decrease the negative effect on processing thus extends the tool life. In this paper, two-dimensional ultrasonic cutting theory was applied to the precision machining of tungsten carbide. With self-developed two-dimensional ultrasonic cutting device, series of cutting experiments were carried out. During cutting process, the flank wear under different cutting length was observed; flank wear situations were compared with those in traditional cutting. In order to predict the tool wear and thus heighten the machining precision, a tool wear prediction model based on time series analysis method was built in the paper. The research results show the built AR (9) time series model can predict the flank wear condition with high precision.