Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission

In modern manufacturing industry, developing automated tool condition monitoring system become more and more import in order to transform manufacturing systems from manually operated production machines to highly automated machining centres. This paper presents a nouvelle cutting tool wear assessment in high precision turning process using type-2 fuzzy uncertainty estimation on acoustic Emission. Without understanding the exact physics of the machining process, type-2 fuzzy logic system identifies acoustic emission signal during the process and its interval set of output assesses the uncertainty information in the signal. The experimental study shows that the development trend of uncertainty in acoustic emission signal corresponds to that of cutting tool wear. The estimation of uncertainties can be used for proving the conformance with specifications for products or auto-controlling of machine system, which has great meaning for continuously improvement in product quality, reliability and manufacturing efficiency in machining industry.     

Tool wear monitoring using genetically-generated fuzzy knowledge bases

Fuzzy logic is an AI method that is being implemented in a growing number of different fields. One of these applications is tool wear monitoring. The construction of a fuzzy knowledge base from a set of experimental data by a human expert however, is a time consuming task, and hence, limits the expansion of the use of this AI method. Alternatively, the fuzzy knowledge base can be automatically constructed by a genetic algorithm from the same set of experimental data without requiring any human expert. This paper compares these two fuzzy knowledge base construction methods and the results obtained in a tool wear monitoring application.     

Cutting tool wear estimation for turning

The experimental investigation on cutting tool wear and a model for tool wear estimation is reported in this paper. The changes in the values of cutting forces, vibrations and acoustic emissions with cutting tool wear are recoded and analyzed. On the basis of experimental results a model is developed for tool wear estimation in turning operations using Adaptive Neuro fuzzy Inference system (ANFIS). Acoustic emission (Ring down count), vibrations (acceleration) and cutting forces along with time have been used to formulate model. This model is capable of estimating the wear rate of the cutting tool. The wear estimation results obtained by the model are compared with the practical results and are presented. The model performed quite satisfactory results with the actual and predicted tool wear values. The model can also be used for estimating tool wear on-line but the accuracy of the model depends upon the proper training and section of data points.     

Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.     

Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear

This study deals with modeling the flank wear of cryogenically treated AISI M2 high speed steel (HSS) tool by means of adaptive neuro-fuzzy inference system (ANFIS) approach. Cryogenic treatment has recently been found to be an innovative technique to improve wear resistance of AISI M2 HSS tools but precise modelling approach which also incorporates the cryogenic soaking temperature to simulate the tool flank wear is still not reported in any open literature. In order to obtain data for developing the ANFIS model, turning of hot rolled annealed steel stock (C-45) by cryogenically treated tools treated at various cryogenic soaking temperatures was performed in steady state conditions while varying the cutting speed and cutting time. The model combined modeling function of fuzzy inference with the learning ability of artificial neural network; and a set of rules has been generated directly from experimental data. It was determined that the predictions usually agreed well with the experimental data with correlation coefficients of 0.994 and mean errors of 2.47%. The proposed model can also be used for estimating tool flank wear on-line but the accuracy of the model depends upon the proper training and selection of data points.     

基于随机模糊神经网络的刀具磨损量软测量技术

刀具磨损检测对于提高加工过程的自动化、高精度化、智能化具有重要意义．本文通 过检测电流信号基于随机模糊神经网络建立了刀具磨损量的软测量模型．该模型的创新之处 在于利用切削参数实时地调整网络的部分参数,从而可以减小切削参数与电流信号之间关系 对于刀具磨损估计的影响并且使得模型具有动态性、实时性．实验验证表明该方法是正确而 有效的．     

Tool wear estimation using an analytic fuzzy classifier and support vector machines

A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.     

Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi’s signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.     

A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

The present work attempts to study the effect of important machining variables on performance characteristics such as material removal rate and tool wear in turning of Inconel 718 using chemical vapour deposition (CVD) coated tungsten carbide (WC) tool. A three dimensional machining model using Lagrangian approach has been developed using DEFORM 3D. The machining simulation is carried out to predict the flank wear and material removal rate (MRR). Flank wear is calculated using Usui’s wear model in the simulation model. The results from simulation model are compared with experimental data generated by the use of Taguchi’s L₁₆ orthogonal array for reducing the experimental runs. Analysis of variance (ANOVA) is performed to identify the most influencing variables for both the performance characteristics. It is found that simulation results are in good agreement with experimental results. A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.     

Prediction of chatter in high-speed milling by means of fuzzy neural networks

This paper develops a new method for predicting chatter vibration in high-speed end milling using a fuzzy neural network model. Firstly, an experimental system is established. The system consists of an NC jig grinding machine with control device, a force sensor, a charge amplifier, and an FFT (fast Fourier transform) analyser. Over 10 groups of the typical experimental data are obtained under different milling tool wear states and cutting conditions. Then, because the experimental system is a nonlinear dynamic system with some fuzzy factors and too complicated to simplify it into an exact mathematical model, it is substituted by fuzzy neural networks, which are trained by using the above data. Lastly, for verifying the effectiveness of predicting self-excited chatter with the above model, some more experiments are performed. The comparison between the calculated and experimental results confirms that the method proposed in this paper could correctly predict the chatter vibration in high-speed milling. Therefore, the models of the method are reasonable and practical, and the accuracy is very high, which is significant for grasping the stable domain of high-speed milling in both theory and practice.     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

Drill flank wear estimation using supervised vector quantization neural networks

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.     

Numerical analysis of the tool wear effect in the machining induced residual stresses

Machining is a dynamic process involving coupled phenomena: high strain and strain rate and high temperature. Prediction of machining induced residual stresses is an interesting objective at the manufacturing processes modelling field. Tool wear results in a change of tool geometry affecting thermo-mechanical phenomena and thus has a significant effect on residual stresses. The experimental study of the tool wear influence in residual stresses is difficult due to the need of controlling wear evolution during cutting. Also the involved phenomena make the analysis extremely difficult. On the other hand, Finite Element Analysis (FEA) is a powerful tool used to simulate cutting processes, allowing the analysis of different parameters influent on machining induced residual stresses.The aim of this work is to develop and to validate a numerical model to analyse the tool wear effect in machining induced residual stresses. Main advantages of the model presented in this work are, reduced mesh distortion, the possibility to simulate long length machined surface and time-efficiency. The model was validated with experimental tests carried out with controlled worn geometry generated by electro-discharge machining (EDM). The model was applied to predict machining induced residual stresses in AISI 316 L and reasonable agreement with experimental results were found.     

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.     

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.     

一种新型谐振式非接触流体声发射传感器的研制

研制高灵敏度、安装使用方便、抗干扰能力强的传感器是刀具磨破损监测研究需要解决的关键技术 .本文根据刀具磨、破损监测的特点 ,研制了既可用于刀具磨损状态监测 ,也可用于刀具破损监测的谐振式高灵敏度流体声发射传感器 .对研制的流体声发射传感器性能进行了实验研究 ,结果表明传感器对刀具磨损产生的声发射信号具有较高的灵敏度 .     

离群样本划分的半监督模糊学习策略

建立一种离群样本划分的半监督模糊学习算法模型。首先,提出一种基于Hopfield参数估计的松弛条件模糊鉴别分析算法,重新定义每一个样本的隶属度,并在特征抽取的过程中,根据隶属度对散布矩阵的定义所做的贡献获得每个样本相应的类别信息,由此获得普通样本分类信息。其次,根据样本隶属度的分布信息划分出离群样本空间,将普通样本分类结果作为离群样本聚类的先验类属信息,并对该空间样本提出一种新的半监督模糊学习策略进行动态聚类。该算法同时具备了监督学习和无监督学习方法的优势,克服了传统聚类缺乏类过程知识的缺点,可以有效地解决特征空间中特殊样本的分类问题。性能分析表明,该方法优于单一的特征抽取方法,在NUST603、ORL、XM2VTS和FERET人脸数据库上的识别性能均得到有效提高。     

Research on multi-signal milling tool wear prediction method based on GAF-ResNext

A key aspect impacting the quality and efficiency of machining is the degree of tool wear. If the tool failure is not discovered in time, the quality of workpiece processing decreases, and even the machine tool itself may be harmed. To increase machining quality, efficiency and facilitate the intelligent advancement of the manufacturing industry, tool wear prediction is crucial. This research offers a multi-signal tool wear prediction method based on the Gramian angular field (GAF) and depth aggregation residual transform neural network (ResNext), enabling fast and accurate tool wear prediction. Specifically, the required one-dimensional signal is obtained through preprocessing including intercepting, splicing and wavelet threshold denoising of the force and vibration signals, and GAF is used to encode the obtained one-dimensional signal to generate a (224 × 224) data matrix. ResNext automatically extracts the features of the data matrix, establish the relationship between features and tool wear, and creates a tool wear prediction model based on GAF-ResNext. The ability of this method to predict tool wear has been trained and tested by milling experimental data. The experimental findings demonstrate the real-time, accuracy, dependability and universality of this method. This method has a better effect when compared to other research methods. The study's findings can boost machining productivity and offer technical support for intelligent tool wear early warning and intelligent manufacturing.     

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.     

Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation

We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling processes. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.