Analytical techniques in fundamental tool wear research

As no systematic work evaluating the usefulness of the various analytical techniques available for fundamental tool wear research has been reported, an attempt has been made to evaluate the advantages and limitations of electron and ion beam instruments for quantitative analysis of the chemical and structural changes taking place in cemented carbide tools as a result of the metal cutting process.     

The dependence of tool overhang on surface quality and tool wear in the turning process

In the turning process, the importance of machining parameter choice is increased, as it controls the surface quality required. Tool overhang is a cutting tool parameter that has not been investigated in as much detail as some of the better known ones. It is appropriate to keep the tool overhang as short as possible; however, a longer tool overhang may be required depending on the geometry of the workpiece and when using the hole-turning process in particular. In this study, we investigate the effects of changes in the tool overhang in the external turning process on both the surface quality of the workpiece and tool wear. For this purpose, we used workpieces of AISI 1050 material with diameters of 20, 30, and 40 mm; and the surface roughness of the workpiece and tool wear were determined through experiments using constant cutting speed and feed rates with different depth of cuts (DOCs) and tool overhangs. We observed that the effect of the DOC on the surface roughness is negligible, but tool overhang is more important. The deflection of the cutting tool increases with tool overhang. Two different analytical methods were compared to determine the dependence of tool deflection on the tool overhang. Also, the real tool deflection values were determined using a comparator. We observed that the tool deflection values were quite compatible with the tool deflection results obtained using the second analytical method.     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

Influence of the tool corner radius on the tool wear and process forces during hard turning

Hard turning has become an alternative machining process for grinding processes of hardened steels. One challenge during hard turning is the increasing wear during the operation time of the tool and the hereby influenced workpiece surface and subsurface properties. This causes unfavorable changes of the microstructure and residual stress state or rather damages of the subsurface. Important factors are the contact conditions between the tool and the workpiece. The width of flank wear land influences the size of the passive force significantly. This has a direct impact on the subsurface properties of the workpiece. One solution is to modify the contact conditions and thereby the specific mechanical and thermal loads that are applied to the tool as well as to the workpiece. This article presents an experimental approach of modified corner radius geometry of cutting tools for hard turning processes. Hereby, the size and direction of the contact length of the cutting edge are adjusted as well as the load impact during machining. The aim is to reduce the tool wear performance. The results show the potential of the load-specific tool design concerning the tool wear and the workpiece subsurface properties. Furthermore, a new approach for predicting the process forces during hard turning is presented.     

The monitoring of flank wear on the CBN tool in the hard turning process

In precision hard turning, tool flank wear is one of the major factors contributing to the geometric error and thermal damage in a machined workpiece. Tool wear not only directly reduces the part geometry accuracy but also increases the cutting forces drastically. The change in cutting forces causes instability in the tool motion, and in turn, more inaccuracy. There are demands for reliably monitoring the progress of tool wear during a machining process to provide information for both correction of geometric errors and to guarantee the surface integrity of the workpiece. A new method for tool wear monitoring in precision hard turning is presented in this paper. The flank wear of a CBN tool is monitored by feature parameters extracted from the measured passive force, by the use of a force dynamometer. The feature parameters include the passive force level, the frequency energy and the accumulated cutting time. An ANN model was used to integrate these feature parameters in order to obtain more reliable and robust flank wear monitoring. Finally, the results from validation tests indicate that the developed monitoring system is robust and consistent for tool wear monitoring in precision hard turning.     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

Modeling and predicting for surface topography considering tool wear in milling process

This paper presents a model for the prediction of surface topography considering tool wear during the milling process. First, the cutting edge path equation, which can be transformed into equivalent polynomial equations and solved for discrete positions along the feed direction, is established including the effect of tool wear. Then, cutting edge is divided into a series of cutting points and an algorithm is proposed to determine the range of divided position angle. Finally, surface topography model is established based on the established cutting path equation, the range of position angle, the calculated cutting time, and spiral lag angle. By using this model, surface topography generation is simplified with respect to other models in literature and the modeling method of surface topography does not need to mesh the workpiece and the model can easily be extended to include other factors on surface generation. Based on the established surface topography model, an algorithm is proposed to simulate generation of surface profile in milling operation. Experimental work and validation of the established model is performed on a five-axis milling center by using stainless steel 1Cr18Ni9Ti and cemented carbides milling cutter. Cutting test results about the topography generation of the plane and cylindrical surface show good agreement with model predictions.     

A new tool wear estimation method based on shape mapping in the milling process

In the milling process, tool wear has a great influence on product machining quality, especially for a difficult-to-cut material. In this paper, a new approach based on shape mapping is proposed to acquire tool wear in order to establish an off-line tool wear predicting model for assessing the degree of wear and remaining useful life. The new approach maps tool wear shape into a metal material by milling holes mode after finishing each of the machining experiments. The metal material has low influence on tool wear compared to the experimental material. Thus, a series of mapped holes, which can represent the worn tool information, are formed on the metal material when finishing all milling experiments. These mapped holes on the metal material are analyzed according to all types of milling cutters in order to establish the relationship between the characteristic parameters of these mapped holes and tool wear. According to the established relationship, the characteristic parameters of these mapped holes are measured on the coordinate measure machine. The tool wear of each machining experiment can be obtained from the measured characteristic parameters of these mapped holes. The new tool wear estimation method does not require the stoppage of the machine tool and the removal of the cutter to measure tool wear in the process of conducting tool wear experiments. The new method can increase the machine tool efficiency of tool wear machining experiments and provide an efficient way to acquire tool wear in the process of establishing an off-line tool wear predicting model. In order to verify the new tool wear estimation method, a series of machining experiments were conducted on the five-axis machining center for cemented carbide cutting tool milling stainless steel. Experiments show that the shape mapping strategy of tool wear can allow for an effective assessment of tool wear and indicate good correlation with the expected wear characteristics and easily conduct tool wear experiments.     

Experimental examination of the wear behaviour of the VAlN tool coating by strip drawing process

Resistance to wear, and therefore the lifetime of forming tools, can be increased by surface functionalisation using novel, multifunctional coatings. Thereby, the tribological requirements on the coating are an essential factor. Within the scope of the research work presented here, tribological examinations were carried out on a metastable vanadium aluminium nitride (VAlN) tool coating when drawing the high-strength sheet metal material DP 800. It was shown that the wear of the VAlN tool coating can already be determined at stable frictional behaviour (μ?<?0.085). The wear analysis was carried out considering the topography and change in hardness of the tool surface during the drawing path of 110,000?mm under a contact stress of 150?MPa.     

Use of process signals for tool wear progression sensing in drilling small deep holes

Detailed knowledge about the relation between wear progression of a cutting tool and the cutting forces generated is of paramount importance for the development of a tool condition monitoring strategy. This paper discusses the changes in the different process signals with progressing tool wear of small diameter twist drills (D=1.5 mm), when drilling boreholes having a depth of 10 times the diameter in plain carbon steel using MQL. The effect of different wear patterns on the process signals is presented. Furthermore, several features, which evolve over the life of the drills, are identified and extracted from the process signals. Knowledge about the evolution of these features can support the user to determine the final tool life stage, so that the drill can be replaced before the final fracture occurs.     

A preliminary analysis on tool wear rate of polishing process: adhesion effects

The effects of adhesive strength between abrasive particle and work on tool wear of a polishing process were investigated. Several sets of experiments were designed to reveal some phenomena about the wear rates of tool and work. The experimental study demonstrated that the wear rate of tool was related to the adhesive strength between abrasive particle and work. Any change of the adhesive strength may significantly alter the wear rate of tool. It was shown that, with an electric field applied on the work, the tool's wear rate in polishing the soda-lime-silica glass could be very different from the one without the electric field. An analytical study was done to examine the role played by the adhesive strength at the interfaces of abrasive particle on the wear rates of tool and work. It was derived from the law of force equilibrium and the principle of minimum potential energy. The computer simulations indicated that an enhancement of adhesion between abrasive particle and work would always increase the wear rate at tool while the wear rate of work could be increased or decreased. Finally, the possible causes of observed phenomena and the limitations of the study were discussed.     

Evaluation of diamond tool wear

The present study proposes a test protocol regarding the wear of sintered diamond tools. A set of parameters, which characterise the grade of wear, and its relationship with the cutting ability of the examined tools, are established. The proposed protocol establishes the procedure and the equipment for carrying out the tests, the features of the materials to use and the format of the report to present the results obtained. The developed test protocol indicates an universally applicable way for measurement of the wear of the diamond tool. It is an indispensable instrument for correctly carrying out the wear tests and for reliably interpreting the results. The protocol developed so far mainly regards laboratory tests, considering the slowness and precision of the measurements involved. Given the total absence of norms, this protocol could be absorbed by national and international norm establishing organisations. This protocol has also been applied to two types of tools and the results obtained have appeared reliable and replicable. The test protocol proposed in this study makes it possible to overcome the difficulties connected to the scarcity of technical data regarding the properties of the tool, which is typical in this field since the recipes for tool manufacturing are patented.     

A review of flank wear prediction methods for tool condition monitoring in a turning process

Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.     

Evaluation of static cutting forces and tool wear in HSM process applied to pocket milling

One of the main operations in the manufacturing of molds and dies is the opening of the initial pocket, from which more complex geometries are machined, in order to obtain the negative shape of the final product. Since this is a rough operation, high cutting forces and significant tool wear are observed. Aiming to reduce such parameters, several strategies of tool entry and internal cut of the pocket have been proposed. In this context, this work aims to evaluate the cutting forces in the different parts of a pocket milling, using three different strategies, which are composed by the tool, cutting conditions, tool entry, and tool trajectory (I: ramp entry with spiral internal cut, II: helical entry with zigzag internal cut, and III: plunge entry). The results obtained for strategies I and II showed an increase in the forces in the direction perpendicular to the face in which flank wear occurred and a sharp increase in force at points where the tool changes trajectory. In strategy III, the occurrence of tool fracture due to chip recut led to very high force values.     

Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring     

Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

In this paper, combinations of signal processing techniques for real-time estimation of tool wear in face milling using cutting force signals are presented. Three different strategies based on linear filtering, time-domain averaging and wavelet transformation techniques are adopted for extracting relevant features from the measured signals. Sensor fusion at feature level is used in search of an improved and robust tool wear model. Isotonic regression and exponential smoothing techniques are introduced to enforce monotonicity and smoothness of the extracted features. At the first stage, multiple linear regression models are developed for specific cutting conditions using the extracted features. The best features are identified on the basis of a statistical model selection criterion. At the second stage, the first-stage models are combined, in accordance with proven theory, into a single tool wear model, including the effect of cutting parameters. The three chosen strategies show improvements over those reported in the literature, in the case of training data as well as test data used for validation—for both laboratory and industrial experiments. A method for calculating the probabilistic worst-case prediction of tool wear is also developed for the final tool wear model.     

On-line monitoring of tool wear in turning operation in the presence of tool misalignment

A vision system using high-resolution CCD camera and back-light was developed for the on-line measurement of nose wear of cutting tool inserts. Initial study showed that the system is sensitive to several factors in the work environment such as misalignment of cutting tool, presence of micro-dust particles, vibration and intensity variation of ambient light. An algorithm using Wiener filtering, median filtering, morphological operations and thresholding was developed to decrease the system error caused by these factors. A conforming method was used to overcome misalignment of the tool insert during offline and on-line measurement. The algorithm, combined with a subtraction method, was applied to measure the nose wear area of the inserts under different machining conditions.