Identification Of Tool Wear State Research Articles

Tool Wear State Identification Based on the IWOA-VMD Feature Selection Method

Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.

Research on tool wear state identification method driven by multi-source information fusion and multi-dimension attention mechanism

Cutting tool wear condition monitoring technology is the key technology of advanced manufacturing system and a crucial component of machining. The stage of the tool wear has a direct impact on performance of the workpiece and efficacy of machine tool. However, the duplication and lack of wear information and the fuzzy area of the tool wear transition stage are the key factors contributing to the incorrect estimation of the tool wear state when extracting cutting tool wear feature information. As a consequence, this study presents a data fusion from several sources-based intelligent tool wear state detection technique. The fusion of data from several sources can effectively realize the complementarity of machining information. This provides the model with more comprehensive identification data. The mapping between wear state and wear characteristics is precisely established. To address these issues, the attention mechanism of channel and spatial latitude is integrated into the feature extraction. The model that was constructed in the present investigation has a comprehensive identification accuracy of 0.982. The F1 score of initial, normal and severe wear stage of tool wear are 0.977, 0.968 and 0.993, which are better than other models. Experiments show that the identification method proposed in this study may provide accurate tool wear condition identification based on machining process data, allowing for more flexible and precise cutting tool change decisions in machining.

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization.

Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of features most relevant to the tool wear state is screened out by the support vector machine recursive feature elimination (SVM-RFE). Utilizing these selected features, we propose a tool wear state identification model, which utilizes an improved northern goshawk optimization (INGO) algorithm to optimize the support vector machine (SVM), hereby referred to as INGO-SVM. The simulation tests reveal that INGO demonstrates superior convergence efficacy and stability. Furthermore, a milling wear experiment confirms that this approach outperforms five other methods in terms of recognition accuracy, achieving a remarkable accuracy rate of 97.9%.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency, so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status. Firstly, the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology, and the features related to the tool wear status are extracted in the time domain, frequency domain and time-frequency domain to form a feature sample matrix; secondly, the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types: slight wear, normal wear and sharp wear to construct a target Finally, the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status. The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%; and compared with BP model, CNN model and SVM model, the accuracy indexes are improved by 9.48%, 3.44% and 1.72% respectively, which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

An unsupervised online monitoring method for tool wear using a sparse auto-encoder

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels.

Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification

On-machine monitoring of tool wear in machining processes has found its importance to reduce equipment downtime and reduce tooling costs. As the tool wears out gradually, the contact state of the cutting edge and the workpiece changes, which has a significant influence on the vibration state of the spindle. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of dynamic signals, which requires expert knowledge and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. In this paper, we present a novel intelligent technique for tool wear state recognition using machine spindle vibration signals. The proposed technique combines derived wavelet frames (DWFs) and convolutional neural network (CNN). Constructed based on dual tree wavelets, DWF are equipped with merits of centralized multiresolution and nearly translation-invariance. In this method, DWFs are employed to decompose the original signal into frequency bands of different bandwidths and different center frequencies, which are more pronounced as the tool wears. Further, the reconstructed sub-signals are stacked into a 2-D signal matrix to match the structure of 2-D CNN while retaining more dynamic information. The 2-D convolutional neural network is utilized to automatically recognize features from the multiscale 2-D signal matrix. End-milling experiments were performed on a S45C steel workpiece at different machining parameters. The experiment results of the recognition for tool wear state show the feasibility and effectiveness of the proposed method.

Hidden semi-Markov model-based method for tool wear estimation in milling process

This paper presents a new method for tool wear estimation in milling process by utilizing the hidden semi-Markov model (HSMM). HSMM differs greatly from the standard hidden Markov model (HMM) in state duration distribution. The model structure and corresponding parameters of HSMM can be easily determined without optimization. Two groups of experiments are carried out to prove the effectiveness of the HSMM-based method by recurring to the Gamma distribution. Five types of time-domain features that characterize tool wear states are extracted from the cutting force signals during milling process. The extracted signal features are utilized to realize tool wear estimation by means of HSMM and some other published methods, respectively. The experimental and analytical results show that the HSMM-based method can reach higher accuracy for tool wear estimation. Besides, the consuming time of HSMM for the identification of tool wear state is less than 0.05 s, which makes tool wear monitoring in industrial environment become more realistic and operable.

Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining

In the cutting zone of a machining process, several variables are influenced by process conditions: cutting force, vibrations, temperature, acoustic emission, power absorption. Some variables, useful for process monitoring, can be measured by sensors installed on the machine tool. However, when assessing a particular process variable, a single sensory source may not be able to meet all the requirements. A solution is sensor data fusion, the purpose of which is to combine sensory information from disparate sources so that the resulting intelligence is reinforced. Multi-sensor signal processing provides for the extraction and selection of signal features, relevant for the machining monitoring scope, that are assembled into sensor fusion pattern feature vectors functional for pattern recognition through knowledge based methods. Cognitive paradigms, such as artificial neural networks, can map input information fed by pattern feature vectors to output determinations for decision making on machining process conditions, including the adoption of corrective actions. Application cases of multi-sensor monitoring of machining process conditions investigated at the Fh-J_LEAPT Naples are reported with reference to: (a) workpiece residual stress assessment in turning of nickel base alloys; (b) tool wear state identification in machining of fiber reinforced composites; (c) chip form control in turning of C steel.

Study on the Identification Method of Tool Wear State Based on BP Neural Network Optimized by Genetic Algorithm

According to the un-stationary feature of the acoustic emission signals of tool wear, a tool wear state identification method based on genetic algorithm and BP neural network was proposed. The method reconstructed the acoustic emission signals and calculated the singular spectrum. And the feature vectors were reconstructed based on the singular spectrum. BP neural network was optimized by genetic algorithm. The weights of BP neural network and the thresholds were optimized originally to get more optimal solutions in solution space. Then the more optimal solutions were put into BP neural network to identify the tool wear state by the optimized classification machine. The study indicated that this method can make an accurate identification of tool wear state and should be widely used.

Identification Method of Tool Wear Based on Locally Linear Embedding and Support Vector Machine

Aiming at the nonlinear characteristics of the tool wear Acoustic Emission signal, tool wear state identification method is proposed based on local linear embedding and vector machine supported. The local linear embedding algorithm makes high dimensional information down to low dimension feature space through commutation, and thus to compress the data for highlighting signal features. This algorithm well compensates for the weakness of linear dimension reduction failing to find datasets nonlinear structure. In this paper, acoustic emission signal is firstly made by phase space reconstruction. Using local linear embedding method, the high dimension space mapping data points are reflected into low-dimensional space corresponding data points, then extracting tool wear state characteristics, and using vector machine supported classifier to identify classification of the tool wear conditions. Experimental results show that this method is used for the exact recognition of the tool wear state, and has widespread tendency.

Surface textural analysis using acousto optic emission- and vision-based 3D surface topography—a base for online tool condition monitoring in face turning

In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. Thus, by monitoring the machined surface topography of the workpiece and extracting the relevant information the cutting process and tool wear state should be able to be monitored and quantified. But the effects of vibrations have been paid less attention. The work in the present paper is divided into two parts. First part consists of a data acquisition and signal processing using acousto optic emission sensor (i.e., laser Doppler vibrometer) for online tool condition monitoring and the second part of the work presents the surface topography analysis of machined surfaces during the progression of the tool wear. Most of the work presented is also a study where surface metrology is being used to measure all aspects of the machining in combination with an online metrology tool. The encouraging results of the work pave the way for the development of a real-time, low cost, and reliable tool–condition–monitoring system. A high degree of correlation is established between the results of the acousto optic emission signal- and vision-based surface textural analysis in identification of tool wear state.

Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach

In automated manufacturing systems, one of the most important issues is accurate detection of the tool conditions under given cutting conditions so that worn tools can be identified and replaced in time. In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. But the effects of vibrations have been paid less attention. In the present paper, an investigation is presented of a tool condition monitoring system, which consists of a fast Fourier transform preprocessor for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. A fast Fourier transform (FFT) can decompose AOE signals into different frequency bands in the time domain. Present work uses a laser Doppler vibrometer for online data acquisition and a high-speed FFT analyser used to process the AOE signals. The generation of the AOE signals directly in the cutting zone makes them very sensitive to changes in the cutting process due to vibrations. AOE techniques is a relatively recent entry into the field of tool condition monitoring. This method has also been widely used in the field of metal cutting to detect process changes like displacement due to vibration and tool wear, etc. In this research work the results obtained from the analysis of acousto-optic emission sensor employs to predict flank wear in turning of AISI 1040 steel of 150 BHN hardness using Carbide insert and HSS tools. The correlation between the tool wear and AOE parameters is analyzed using the experimental study conducted in 16 H.P. all geared lathe. The encouraging results of the work pave the way for the development of a real-time, low-cost, and reliable tool condition monitoring system. A high degree of correlation is established between the results of the AOE signal and experimental results in identification of tool wear state.