Tool Breakage Research Articles

New Process for Narrow Pipes

AbstractThe greatest challenge in many processes is not the question of “How” but of “How long”. All too often, tool breakage or damage leads to unplanned production stops.

An optical fiber measurement system design on tool radial vibration

The effects of tool radial vibration bring not only poor surface quality, inferior dimensional accuracy, but also disproportionate tool wear or tool breakage and excessive noise. Therefore, online measurement and monitoring of tool vibration are necessary. In order to monitor the tool vibration, an optical fiber measurement system was design in this pater. Firstly, the structure and basic principle of the optical fiber sensor was given; secondly, the light intensity to voltage converter circuit was introduced; then, an experiment platform was built for verify the feasibility of the optical measuring system, and the result shows that the radial vibration of a smooth 10 mm diameter shaft can be measured quickly.

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

Cutting tool wear in machining processes reduces the product surface quality, affects on the dimensional and geometrical tolerances and causes tool breakage during the metal cutting. Therefore, online tool wear monitoring is needed to prevent reduction in machining quality.An artificial neural network (ANN) model was developed in this study to predict and simulate the tool flank wear. To reach to this aim, an experiment array was provided using of full factorial method and the tests were conducted on a CNC lathe machine tool. Vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool flank wear. For designing the model, the cutting parameters, cutting forces and vibration amplitude were defined as model input and tool flank wear was selected as output. The model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. The estimated and measured results were then compared with each other. Based on the comparison results, maximum squared error values are under and the R2 is 1 which it means that the designed model can predict the results with a high and reliable accuracy.

Acoustic Emission in Stainless Steel End Milling with Carbide Tools

Tool wear is a complex phenomenon, it worsens surface quality, increases power consumption, and causes rejection of machined parts. Tool wear has a direct effect on the quality of the surface finish of the workpiece, dimensional precision and ultimately the cost of the parts produced. In modern automated manufacturing machines, tool monitoring system for automated machines should be capable of operating on-line and interpret the working condition of machining process at a given point in time. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool in order to avoid tool failure or undesirable circumstances. This study therefore uses acoustic emission (AE) sensing techniques, signal processing and Artificial Neural Networks (ANN) frameworks to model and validate the machining process. The AE showed effects of tool breakage and ANN predictions closest to the experimental cutting parameters were obtained. It was also shown that the ANN prediction model obtained is a useful, reliable and quite effective tool for modeling tool wear of carbide tools when working on stainless steel. Thus, the results of the present research can be successfully applied in the manufacturing industry to reduce the time, energy and high experimental costs.

An approach for optimization of turning parameters considering uncertainty parameters

Regenerative chatter in turning can cause serious tool wear or breakage, along with poor surface finish on the machined workpiece. Consequently, it is critical to avoid the occurrence of chatter in turning process. In this paper, dynamic model of regenerative chatter in turning process is established to predict the limited cutting width and derive the stability lobe diagram. This study addresses the influences of uncertain factors on the turning process, and a reliability based optimization model is established to obtain optimal turning parameters. In the optimization model, the maximum material removal rate is taken as the objective function and the reliability of turning stability, surface precision, and cutting power is defined as the constraint function. The sequential optimization and reliability assessment (SORA) is utilized to solve the optimization model. Finally, a discussion of the practical application of this method is presented.

On modeling of cutting forces in micro-end milling operation

ABSTRACTMicro-end milling is used for manufacturing of complex miniaturized components precisely in wide range of materials. It is important to predict cutting forces accurately as it plays vital role in controlling tool and workpiece deflections as well as tool wear and breakage. The present study attempts to incorporate process characteristics such as edge radius of cutting tool, effective rake and clearance angles, minimum chip thickness, and elastic recovery of work material collectively while predicting cutting forces using mechanistic model. To incorporate these process characteristics effectively, it is proposed to divide cutting zone into two regions: shearing- and ploughing-dominant regions. The methodology estimates cutting forces in each partitioned zone separately and then combines the same to obtain total cutting force at a given cutter rotation angle. The results of proposed model are validated by performing machining experiments over a wide range of cutting conditions. The paper also highlights the importance of incorporating elastic recovery of work material and effective rake and clearance angle while predicting cutting forces. It has been observed that the proposed methodology predicts the magnitude and profile of cutting forces accurately for micro-end milling operation.

A Mechanistic Model for Prediction of Cutting Parameters in Micro-Scale Milling

While down-scaling of micro milling process is similar to the conventional process, there are specific issues that differ from macro machining due to higher ratios of feed per tooth to tool radius and tool run-out to tool diameter, size-effect, minimum chip thickness, elastic-plastic deformation, microstructure effects, etc. One of the challenges in micro machining is attaining accurate and reliable machining parameters, which can reduce tool wear and breakage to achieve higher productivity and quality at a lower cost. Therefore, this paper presents a new mechanistic model for predicting the precise process parameters considering material properties and principles of micro-milling under various cutting conditions. The proposed model also takes into account the nonlinearity and dynamics of minimum chip-thickness, micro-milling cutting forces considering cutting, as well as edge and damping coefficients into. The predicted stability lobes and the stability limits from experiments are in sufficient agreement. The research of micro-scale milling parameters is significant to improve the precision of machined parts, reduce the wear and tear of the micro-milling blade and extend the life of micro-tools.

Analysis of the micro turning process in the Ti-6Al-4V titanium alloy

The great challenge of the modern industry is carrying out an online prediction in the shop floor during the machining to define the exact instant of tool breakage and simultaneously improve the quality of manufactured products. This work shows a study to examine the effect of the feed rate, depth of cut, cooling system, and type of tool on the responses: surface roughness, passive force (Fp), feed rate force (Ff), cutting force (Fc), and micro hardness in the turning of Ti-6Al-4V titanium alloy. Experimental tests were carried out with workpieces 4 mm in diameter, and the surface roughness, micro hardness, and cutting efforts were analyzed. The analysis of variance was used to define the influence of input parameters on the responses. The results showed that the lowest surface roughness was achieved using the carbide tool with code TPMT and the lower input parameters, but the most important parameter was feed rate. The cutting efforts were influenced by feed rate and depth of cut. On the other hand, the cooling system does not show good efficiency in the cutting efforts. However, the geometry of the tool and the Minimum Quantity Lubrication system were more effective to cause the hardening in micro scale at the surface of the material.

Sequential spindle current-based tool condition monitoring with support vector classifier for milling process

Inexpensive tool condition (TC) monitoring plays a significant role for less human duty machining process in large-scale manufactory. A time-sequential spindle current-based tool breakage diagnosis technique with least squares support vector machine (LS-SVM) classifier is investigated to provide an inexpensive on-line TC monitoring system for milling process. The recognition technique consists of a spindle motor current feedback sensor, a signal processor, and an intelligent classifier. The processor generates machining condition features with Sym6 wavelet transformation to decompose the feedback signals in time domains to generate sequence samples. The features involving both normal and broken tool conditions during machining are fed into the classifier to conduct kernel-based LS-SVM training. With the transformation and training, an object oriented representation function as the LS-SVM classifier is set up and then utilized to diagnose tool fractures in the real time under varying cutting conditions. Experiments were conducted on a milling platform with the built monitoring system consisting of a current acquisition system and its processing software. Experimental results show high accuracy rate and high calculation performance in on-line monitoring of cutting tool conditions for milling process.

A method based on spindle motor current harmonic distortion measurements for tool wear monitoring

This paper deals with an indirect measurement of tool wear from spindle motor current. The main focus of this paper is on harmonic analysis of motor current. In this research, the nonlinear harmonic distortion caused by tool breakage and tool wear in the turning process is scrutinized. Representation of fundamental and harmonic components of the signals indicates that the amplitude of fundamental and odd order harmonic components of the signal for worn tool is higher than sharp tool. As the result of increasing harmonics the waveform of AC current signal becomes more distorted. The total harmonic distortion and crest factor measurements, which indicate the level of distortion in the current signal, are proposed as the measures for detecting tool wear and tool breakage by measuring electric current signal. The results show that the total harmonic distortion and crest factor measures increases as tool wear occurs and abruptly increases when there is tool breakage.

A New Cold Work PM-Grade Combining High Wear Resistance with High Ductility

Abrasive wear, cracking, and chipping are the most critical failure mechanisms in cold work tooling operations, such as blanking and forming, fine blanking, deep drawing, cold forging, powder compacting, and more. A good combination of wear resistance and ductility is needed to meet the requirements for long tool life and avoid breakage of tools.

Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling

Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner.Nowadays, spindle power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate spindle power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle power data were collected from the power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the spindle power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with power data as compared to that between 0.4–17.9% with force data. Findings suggest that spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

Approaches for improvement of EDM cutting performance of SiC with foil electrode

Electrical discharge machining by foil electrode serves as an alternative method for SiC slicing. This technology uses a highly tensioned thin foil as the tool electrode. The main advantages over wire EDM are that the foil thickness can be made smaller than the wire diameter, vibrations can be avoided by applying high tension, and higher current can be supplied since there is less risk of tool breakage. However, due to the large side surface area of the foil electrode, there is a high occurrence probability of side surface discharges and high concentration of debris, which affects kerf width accuracy and machining stability. In the aim to overcome both problems, this study proposes two foil electrode designs: a foil electrode in which holes are machined and the insulation of the side surface areas by a resin coating layer of 5μm thickness. The influences of both foil electrodes were tested with three different slicing strategies: no strategy, applying jump motion of the tool electrode, and applying reciprocating motion. From machining experiments and comparative studies of the discharge delay time, it was found that with both foil tools, the occurrence probability of side surface discharges can be reduced. In addition, the chip pocketing effect of the holes enhance the flushing conditions, resulting in a higher cutting speed.

Fatigue failure of coated carbide tool and its influence on cutting performance in face milling SKD11 hardened steel

Failure patterns of coated carbide tool were investigated by high-speed face milling of the hardened steel SKD11. Tool failure surface morphology, cutting force and machined surface roughness were also analyzed to reveal the failure mechanisms. The results indicated that the dominant failure pattern of coated carbide tool was breakage. The primary mechanism of tool breakage was fatigue fracture. Under different cutting speeds, the distinctive morphologies of fatigue fracture were presented on the failure surfaces. At low cutting speeds, many fatigue sources were observed on the rake face. The distance between fatigue sources and tool nose was approximately two times of the depth of cut. With the increase of cutting speed, the fatigue striations and riven patterns were observed at the fracture surface. In addition, the fatigue steps and crack deflection were found under high cutting speeds. The main fracture mode was intergranular fracture at lower cutting speeds. However, it was transgranular fracture at higher cutting speeds. Furthermore, the irregular fracture surfaces at low cutting speeds and at high cutting speeds contribute to a larger cutting force increment compared with the medium cutting speeds. The increment of surface roughness in the initial and severe wear stages was lower than that in the steady wear stage, while the deviation of surface roughness was relatively large.

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.

Model-Based Online Tool Monitoring for Hobbing Processes

Continuous increase in flight passengers alongside with a high demand for fuel efficiency has led to the development of Geared Turbo Fans (GTF). Being a safety-critical part, the gearbox faces strong safety requirements that also account for sophisticated manufacturing processes and monitoring systems. One major issue is tool wear and the threat of tool breakage during the hobbing process. Due to the high costs of both the raw material and the tool, wear induced tool breakage is a major cost driver. Common practice today is to use each tool for a designated time, but in-situ online wear assessment would result in a reduction of costs as tools can be used to their individual potential. Tool wear of hobs is not spread equally across all cutting edges; hence the assignment of tool wear to each tooth would enable a monitoring system to analyze the individual tool life and predict its operational capability. This research paper presents a method of using effective power signals in combination with a predictive model to determine the actual wear status of each tooth. The model uses the chip geometry and a force model in order to predict the expected torques of the spindle and compares them in real-time with measurement data. An algorithm then estimates the wear status of each tooth. These findings enable further research on an online, model based and position-oriented tool wear monitoring system.

Increasing productivity in turning of hard-to-cut materials by means of modified flank faces

The high thermo-mechanical tool load during cutting of hard-to-cut material leads to an increased tool wear, which affects the surface integrity and tool life adversely. An innovative approach to counter the wear is to retract the tools flank face at a certain distance from the cutting edge. The so-called flank face modification geometrically limits the tool wear and increases the available material of the tool, which can be removed by the abrasive cutting process until a certain tool life criterion is reached. However, the flank face modification weakens the mechanical stability of the cutting edge. To avoid tool breakage due to excessive mechanical stress, this paper presents a new design approach based on static finite element simulations. Finally, experimental results show an increase in tool life up to 75% compared to conventional tools with these designed modifications

Indirect Monitoring Method of Tool Wear using the Analysis of Cutting Force during Dry Machining of Ti Alloys

Tool life optimization is in constant evolution; so many researchers have focused applied different measuring techniques for the tool wear because plays a significant role in the control and improvement of product during machining operations, in real time. Titanium alloys are widely used in many industries of strategic interest as aerospace sector, in the manufacturing of parts with a good strength to weight ratio. These alloys usually show a poor machinability, due to their low thermal conductivity and modulus of elasticity, which may increase the cutting temperature and make the tool breakage more likely. This paper presents a methodology for the tool wear monitoring by using the analysis of cutting forces during dry machining of Titanium alloys.

Experimental study of tool bending force and feed rate in ECDM milling

Electrochemical discharge machining (ECDM) is a suitable process for the fabrication of microholes and microchannels in the nonconductive materials such as glass. In the ECDM milling, in some conditions, the tool is penetrated to the workpiece, which may lead to a breakage in the tool. In order to avoid the tool breakage, machining the parameters such as feed rate should be selected appropriately. In this study, the bending force applied on the tool was evaluated as a function of tool diameter, electrolyte concentration, presence of the magnetic field, and tool feed rate. The maximum feed rate, which keeps the bending tool force in the constant ranges, is desirable. According to the experimental results and combination of the machining parameters, the optimum feed rate was obtained. The results showed a decrease in the bending force by increasing the electrolyte concentration and elevating the applied voltage. In addition, the significant effect of a magnetic field on the reduction of bending force in the lower concentration of electrolyte (15 wt%) was observed.

Tool condition monitoring in interrupted cutting with acceleration sensors

Abstract Tool breakage is a serious issue in conditions with highly variable stress such as interrupted turning. The tool may fail suddenly though commonly tool failure is preceded by other symptoms such as chipping or fracture of tool edges and tool wear before the complete failure. These symptoms can be used to predict reliably complete tool failure. In the case of a complete failure, the surface integrity of the workpiece is commonly ruined causing waste, making the individual events one of the most expensive failures in small series and flexible manufacturing in addition to collisions. In earlier studies, tool wear has been monitored by force sensors. There are also methods for estimating cutting force with acceleration sensors. In this study, it is demonstrated that it is possible to estimate tool deflection, connected to main cutting force, with acceleration sensor and use this information for detecting the chipping and small fracture of the tool edge. The method presented in this study can be used as a predictor for complete tool failure and thus prevent waste.