Tool Wear Values Research Articles

Development of a Low Cost MQL Setup for Turning Operations

In this study, the effect of MQL application on the tool wear, surface roughness and chip formation in turning Aluminum alloy 6061 is investigated. Experiments were carried out by plain turning of an Aluminum bar with varying depth of cut, cutting speeds (spindle speed) and cutting environments (Dry, Wet and MQL). A newly designed, cost efficient and portable MQL setup was developed for this study. For each experimental trial, five passes were carried out in order to promote the formation of tool wear. After each pass, the tool surface was examined under a microscope and the surface roughness was measured using a stylus based surface tester. The extent of tool wear from each pass is measured by using ImageJ software. Chips were collected after the five passes and were physically examined. The newly designed MQL delivery system was successful in delivering a fine mist of lubricant at the cutting point. MQL lubrication provided lower surface roughness and tool wear values compared to wet and dry conditions due to effective temperature reduction and improved lubrication penetration of cutting zones, as well as better chip flushing. Chip formed under MQL conditions had reduced chip thickness due to reduced temperatures. MQL was found to have the greatest positive impact on tool wear and surface roughness parameters based on ANOVA results.

Analysis of cutting parameters and cooling/lubrication methods for sustainable machining in turning of Haynes 25 superalloy

While the use of cutting oils in machining operations facilitate material removal, the use of these oils are questioned based on the risks they pose for operator health and the environment. As an alternative to the excessive use of cutting oils, the Minimum Quantity Lubrication (MQL) method aims to reduce the use of cutting fluids as a step in achieving clean, environmentally friendly, and sustainable manufacturing. In this study, the machinability of cobalt-based Haynes 25 superalloy, which is a difficult-to-machine alloy used in strategic applications, was investigated under three separate cutting methods (dry, conventional cooling and lubrication, and minimum quantity lubrication). The experiments were conducted on a CNC turning machine using uncoated carbide cutting tools using four separate cutting speeds (15 m/min, 30 m/min, 45 m/min, and 60 m/min), three separate feed rates (0.08 mm/rev, 0.12 mm/rev and 0.16 mm/rev), and a fixed depth of cut value (1 mm). To determine the relationships among machining parameters and outputs, tool wear (VN) and surface roughness (Ra) values were measured. Additionally, the wear mechanisms acting on the cutting inserts were determined using scanning electron microscope (SEM). Following the conclusion of experiments, the Taguchi's signal to noise ratio (S/N) analysis was used to establish the optimal set of cutting parameters. In conclusion, when the MQL method was employed in conjunction with high pressure, the amount of oil used was reduced while the machinability of the material was improved. Tests conducted under all three methods of cutting revealed poor surface roughness at low cutting speeds, and high tool wear at high cutting speeds.

Effect of tool material on tool wear and delamination during machining of particleboard

The tool material and the parameters of machining are the main conditions which decide the quality of particleboard surface. The objective of the study was to investigate the wear of a carbide cemented bit and high-speed steel bit during machining of melamine-faced particleboard. Analysis of variance (ANOVA) was implemented to find the significance of the effect of machining parameters and tool wear on the value of the delamination factor A del. The delamination factor A del is defined as the ratio of area of delamination (mm2) to the measured length of the test piece (cm). To determine the durability of the cutting tool a geometric criterion was adopted, i.e., maximum wear of the tool flank. We observed a clear impact of cutting speed on the length of the tool life. This relation was manifested as a decrease in the tool life with increasing cutting speed. It was also found that a reduction in the cutting speed increases the area of delamination, leading to an increase in the value of the A del factor. It was especially visible for higher values of tool wear. In the case of feed per tooth we observed no clear effect on intensifying the delamination phenomenon.

Influence of Tool Wear on Residual Stresses When Turning Inconel 718

This paper analyzes the effect of tool wear on residual stresses when turning. Inconel 718 discs were machined for prolonged periods at several cutting speeds, feed-rates and depth of cut. Tests were interrupted to measure and relate tool wear with the subsequent residual stress measurements. The discussion of experimental results is supported by orthogonal cutting simulations. It was found a critical tool wear where tensile surface residual stresses were maximum, decreasing for lower and higher values of tool wear. Nevertheless, it was observed that the compressive residual stress layer increased with increasing tool wear.

Tool wear and form accuracy in ultrasonically machined microchannels

Quantifying wear on the basis of weight at micro levels may be erroneous. Moreover, information about wear distribution cannot be obtained on the basis of weight monitoring. Thus, quantification on the basis of linear measurements is an effective solution at micro levels. This paper presents a mathematical model for quantifying wear measured in two dimensions and subsequently converting it to a three dimensional wear model. The efficacy of the model was verified through machining data from ultrasonic micromachining of glass with tungsten carbide tools. The tool wear values obtained for the model was then correlated with the form accuracy of the machined microchannel which were found reasonable. Further, the effects of different process parameters like abrasive concentration, power rating, abrasive size and workpiece feed were studied on the tool wear behaviour and presented vis-a-vis microchannel form accuracy. High power rating and high abrasive concentration resulted in more tool wear and increased form inaccuracy; coarse abrasives resulted in higher form inaccuracy. Longitudinal wear resulted in decreased microchannel depth while lateral wear led to tapered microchannel walls and edge rounding wear yielded rounded corners in the fabricated microchannels.

Improving Machinability in Conventional Turning of Ti-6Al-4V Using Work Piece Pre-Heating with Standard Cutting Tools

Machining hard to machine materials using conventional method of machining has proved to be very costly as these materials greatly affect the tool life because of poor machinability. One material that requires considerable study is Titanium which is a relatively lightweight material and provides excellent mechanical properties. The major problems in machining Titanium Alloys are the high cutting temperatures and rapid tool wear. Machining of Titanium using techniques like Laser Assisted Machining and Plasma Assisted Machining have proven to give high productivity rates, but the costs associated are very high. The main objective of this work is to develop a method for improving the machinability of Ti-6Al-4V using Work Piece Pre-Heating technique by using Conventional Machining with standard tools. Design of experiments was performed using Taguchi’s robust design. The machining operation was performed at elevated temperatures using oxy-acetylene flame. The tools used are Coated and Uncoated Carbide Tools. Based on the tool wear values obtained with different cutting conditions, it is concluded that this technique is feasible with the coated and uncoated carbide tools to machine titanium components commercially.

Estimating the remaining useful tool life of worn tools under different cutting parameters: A survival life analysis during turning of titanium metal matrix composites (Ti-MMCs)

Utilizing the full tool life capacity of cutting tools has always been a concern, due to the considerable costs associated with suboptimal replacement of tools. Reliable reuse of worn inserts could significantly reduce machining costs. This study aims at estimating the remaining useful life of worn cutting inserts under multiple cutting parameters during turning of Ti-MMCs, using the actual tool wear value of the worn tool as the input data. A proportional hazards model with a Weibull baseline is developed. Maximum flank wear length at the transition point between the second and third state of tool wear is chosen as the failure criteria. Tool wear, cutting speed and feed rate are considered as the covariates of the model. The reliability and hazard functions are calculated from the model and are utilized to obtain the mean residual life of inserts under different cutting parameters and tool wear levels. The accuracy of the model is validated using experimental data. The results confirmed the validity and reliability of the model.

Multi-scale hybrid HMM for tool wear condition monitoring

In a machining system, accurate tool wear condition monitoring is paramount for guaranteeing the quality of the workpiece and tool life. Cutting force signal is the most commonly used signal to depict the tool wear variation during the machining process. In this paper, a novel approach for tool wear condition monitoring is proposed, which is based on the multi-scale hybrid hidden Markov model (HHMM) analysis of cutting force signal. The proposed approach captures the deeply mined information of tool wear states and holds an accurate tool wear value monitoring performance from both local and global analyses point of view. The local model deals with the wavelet coefficients of cutting force in different frequencies as a cross-twist Markov depended structure within instant time resolution, which reflects the tool wear state feature from frequency dimension. The global model depicts the long time dynamical degradation of tool wear condition combined with the local model as a composite structure. Experimental studies on CNC turning of nickel alloy, Inconel 718, show that the proposed HHMM approach is efficient in tool wear monitoring and outperforms the single hidden Markov model (HMM).

The Dependence of Surface Quality on Tool Wear of Circular Saw Blades during Transversal Sawing of Beech Wood

The article deals with the influence of the tool wear of several circular saw blades, each with different numbers of teeth (24, 40, and 60), on the quality of a machined wood surface. The surface quality was evaluated based on the surface roughness, which was represented by the surface arithmetical mean deviation Ra. To achieve the conditions of manual sawing, the saw blade was shifted into the cut with a constant feed force of 15 N. The results showed that the 40-tooth saw blade obtained the most suitable results; it exhibited the longest sawn distance and reached moderately good values of tool wear. The average values of surface roughness ranged from 3.9 to 14.5 µm, and the saw blade wear increased proportionally with sawn distance. The tool wear of the saw blade had no unambiguous effect; increase in tool wear did not lead to a deterioration of surface quality.

Applying Minimum Quantity Lubrication (MQL) Method on Milling of Martensitic Stainless Steel by Using Nano Mos2 Reinforced Vegetable Cutting Fluid

Martensitic stainless steel materials have provided some benefits for aerospace, automotive, hydroelectric engines, cutlery, defense, power hand tools, pump parts, valve seats, chisels, bushings, ball bearings, sporting equipment industry, dental and surgical instruments etc. due to their hardness, strength, and wear resistance. Machining operations such as milling, turning, drilling can be applied to give them the final form. But, these kinds of steels are specified as hard-to-machine materials owing to their high strength, low thermal conductivity and work hardening tendency during machining operations. However, martensitic stainless steels can be machined by using cutting fluids which are environmentally hazardous, unhealthy, and costly. In this study, minimum quantity lubrication (MQL) method was applied by using commercial vegetable cutting fluid and 1%wt. of nano MoS2 (Molybdenum Disulphide) particles reinforced vegetable cutting fluid during milling of AISI 420 martensitic stainless steel with uncoated Tungsten Carbide (WC) cutting tool and the sustainable milling operation was performed. The experiments were carried out at constant cutting speed, feed, and depth of cut. Two different amounts of nanofluids – pressure air mist supplied by MQL system were applied as 20ml/h and 40ml/h. In the consequence of milling operations, initial tool wear and surface roughness were investigated. According to the experimental results, the MQL method reduced the tool wear and surface roughness. In addition, minimum tool wear and surface roughness values were obtained in nano MQL milling at 40ml/h MQL flow rate.

Tool condition monitoring (TCM) using neural networks

Cutting tool conditions significantly influence the quality and precision of the machined parts. With the ability to monitor the cutting tool condition, machining quality can be maintained and catastrophic failure can be eliminated. In this manner, production automation can be achieved. Therefore, tool condition monitoring (TCM) is extremely important to achieve high quality and automation of boring processes. Neural networks have been widely used in condition monitoring. Counterpropagation neural networks (CPNs), which are based on competitive learning, have been utilized in TCM in this research for high quality and automated boring. The inputs of the CPNs were the indexes acquired from three-axis cutting force data. The output was either the tool state or the value of tool wear. Seventy CPN network structures have been utilized for both real-time recognition and real-time measurements. The performance of the CPNs for TCM depends on the network structures. The results of this research are exceedingly successful. Real-time recognition of tool states showed excellent results, using a 2 × 30 × 1 CPN, of being able to predict tool states real time with 100 % accuracy. Real-time measurements can achieve a minimum error of 8.46 % using a 3×69×1 CPN, which is sufficient for continuous assessment of the tool degradation. Control actions can be taken to stop the boring process in order to avoid catastrophic failure and to enhance quality and automation of the boring process.

1701 Cutting Condition Monitoring in Milling Operation with Micro End mill

In this paper, a cutting force measurement technique by using image processing was proposed to optimize cutting process and detect abnormality in cutting operation. In the technique, we captured images of end mill by a small CCD camera and mirrors during cutting process from x and y direction to determine deflection of a tool and then estimated cutting force in x-y plane. In optimization, relation between cutting force and cutting volume was formulated and optimization effect was verified by the proposed equipment. In detection of abnormality in cutting operation, relation between tool wear and RMS value of cutting force was estimated. Finally, we optimized cutting condition and detected abnormality in cutting operation very well.

Experimental investigation of precision turning of Monel K-500 under dry conditions

In this paper, final surface accuracy in turning the super alloy Monel K-500 is studied. The experiments were conducted on the basis of the design of experiment methodology considering four inputs of tool nose radius, feed rate, depth of cut, and cutting speed, and three outputs of surface roughness, dimensional deviation, and tool wear. The aim of this work is to identify these three phenomena to achieve a desirable machined surface with acceptable finishing and the least deviation from nominal dimensions under different parametric conditions. It was observed that the quality of the machined surface in the direction of the machining length is not constant and, in some trials, the values of Ra increase considerably at the end of the machining length. The results show that cutting speed can improve surface accuracy, in a way that the more the cutting speed, the less the dimensional deviation. Less depth of cut and tool radius affect dimensional deviation as well. Although it has a small effect on dimensional deviation, feed rate plays the most important role in controlling tool wear. Finally, on the basis of Grey relational analysis, a simultaneous optimization is carried out on surface roughness, dimensional deviation, and tool wear values. In order to minimize these responses, optimal parametric conditions are presented. A satisfying correspondence was observed between the predicted results and the confirmation observations.

Tool wear model based on least squares support vector machines and Kalman filter

In this study, the least squares support vector machines (LS-SVM) and Kalman filter (KF) technique are used to establish the tool wear estimation model. Tool wear prediction model, based on LS-SVM, is given to describe the mapping relationship between input–output factors. The cutting conditions (feed rate, cutting speed, and depth of cut), cutting time, and wear position constitute the input factors and tool wear is the output parameter of the model. In order to improve the accuracy of the LS-SVM results, the KF technique is used to update the tool wear estimated results of LS-SVM-based model, which is called the LS-KF model, according to the measured tool wear values. Experiment work is performed on machining center for cemented carbide ball-end cutter cutting stainless steel. Those two models (LS-SVM model and LS-KF model) are applied to the actual milling machining to verify their performance. Results show that predicted tool wear based on the proposed LS-KF model has more precision than that of LS-SVM model.

Investigation of the effects of alumina nanoparticles on spur gear surface roughness and hob tool wear in hobbing process

Hobbing process is one of the machining methods in manufacturing spur gears. In machining processes, selecting a suitable lubricant is one of the significant factors in enhancing machining productivity and improving the characteristics of manufactured workpieces. This study has used the nanolubricant including alumina nanoparticles dispersed in mineral-based oil 25W-50 in hobbing process. Then, hobbing process using the lubricant including both nanoparticles, and none has been done utilizing with the similar hob tools. Two spur gears with DIN1.7131 material have been manufactured using each of two lubricants. Then, the hob tool wear and surface roughness values of manufactured spur gears were investigated. Comparing the results show that using lubricant including alumina nanoparticles in hobbing process causes an expressive decrease in the hob tool crater and flank wear. Also, arithmetic surface roughness value in spur gears manufactured with nanolubricant has decreased.

Tool wear prediction considering uncovered data based on partial least square regression

Tool wear prediction plays an important role in guaranteeing the workpiece quality and improving the production efficiency. However, because of the uncertainty and complexity of tool wear process, it is hard to ensure that the samples related to all tool wear values can be collected during the training stage. Therefore, the accuracy of tool wear prediction for these uncovered data will deteriorate severely. In this paper, partial least square regression is presented to realize the tool wear prediction based on force signal. The main characteristic of this method is that the regression analysis is in the principal component space so that the multicollinearity between explanatory variables can be avoided effectively. Side milling experiment was carried out to validate the effectiveness of the proposed model. The analysis and comparison under different number of uncovered data show that the partial least square regression based tool wear prediction is more accurate.

Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models

The possibility of predicting the amount of the tool wear in machining processes is an interesting topic for industries, since tool wear affects surface integrity of the final parts and tool life is strictly connected with substitution policy and production costs. The definition of models able to correctly forecast the tool wear development is an important topic in the research field. For this reason in the present work, a comparison between response surface methodology (RSM) and artificial neural networks (ANNs) fitting techniques in tool wear forecasting was performed. For developing these predictive models, experimental values of tool wear, obtained by longitudinal turning operations with variable cutting parameters, were collected. Once selected, the best configuration of the two previously mentioned techniques, the resultant errors with respect to experimental data were estimated and then compared. The results showed that the developed models are able to predict the amount of wear. The comparison demonstrated that ANNs give better approximation than RSM in the prediction of the amount of the flank wear (VB) and of the crater wear (KT) depth. The obtained results are interesting not only from a scientific point of view but also for industries. In fact, it should be possible to implement the best model into a production manager software in order to correctly define the tool change during the lot production.

Research of PCBN Tool Wear Mechanism in Turning Ni-Based Superalloy

This paper mainly focus on the researches of tool wear mechanisms in turning Ni-based superalloy GH706 with PCBN insert. First, the process of flank face wear and rake face wear are studied by observing the pictures of tool wear. There are notch wear and flank wear on the flank face, and there are built-up-edge and micro-chipping on the rake face. Secondly, tool wear mechanisms are researched by combining the tribology and chemistry. A phenomenon called furrow effect cause flank face wear, and there are some chemical reactions on the rake face in cutting process. Lastly, in order to make sure a tool wear value which may be as a standard of PCBN tool in turning GH706, the relationships of cutting forces and flank face wear VB are showed by drawing the curves of cutting force values along VB, which indicate cutting forces turn up at the point where VB =180μm.

Correlation study of tool flank wear with machined surface texture in end milling

Indirect tool condition monitoring technique using surface texture analysis is gaining a parallel improvement with the advances of digital image processing techniques with the advent of high- end machine vision systems for fulfilment of high product quality. In this work, condition monitoring of HSS mills and coated carbide milling inserts has been performed by analyzing the resulting end-milled surface images using image texture analyses. The machined surface images were pre-processed by recovering them from inhomogeneous illumination and then two texture analysis methods, namely, gray level co-occurrence matrix (GLCM) and run length statistical (RLS) techniques were applied on the pre-processed images. Texture descriptors obtained have been highly correlated with the trend of flank wear. Finally a selection of texture features, namely, contrast and GLN, has been made within those extracted texture features for best correlation with tool wear values.

Tool wear monitoring based on cointegration modelling of multisensory information

A new method based on the cointegration theory is presented in this article to realise tool wear monitoring based on multisensory information. The main characteristic of cointegration modelling is that it can reflect the long-run equilibrium relationship of non-stationary time series, thereby avoiding the appearance of spurious regression. Monitoring of milling process was carried out by installing two dynamic force sensors in different directions. The cointegration model is constructed to describe the relationship between the tool wear value and the relevant features extracted from the sensory signal. For comparison, the root mean square error (RMSE), mean error (ME) and residual error distribution are utilised to evaluate the prediction accuracy of the cointegration model and the multiple linear regression (MLR) method. The monitoring and comparison results show that the proposed method can avoid the spurious regression effectively and predict the tool wear more accurately. This method casts new light on the accurate prediction of tool wear in the industrial environment.