Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.

Tool Wear and Its Effect on Surface Roughness in Diamond Cutting of Glass Soda-lime

For the technology of diamond cutting of optical glass, the high tool wear rate is a main reason for hindering the practical application of this technology. Many researches on diamond tool wear in glass cutting rest on wear phenomenon describing simply without analyzing the genesis of wear phenomenon and interpreting the formation process of tool wear in mechanics. For in depth understanding of the tool wear and its effect on surface roughness in diamond cutting of glass, experiments of diamond turning with cutting distance increasing gradually are carried out on soda-lime glass. The wear morphology of rake face and flank face, the corresponding surface features of workpiece and the surface roughness, and the material compositions of flank wear area are detected. Experimental results indicate that the flank wear is predominant in diamond cutting glass and the flank wear land is characterized by micro-grooves, some smooth crater on the rake face is also seen. The surface roughness begins to increase rapidly, when the cutting mode changes from ductile to brittle for the aggravation of tool wear with the cutting distance over 150 m. The main mechanisms of inducing tool wear in diamond cutting of glass are diffusion, mechanical friction, thermo-chemical action and abrasive wear. The proposed research makes analysis and research from wear mechanism on the tool wear and its effect on surface roughness in diamond cutting of glass, and provides theoretical basis for minimizing the tool wear in diamond cutting brittle materials, such as optical glass.

Hardening Effect on Machined Surface for Precise Hard Cutting Process with Consideration of Tool Wear

During hard cutting process there is severe thermodynamic coupling effect between cutting tool and workpiece, which causes quenching effect on finished surfaces under certain conditions. However, material phase transformation mechanism of heat treatment in cutting process is different from the one in traditional process, which leads to changes of the formation mechanism of damaged layer on machined workpiece surface. This paper researches on the generation mechanism of damaged layer on machined surface in the process of PCBN tool hard cutting hardened steel Cr12MoV. Rules of temperature change on machined surface and subsurface are got by means of finite element simulation. In phase transformation temperature experiments rapid transformation instrument is employed, and the effect of quenching under cutting conditions on generation of damaged layer is revealed. Based on that, the phase transformation points of temperature under cutting conditions are determined. By experiment, the effects of cutting speed and tool wear on white layer thickness in damaged layer are revealed. The temperature distribution law of third deformation zone is got by establishing the numerical prediction model, and thickness of white layer in damaged layer is predicted, taking the tool wear effect into consideration. The experimental results show that the model prediction is accurate, and the establishment of prediction model provides a reference for wise selection of parameters in precise hard cutting process. For the machining process with high demanding on surface integrity, the generation of damaged layer on machined surface can be controlled precisely by using the prediction model.

Cutting Temperature and Tool Wear of Hard Turning Hardened Bearing Steel

A study was undertaken to investigate the performan ce of PCBN tool in the finish turning GCr15 bearing steel with different hardness between 30～64 HRC. The natural thermocouple was used to measure the cutting tem p erature, tool life and cutting temperature were investigated and compared. The m aterial can be heated by this instrument which using low voltage and high elec trical current, while PCBN can’t be heated by electrifying directly, so the ke ntanium layer coating over the PCBN is heated, so the PCBN is heated and its th ermoelectric property is got by this method. [TPP129,+60mm88mm,Y,PZ#] Fig.1 Effect of cutting depth and workpiec hardness on. the cutting temperatureThe objective was to determine the influence of the workpiece hardness on change s in cutting temperature and tool wear characterize. It can be found from Fig.1 that the cutting temperature show an increasing tendency with the improvement of workpiece hardness within the cutting speed scope when the workpiece hardness i s under HRC50. And on the other hand, it is found that the cutting temperature s how the downtrend with the improvement of workpiece hardness when the workpiece hardness is over HRC50. According to experimental results, the critical hard ness when turning hardened GCr15 bearing steel with PCBN tool is about HRC50. Th e wear causes of PCBN tool have been found out through taking photos on the micr o-shape of PCBN poly-laminate initial surface as well as face and flank of wea r tool and analysis on chemical elements. It is discovered that the PCBN tools a re not suitable for cutting the workpiece at nearly critical hardness, because n ear the critical hardness, PCBN wear at the highest speed. For researching the w ear rule of PCBN tool, the tool wear experiments have been carried on by using b earing steel GCr15 at hardness HRC40 and HRC60 with changing cutting speed. The indexes of tool life equations is gained under two kinds of conditions w hich are bigger than 0.6, so the effects of cutting speed on the PCBN tool are m uch less than that of carbide tool and ceramic tool.

EFFECT OF TOOL WEAR ON MICROSTRUCTURE, MECHANICAL PROPERTIES AND ACOUSTIC EMISSION OF FRICTION STIR WELDED 6061 Al ALLOY

Tool condition is one of the main concerns in friction stir welding （FSW）, because the geometrical condition of the tool pin including size and shape is strongly connected to the microstrueture and mechanical performance of the weld. Tool wear occurs during FSW, especially for welding metal matrix composites with large amounts of abrasive particles, and high melting point materials, which significantly expedite tool wear and deteriorate the mechanical performance of welds. Tools with different pin-wear levels are used to weld 6061 Al alloy, while acoustic emission （AE） sensing, metallographic sectioning, and tensile testing are employed to evaluate the weld quality in various tool wear conditions. Structural characterization shows that the tool wear interferes with the weld quality and accounts for the formation of voids in the nugget zone. Tensile test analysis of samples verifies that both the ultimate tensile strength and the yield strength are adversely affected by the formation of voids in the nugget due to the tool wear. The failure location during tensile test clearly depends on the state of the tool wear, which led to the analysis of the relationships between the structure of the nugget and tool wear. AE signatures recorded during welding reveal that the AE hits concentrate on the higher amplitudes with increasing tool wear. The results show that the AE sensing provides a potentially effective method for the on-line manitoring of tool wear.

Influence of Minimum Quantity Lubrication Parameters on Tool Wear and Surface Roughness in Milling of Forged Steel

The minimum quantity of lubrication（MQL） technique is becoming increasingly more popular due to the safety of environment.Moreover,MQL technique not only leads to economical benefits by way of saving lubricant costs but also presents better machinability.However,the effect of MQL parameters on machining is still not clear,which needs to be overcome.In this paper,the effect of different modes of lubrication,i.e.,conventional way using flushing,dry cutting and using the minimum quantity lubrication（MQL） technique on the machinability in end milling of a forged steel（50CrMnMo）,is investigated.The influence of MQL parameters on tool wear and surface roughness is also discussed.MQL parameters include nozzle direction in relation to feed direction,nozzle elevation angle,distance from the nozzle tip to the cutting zone,lubricant flow rate and air pressure.The investigation results show that MQL technique lowers the tool wear and surface roughness values compared with that of conventional flood cutting fluid supply and dry cutting conditions.Based on the investigations of chip morphology and color,MQL technique reduces the cutting temperature to some extent.The relative nozzle-feed position at 120°,the angle elevation of 60° and distance from nozzle tip to cutting zone at 20 mm provide the prolonged tool life and reduced surface roughness values.This fact is due to the oil mists can penetrate in the inner zones of the tool edges in a very efficient way.Improvement in tool life and surface finish could be achieved utilizing higher oil flow rate and higher compressed air pressure.Moreover,oil flow rate increased from 43.8 mL？h to 58.4 mL？h leads to a small decrease of flank wear,but it is not very significant.The results obtained in this paper can be used to determine optimal conditions for milling of forged steel under MQL conditions.

Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

BP-Neural-Network-Based Tool Wear Monitoring by Using Wav elet Decomposition of the Power Spectrum

In a drilling process, the power spectrum of the drilling force is related tothe tool wear and is widely applied in the monitoring of tool wear. But the feature extraction andidentification of the power spectrum have always been an unresolved difficult problem. This papersolves it through decomposition of the power spectrum in multilayers using wavelet transform andextraction of the low frequency decomposition coefficient as the envelope information of the powerspectrum. Intelligent identification of the tool wear status is achieved in the drilling processthrough fusing the wavelet decomposition coefficient of the power spectrum by using a BP (BackPropagation) neural network. The experimental results show that the features of the power spectrumcan be extracted efficiently through this method, and the trained neural networks show highidentification precision and the ability of extension.

Tool Wear Monitoring in Drilling Using Multiple Feature Fusion of the Cutting Force

This paper presents a tool wear monitoring method in drilling process using cutting force signal. The kurtosis coefficient and the energy of a special frequency band of cutting force signals were taken as the signal features of tool wear as well as the mean value and the standard deviation from the time and frequency domain. The relationships between the signal feature and tool wear were discussed; then the vectors constituted of the signal features were input to the artificial neural network for fusion in order to realize intelligent identification of tool wear. The experimental results show that the artificial neural network can realize fusion of multiple features effectively, but the identification precision and the extending ability are not ideal owing to the relationship between the features and the tool wear being fuzzy and not certain.

A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion

In order to achieve predictive maintenance of CNC machining tools and to be able to change tools intelligently before tool wear is at a critical threshold,a CNN-LSTM tool wear prediction model based on particle swarm algorithm(PSO)optimization with multi-channel feature fusion is proposed.Firstly,the raw signals of seven channels of the machining process are collected using sensor technology and processed for noise reduction;secondly,the time-domain,frequency-domain and time-frequency-domain features of each channel signal are extracted,and a sample data set of spatio-temporal correlation of traffic flow is constructed by dimensionality reduction processing and information fusion of the above features;finally,the data set is input to the CNN-LSTM-PSO model for training and testing.The results show that the CNN-LSTM-PSO model can effectively predict tool wear with an average absolute error MAE value of 0.5848,a root mean square error RMSE value of 0.7281,and a coefficient of determination R2 value of 0.9964;and compared with the BP model,CNN model,LSTM model and CNN-LSTM model,its tool wear prediction accuracy improved by 7.56%,2.60%,2.98%,and 1.63%,respectively.

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.

Investigation on tool wear and tool life prediction in micro-milling of Ti-6Al-4V

Short tool life and rapid tool wear in micromachining of hard-to-machine materials remain a barrier to the process being economically viable. In this study, standard procedures and conditions set by the ISO for tool life testing in milling were used to analyze the wear of tungsten carbide micro-end-milling tools through slot milling conducted on titanium alloy Ti-6 Al-4 V. Tool wear was characterized by flank wear rate,cutting-edge radius change, and tool volumetric change. The effect of machining parameters, such as cutting speed and feedrate, on tool wear was investigated with reference to surface roughness and geometric accuracy of the finished workpiece. Experimental data indicate different modes of tool wear throughout machining, where nonuniform flank wear and abrasive wear are the dominant wear modes. High cutting speed and low feedrate can reduce the tool wear rate and improve the tool life during micromachining.However, the low feedrate enhances the plowing effect on the cutting zone, resulting in reduced surface quality and leading to burr formation and premature tool failure. This study concludes with a proposal of tool rejection criteria for micro-milling of Ti-6 Al-4 V.

Implementation of remote monitoring system for prediction of tool wear and failure using ART2

Remote monitoring of tools for prediction of tool wear in cutting processes was considered,and a method of implementation of a remote-monitoring system previously developed was proposed.Sensor signals were received and tool wear was predicted in the local system using an ART2 algorithm,while the monitoring result was transferred to the remote system via internet.The monitoring system was installed at an on-site machine tool for monitoring three kinds of tools cutting titanium alloys,and the tool wear was evaluated on the basis of vigilances,similarities between vibration signals received and the normal patterns previously trained.A number of experiments were carried out to evaluate the performance of the proposed system,and the results show that the wears of finishing-cut tools are successfully detected when the moving average vigilance becomes lower than the critical vigilance,thus the appropriate tool replacement time is notified before the breakage.

Tool Wear Classification Using Fuzzy Logic for Machining of Al/SiC Composite Material

Tool wear state classification has good potential to play a critical role in ensuring the dimensional accuracy of the work piece and prevention of damage to cutting tool in machining process. During machining process, tool wear is an important factor which contributes to the variation of spindle motor current, speed, feed and depth of cut. In the present work, online tool wear state detecting method with spindle motor current in turning operation for Al/SiC composite material is presented. By analyzing the effects of tool wear as well as the cutting parameters on the current signal, the models on the relationship between the current signals and the cutting parameters are established with partial design taken from experimental data and regression analysis. The fuzzy classification method is used to classify the tool wear states so as to facilitate defective tool replacement at the proper time.

Performance evaluation of minimum quantity lubrication by vege-table oil in terms of cutting force,cutting zone temperature,tool wear,job dimension and surface finish in turning AISI-1060 steel

In all machining processes, tool wear is a natural phenomenon and it leads to tool failure. The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Metal cutting fluid changes the performance of machining operations because of their lubrication, cooling and chip flushing functions, but the use of cutting fluid has become more problematic in terms of both employee health and environmental pollution. The minimization of cutting fluid also leads to economical benefits by way of saving lubricant costs and workpiece/tool/machine cleaning cycle time. The concept of minimum quantity lubrication (MQL) has been suggested since a decade ago as a means of addressing the issues of environmental intru- siveness and occupational hazards associated with the airborne cutting fluid particles on factory shop floors. This paper deals with experimental investigation on the role of MQL by vegetable oil on cutting temperature, tool wear, surface roughness and dimen- sional deviation in turning AISI-1060 steel at industrial speed-feed combinations by uncoated carbide insert. The encouraging results include significant reduction in tool wear rate, dimensional inaccuracy and surface roughness by MQL mainly through reduction in the cutting zone temperature and favorable change in the chip-tool and work-tool interaction.

Influences of Tool Wear on Residual Stress and Fatigue Life of Workpiece in Hard Cutting Process

Tool wear has an important influence on the residual stress distribution on the machined surface.Also,it will influence the fatigue life of finished workpiece. In this research,the hard turning process of hardened die steel Cr12 MoV was studied by using PCBN tool with considering tool wear. Based on the numerical treatment of residual stress,the dispersion and distribution curves of different tool wear were fitted,and the influence mechanism of tool wear on the residual stress distribution of machined surface was analyzed.Based on the theory of fatigue mechanics and mathematical statistics,the mathematical model for difference of stress dispersion and fatigue life was established. The rotating and bending tests were carried out on the standard parts after cutting process for the workpiece. The influence of tool wear on fatigue life was revealed by fracture surface morphology and fatigue life study. The results provide theoretical support for control of residual stress and the fatigue property of the machined surface under the actual working conditions.

Tool Wear Prediction in Micro-end-milling with Hard Material

The applications of micro-machining have increased drastically in the last ten years. However, tools with less than 1mm diameter using for micro-mills have very short and unpredictable life when they are used to cut hard metals. In this study, preliminary design of experiment (DOE) test program was conducted to investigate and identify the factors affecting tool wear at the micro-scale with hard material. Analysis of variance (ANOVA) and Taguchi method were efficient to determine appropriate cutting condition and the effect of parameters. A simple model was also developed to predict the width of slots on the workpiece along the cutting length. The obtained results can provide the basic guidelines for parameter setting of micro-end-milling with hard material.

In-process tool wear monitoring using laser-CCD

A simple and reliabe monitoring method based on laser-CCD trigonometric measurement is Proposed for toolwear sensing in the automation of manufacturing processes, and experimental results show this method is good for in-dustrial use.

Tool Wear Estimate in Milling Operation by FEM

Many researches show that, in metal cutting process, tool wear rate depends on some cutting process parameters, such as temperature at tool face, contact pressure and relative sliding velocity at tool/chip and tool/workpiece interfaces. Finite element method(FEM) application enables the estimate of these parameters and the tool wear. A tool wear estimate program based on chip formation and heat transfer analysis is designed and compiled with Python to calculate the wear rate and volume, and update tool geometry according to the tool wear. The progressive flank and crater wears in milling operation are estimated by the program. The FEM code ABAQUS/Explicit and Standard are employed to analyze chip formation and heat transfer process.

Tool wear mechanism in turning of novel wear-resisting aluminum bronze

Tool wear and wear mechanism during the turning of a wear-resisting aluminum bronze have been studied. Tool wear samples were prepared by using M2 high-speed steel and YW1 cemented carbide tools to turn a novel high strength, wear resisting aluminum bronze without coolant and lubricant. Adhesion of workpiece materials was found on tool’s surface. Under the turning condition used in this study major wear mechanisms for turning aluminum bronze using M2 high-speed steel tool are diffusion wear, adhesive wear and plastic deformation and shear on the crater. Partial melting of high-speed steel on the rake plays a role in the tool wear also. Major wear mechanisms for turning aluminum bronze using YW1 cemented carbide tool are diffusion wear, attrition wear and sliding wear. To control the machining temperature is essential to reduce tool wear.