A cutting power model for tool wear monitoring in milling

This paper describes a cutting power model in face milling operation, where cutting conditions and average tool flank wear are taken into account. The cutting power model is verified with experiments. It is shown with the simulations and experiments that the simulated power signals predict the mean cutting power better than the instantaneous cutting power. Finally, the cutting power model is used in a cutting power threshold updating strategy for tool wear monitoring which has been carried out successfully in milling operations under variable cutting conditions.     

Real-time tool wear monitoring in milling using a cutting condition independent method

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.     

An overview of approaches to end milling tool monitoring

The increase in awareness regarding the need to optimise manufacturing process efficiency has led to a great deal of research aimed at machine tool condition monitoring. This paper considers the application of condition monitoring techniques to the detection of cutting tool wear and breakage during the milling process. Established approaches to the problem are considered and their application to the next generation of monitoring systems is discussed. Two approaches are identified as being key to the industrial application of operational tool monitoring systems.Multiple sensor systems, which use a wide range of sensors with an increasing level of intelligence, are seen as providing long-term benefits, particularly in the field of tool wear monitoring. Such systems are being developed by a number of researchers in this area. The second approach integrates the control signals used by the machine controller into a process monitoring system which is capable of detecting tool breakage. Initial findings mainly under laboratory conditions, indicate that both these approaches can be of major benefit. It is finally argued that a combination of these approaches will ultimately lead to robust systems which can operate in an industrial environment.     

Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods

The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Several methods to develop monitoring devices for observing the wear levels on the cutting tool on-line while engaged in cutting have been attempted. This paper presents a review of some of the methods that have been employed in tool condition monitoring. Particular attention is paid to the manner in which sensor signals from the cutting process have been harnessed and used in the development of tool condition monitoring systems (TCMSs).     

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring

The cutting tools are today used a lot by industry and they are expensive, so it was interesting to optimize their use, by developing a predictive method of their wear, particularly, the flank wear V _b . For this task, the flank tool wear was measured in off-line using a binocular microscope, whereas, the cutting forces are recorded by means of a dynamometer (Kistler 9255B). The acquired signatures are analyzed during the milling operation throughout the tool life. In this paper, we are interested in the extraction of the appropriate indicators which characterize the tool wear by temporal and frequential analyses of the cutting force signals; and highlighting the influence of the clamp holes and the machining cycle to the quality of the measurements.     

Tool wear detection and fault diagnosis based on cutting force monitoring

In metal cutting processes, an effective monitoring system, which depends on a suitably developed scheme or set of algorithms can maintain machine tools in good condition and delay the occurrence of tool wear. In this paper, an approach is developed for fault detection and diagnosis based on an observer model of an uncertain linear system. A robust observer is designed, using the derived uncertain linear model, to yield the necessary and key information from the system. Subsequently, it is used as a state (tool wear) estimator, and fault detection is carried out by using the observed variables and cutting force. The developed approach is applied to milling machine center. Several linear models are identified based on different working conditions. A dominant model plus uncertain terms is derived from these model set and used as an observer. Threshold values are proposed for detecting the fault of the milling machine. Examples taken from experimental tests shown that the developed approach is effective for the fault detection. The approach can be used for fault detection of failures arising from sensor or actuator malfunction.     

Radial immersion angle estimation using cutting force and predetermined cutting force ratio in face milling

Radial immersion ratio is an important factor to determine the threshold for tool conditioning monitoring and automatic force regulation in face milling. In this paper, a method of on-line estimation of the radial immersion angle using cutting force is presented. When a tooth finishes sweeping, a sudden drop of cutting force occurs. This force drop is equal to the cutting force that acts on a single tooth at the swept angle of cut and can be obtained from the cutting force signal in feed and cross-feed directions. The ratio of cutting forces in feed and cross-feed directions acting on the single tooth at the immersion angle is a function of the immersion angle and the ratio of radial-to-tangential cutting force. In this study, it is found that the ratio of radial-to-tangential cutting force is not affected by cutting conditions and axial rake angle. Therefore, the ratio of radial-to-tangential cutting force determined by just one preliminary experiment can be used regardless of the cutting conditions for a given tool and workpiece material. Using the measured cutting force during machining and a predetermined ratio, the radial immersion ratio is estimated in the process. Various experiments show that the radial immersion ratio and instantaneous ratio of the radial to tangential direction cutting force can be estimated very well by the proposed method.     

Tool life and tool wear in the semi-finish milling of inclined surfaces

Functional die and mold components have complex geometries and are made of high hardness materials, which make them difficult to machine. This work contributes to a better understanding of this type of process and of the wear mechanisms of tools used in semi-finishing operations of hardened steels for dies and molds. Several milling experiments were carried out to cut AISI H13 steel with 50 HR_C of hardness using the high-speed milling technique. The main goal was to verify the influence of workpiece surface inclination and cutting conditions on tool life and tool wear mechanisms. The main conclusions were the inclination of the machined surface strongly influences tool life and tool wear involves different mechanisms. At the beginning of tool life, the wear was caused mainly by abrasion on the flank face plus diffusion and attrition on the rake face. At the end of tool life, the mechanisms were adhesions and microchipping at the cutting edge.     

An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts

Inconel 718 is a difficult-to-cut nickel-based superalloy commonly used in aerospace industry. This paper presents an experimental study of the tool wear propagation and cutting force variations in the end milling of Inconel 718 with coated carbide inserts. The experimental results showed that significant flank wear was the predominant failure mode affecting the tool life. The tool flank wear propagation in the up milling operations was more rapid than that in the down milling operations. The cutting force variation along with the tool wear propagation was also analysed. While the thermal effects could be a significant cause for the peak force variation within a single cutting pass, the tool wear propagation was believed to be responsible for the gradual increase of the mean peak force in successive cutting passes.     

Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools

In face milling processes, the surface quality of the machined part depends on many factors, including feed, cutting tool geometry and tool errors. In this work, a numerical model for predicting the surface profile and surface roughness as a function of these factors is presented, incorporating a random values generation algorithm that makes it possible to determine the variation in surface roughness from the values that can be adopted by tool errors. This work is focused on round insert cutting tools and the influence of tool errors such as radial and axial runouts. The results that correspond to a number of teeth equal to 4, insert diameter of 12 mm, depth of cut of 0.5 mm, cutting speed of 120 m/min and feed of 0.4–1.4 mm/rev are analysed. Milling experiments are made to verify the validity of the model and the discrepancies between the experimental and theoretical surface profiles are assumed to be a consequence of different factors such as the variation in undeformed chip thickness along the surface profile.     

An example of selection of the cutting conditions in broaching of heat-resistant alloys based on cutting forces, surface roughness and tool wear

Despite the fact that broaching has been used for long a time as a machining process for manufacturing highly accurate, complex profiles, little work has been published on the selection of cutting conditions to maximise tool life while achieving the required surface quality and level of cutting forces. This is even more important when notorious difficult-to-cut materials, such as Ni and Ti alloys, used in the aero-engine/power generation industry where high geometrical accuracy, along with restricted surface quality are required. The paper describes a multi-step methodology to select the cutting conditions for the broaching of Ni and Ti alloys used in the manufacture of aero-engines and power generators. Based on the application of the Taguchi technique, the cutting conditions (cutting speed, rise per tooth, rake angle, coolant type) were selected in order to obtain fine surface quality along with reasonably low levels of main cutting force (Fz) and perpendicular cutting force (Fy). This was followed by a reduced number of tool life tests which were carried out in order to select the final cutting conditions. Scanning electron microscopy (SEM) and chemical composition analysis on the flank and rake faces of the tools were employed to characterise the worn tools. It was found that when broaching forces, surface roughness and tool life are considered as process output measures, and taking into consideration generic process constraints on process productivity, machine tool stability, and tool stiffness, only a “pseudo-optimal” solution for the cutting conditions could be specified.     

Theoretical modelling and simulation of cutting forces in face milling with cutter runout

A new approach to theoretical modelling and simulation of cutting forces in face milling is presented. Based on a predictive machining theory, the action of a milling cutter is modeled as the simultaneous actions of a number of single-point cutting tools. The milling forces are predicted from the workpiece material properties, cutter parameters, tooth geometry, cutting conditions and types of milling. The properties of the workpiece material are considered as functions of strain, strain-rate and temperature in the cutting region. It takes into account the effect of the intermittent contact between each milling tooth and the workpiece on the temperature in the cutting region. It also takes into account the effect of cutter runout on the undeformed chip thickness. Milling experiments have been conducted to verify the proposed model. Good agreements between the experimental and simulated results are presented.     

Indirect cutting force measurement in multi-axis simultaneous NC milling processes

There have been many research works for the indirect cutting force measurement in machining process, which deal with the case of one-axis cutting process. In multi-axis cutting process, the main difficulties to estimate the cutting forces occur when the feed direction is reversed. This paper presents the indirect cutting force measurement method in contour NC milling processes by using current signals of servo motors. A Kalman filter disturbance observer and an artificial neural network (ANN) system are suggested. A Kalman filter disturbance observer is implemented by using the dynamic model of the feed drive servo system, and each of the external load torques to the x and y-axis servo motors of a horizontal machining center is estimated. An ANN system is also implemented with a training set of experimental cutting data to measure cutting force indirectly. The input variables of the ANN system are the motor currents and the feedrates of x and y-axis servo motors, and output variable is the cutting force of each axis. A series of experimental works on the circular interpolated contour milling process with the path of a complete circle has been performed. It is concluded that by comparing the Kalman filter disturbance observer and the ANN system with a dynamometer measuring cutting force directly, the ANN system has a better performance.     

Experimental studies of cutting force variation in face milling

The purpose of this paper is to present a developed cutting force model for multi-toothed cutting processes, including a complete set of parameters influencing the cutting force variation that has been shown to occur in face milling, and to analyse to what extent these parameters influence the total cutting force variation for a selected tool geometry. The scope is to model and analyse the cutting forces for each individual tooth on the tool, to be able to draw conclusions about how the cutting action for an individual tooth is affected by its neighbours.A previously developed cutting force model for multi-toothed cutting processes is supplemented with three new parameters; eccentricity of the spindle, continuous cutting edge deterioration and load inflicted tool deflection influencing the cutting force variation. A previously developed milling force sensor is used to experimentally analyse the cutting force variation, and to give input to the cutting force simulation performed with the developed cutting force model.The experimental results from the case studied in this paper show that there are mainly three factors influencing the cutting force variation for a tool with new inserts. Radial and axial cutting edge position causes approximately 50% of the force variation for the case studied in this paper. Approximately 40% arises from eccentricity and the remaining 10% is the result of spindle deflection during machining. The experimental results presented in this paper show a new type of cutting force diagrams where the force variation for each individual tooth when two cutting edges are engaged in the workpiece at the same time. The wear studies performed shows a redistribution of the individual main cutting forces dependent on the wear propagation for each tooth.     

On cutting parameters selection for plunge milling of heat-resistant-super-alloys based on precise cutting geometry

In plunge milling operation the tool is fed in the direction of the spindle axis which has the highest structural rigidity, leading to the excess high cutting efficiency. Plunge milling operation is one of the most effective methods and widely used for mass material removal in rough/semi-rough process while machining high strength steel and heat-resistant-super-alloys. Cutting parameters selection plays great role in plunge milling process since the cutting force as well as the milling stability lobe is sensitive to the machining parameters. However, the intensive studies of this issue are insufficient by researchers and engineers. In this paper a new cutting model is developed to predict the plunge milling force based on the more precise plunge milling geometry. In this model, the step of cut as well as radial cutting width is taken into account for chip thickness calculation. Frequency domain method is employed to estimate the stability of the machining process. Based on the prediction of the cutting force and milling stability, we present a strategy to optimize the cutting parameters of plunge milling process. Cutting tests of heat-resistant-super-alloys with double inserts are conducted to validate the developed cutting force and cutting parameters optimization models.     

A brief review: acoustic emission method for tool wear monitoring during turning

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are:

1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction.

2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc.

3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and sensor and data fusion.

A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring methodology.     

Data fusion neural network for tool condition monitoring in CNC milling machining

Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method.     

Bandsawing. Part I: cutting force model including effects of positional errors, tool dynamics and wear

This article presents a mechanical cutting force model for bandsawing. The model describes the variation in cutting force between individual teeth and relates it to initial positional errors, tool dynamics and edge wear. Bandsawing is a multi-tooth cutting process, and the terminology of the cutting action is discussed and compared with other cutting processes. It will also be shown that the setting pattern and the preset feed govern the cutting data.     

A new versatile in-process monitoring system for milling

Tool condition monitoring (TCM) systems can improve productivity and ensure workpiece quality, yet, there is a lack of reliable TCM solutions for small-batch or one-off manufacturing of industrial parts. TCM methods which include the characteristics of the cut seem to be particularly suitable for these demanding applications. In the first section of this paper, three process-based indicators have been retrieved from literature dealing with TCM. They are analysed using a cutting force model and experiments are carried out in industrial conditions. Specific transient cuttings encountered during the machining of the test part reveal the indicators to be unreliable. Consequently, in the second section, a versatile in-process monitoring method is suggested. Based on experiments carried out under a range of different cutting conditions, an adequate indicator is proposed: the relative radial eccentricity of the cutters is estimated at each instant and characterizes the tool state. It is then compared with the previous tool state in order to detect cutter breakage or chipping. Lastly, the new approach is shown to be reliable when implemented during the machining of the test part.