Catastrophic Tool Failure Research Articles

Machinability of Ti6Al4V Alloy: Tackling Challenges in Milling Operations

This study investigates strategies for improving the 3D milling of Titanium Alloy Grade 5 (Ti6Al4V) by optimizing machining parameters and cutting tool engagement techniques. Ti6Al4V presents significant machining challenges due to its low machinability index (20%), which directly impacts manufacturing efficiency. High temperatures during machining, often exceeding 8820C, lead to phase transformations, creating a harder Beta lamellar equiaxed microstructure. This, coupled with the alloy's poor thermal conductivity, results in heat concentration at the cutting tool interface, accelerating thermo-chemical wear and potentially catastrophic tool failure. This study explores how controlled cooling methods, coupled with appropriate lubrication, can effectively dissipate heat and flush away chips, mitigating the detrimental effects of high temperatures. Furthermore, the selection of cutting tool materials and coatings with high thermal conductivity and chemical inertness, along with aggressive rake angles and higher relief angles, are examined as methods to improve shearing, minimize smearing, and enhance surface quality. By optimizing these parameters, this study aims to provide manufacturers with practical strategies to overcome the challenges of Ti6Al4V machining, ultimately increasing tool life and overall milling efficiency.

Multiresolution analysis for tool failure detection in CFRP/Ti6Al4V hybrid stacks drilling in aircraft assembly lines

This article presents a method for identifying Catastrophic Tool Failures during CFRP/Ti6Al4V hybrid stacks drilling in aircraft fuselage components assembly. The study is based on a real industrial case in the production system of a commercial aircraft section, where data was collected from the aircraft assembly line for 17 months. Over 30 different tool configurations were utilized in machining the aircraft section. However, the tool exhibiting the highest breakage rate, specifically a 4.6 mm diameter spiral fluted drill designed for CFRP/Ti6Al4V applications, was chosen for further analysis. This data collection included 69 samples of the selected cutting tool, revealing 19 instances of tool failure. The proposed detection method for spindle consumption signals utilizes Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT) and Z-score technique. The research provides a comprehensive analysis of the use of DWT, explaining how the approximation and detail coefficients are obtained, which padding mode is suitable for the industrial case, the influence of the sliding process and filter bank length on the detection of sudden drops in the signal, and how to choose a suitable decomposition level in MRA based on the information provided by the scalograms. The confusion matrix results demonstrate the method's high performance, with Recall and Precision values equal to 1, which means that 100 % of tool failures were detected with no false positives. Additionally, the algorithm running time is 0.65 s, indicating that the method would apply to real-time tool failure detection in industrial machining production systems.

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

The current state-of-the-art review on tool condition monitoring for turning of titanium-based superalloys is presented in this paper. Titanium (Ti) superalloys are widely utilised in aerospace industry, automobile industry, petrochemical applications. Ti superalloys are also used in fabrication of biomedical components due to their outstanding combination of mechanical properties and strong corrosion resistance at extreme temperatures. But these superalloys are difficult-to-cut because to their low heat conductivity, low elastic modulus, high strength, and strong chemical resistance. Literature review highlights the drastic reduction in tool life of titanium superalloys at highspeed and feed rates throughout the machining process. The review paper focuses on (i) various reasons to deploy tool condition monitoring; and (ii) study of tool condition monitoring methods based on machine learning techniques to identify the ideal parameters for the prevention of catastrophic tool failure.

Investigation of Gyroscopic Effect on the Stability of High Speed Micromilling via Bifurcation Analysis

Catastrophic tool failure due to the low flexural stiffness of the micro-tool is a major concern for micromanufacturing industries. This issue can be addressed using high rotational speed, but the gyroscopic couple becomes prominent at high rotational speeds for micro-tools affecting the dynamic stability of the process. This study uses the multiple degrees of freedom (MDOF) model of the cutting tool to investigate the gyroscopic effect in machining. Hopf bifurcation theory is used to understand the long-term dynamic behavior of the system. A numerical scheme based on the linear multistep method is used to solve the time-periodic delay differential equations. The stability limits have been predicted as a function of the spindle speed. Higher tool deflections occur at higher spindle speeds. Stability lobe diagram shows the conservative limits at high rotational speeds for the MDOF model. The predicted stability limits show good agreement with the experimental limits, especially at high rotational speeds.

Tool wear and remaining useful life prediction in micro-milling along complex tool paths using neural networks

Monitoring tool wear state and prediction of the remaining useful life in micro-milling help avoid down time due to unalarmed failure of the cutting tool. In this regard, investigations based on experiments, and data-based models, along straight tool paths have been reported in the literature. However, industrial applications often require machining along complex tool paths, where the tool failure possibilities increase multifold, have not been studied elaborately in the literature. Therefore, the main objective of this work is to develop a data-based model, which can predict the tool wear state, and remaining useful life of the tool, considering the effect of tool path complexities due to varying tool path radius in micro-milling. The data for developing the model were generated by performing micro-milling experiments under different processing parameters, such as feed, depth of cut, spindle rotation speed, and tool path radius. The tool images were captured in-situ without removing the tool from the spindle, and image binarization and alignment operations were performed to extract corresponding cutting edge wear features, such as diameter reduction as well as the wear of individual cutting edges. The tool wear classification criteria were defined to categorize the tool condition in three regions: initial wear, steady-state wear, and critical wear. To capture and model the complex mechanism of micro tool wear and failure, artificial neural networks, as well as deep belief networks, were implemented to predict the wear state as well as the remaining-useful-life (RUL) of the tools. It was found that the wear rate increased with increasing tool path radii and the larger radii could lead to catastrophic tool failure. The neural-network models gave 93–99% accuracy for the prediction of wear state classification and RUL.

Needs, Requirements and a Concept of a Tool Condition Monitoring System for the Aerospace Industry.

In this paper, we describe the needs and specific requirements of the aerospace industry in the field of metal machining; specifically, the concept of an edge-computing-based production supervision system for the aerospace industry using a tool and cutting process condition monitoring system. The new concept was developed based on experience gained during the implementation of research projects in Poland’s Aviation Valley at aerospace plants such as Pratt & Whitney and Lockheed Martin. Commercial tool condition monitoring (TCM) and production monitoring systems do not effectively meet the requirements and specificity of the aerospace industry. The main objective of the system is real-time diagnostics and sharing of data, knowledge, and system configurations among technologists, line bosses, machine tool operators, and quality control. The concept presented in this paper is a special tool condition monitoring system comprising a three-stage (natural wear, accelerated wear, and catastrophic tool failure) set of diagnostic algorithms designed for short-run machining and aimed at protecting the workpiece from damage by a damaged or worn tool.

Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy

An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool wear is efficient, economical for the machining industry to predict tool life, which in turn avoids catastrophic tool failure.

Detection of accelerated tool wear in turning

There are many algorithms for the identification of gradual tool wear (GTW) which usually counts in minutes and the detection of catastrophic tool failure (CTF) which counts in milliseconds. However, the tool may lose its cutting ability by accelerated tool wear (ATW), which may last several seconds and cannot be sensed either by GTW or CTF detection algorithms. The paper presents an innovative algorithm for early detection of ATW and CTF. It consists in comparing the waveforms of the cutting force sensor signal in hierarchical time windows. The compared waveforms are independent of the absolute value of the signal. This allows the detection of ATWs of different intensity and duration. Tests proved that successful detection of ATW allows for prevention of CTF.

Tool wear in dry gear hobbing of 20MnCr5 case-hardening steel, 42CrMo4 tempered steel and EN-GJS-700-2 cast iron

The demand for higher power densities in gear applications leads to the use of high-strength materials. This poses a challenge for manufacturing technologies with regard to the machinability of these materials. Even though gear hobbing is one of the most common technologies for gear machining, only limited investigations on the machinability of high-strength materials have been carried out. In this paper, the machinability of different workpiece materials by gear hobbing under dry cutting conditions is investigated. The tool life and wear behavior of powder metallurgical high-speed steel PM-HSS S390 and sintered tungsten carbide – cobalt WC-Co K30 tools with an AlCrN coating were analyzed. 20MnCr5 case-hardening steel, 42CrMo4 tempered steel and EN-GJS-700-2 cast iron were machined using the fly-cutting trial as an analogy process for gear hobbing. To identify a respective target tool life of LT,10 = 10 m, the cutting speed was varied while the feed rate was defined based on the respective tool concept and cutting substrate. The experiments showed a good machinability of the soft state material 20MnCr5 with PM-HSS S390 tools up to a cutting speed of vc = 350 m/min. A machining of the high-strength materials 42CrMo4 and EN-GJS-700-2 resulted in high tool wear or catastrophic tool failure. Even at low cutting speeds of vc = 50 m/min, the target tool life LT,10 could not be reached. When using WC-Co K30 tools, tool lives of LT > 6 m and cutting speeds up to vc = 400 m/min were feasible when machining 42CrMo4 tempered steel. For the machining of EN-GJS-700-2 cast iron, high abrasive tool wear was observed, which led to short tool lives of LT < 4 m at cutting speeds above vc ≥ 200 m/min. By reducing the cutting speed to vc = 100 m/min, a tool life of LT > 15 m could be achieved.

Modelling and analysis of cutting forces while micro end milling of Ti-alloy using finite element method

Micromilling is one of the preferable micro-manufacturing process, as it exhibits the flexibility to produce complex 3D micro-parts. The cutting forces generated in micro end milling can be attributed for tool vibration and process instability. If cutting forces are not controlled below critical limits, it may lead to catastrophic failure of tool. Cutting force has a significant role to decide the surface roughness. Therefore accurate prediction of cutting forces and selection of suitable cutting parameters mainly feed, is important while micro end milling. In present study, finite element method (FEM) based model has been developed by using ABAQUAS/Explicit 6.12 software. Von-Misses stresses and cutting forces are predicted while micro end milling of Ti-6Al-4V. Further, cutting forces were measured during experimentation using dynamometer mounted on micro-milling test bed. Cutting forces predicted by FEM model are in good agreement with the experimental force values. Obtained FEM results have been used to study the size effect in micro end milling process. Moreover, the effect of uncut chip thickness to cutting edge radius ratio (h/rc) on surface roughness (Ra) has been studied. It is found the feed 2.5 µm/tooth is suitable value to produce optimum surface roughness and cutting forces.

Experimental investigation into tool wear, cutting forces, and resulting surface finish during dry and flood coolant slot milling of Inconel 718

Inconel 718 superalloy is a widely used material in aerospace due to its high strength, corrosion resistance, and high resistance to thermal fatigue. Excellent mechanical and thermal properties also make Inconel 718 one of the most difficult-to-machine alloys. This study aims to take a different approach of analyzing tool wear during machining of Inconel 718 by measuring worn-out area on each flute from three different faces of flute. This paper also includes analysis of cumulative tool wear, cutting forces, surface roughness, topography, burr formation, and chip morphology at various machining parameters and cooling conditions. The cutting speed was kept constant at 60 m/min due to the goal of machining at highest possible speed and the limitation in maximum spindle speed of the machine tool. Experiments were carried out in dry and flood coolant machining using uncoated carbide tools by varying depth of cut and feed rate for four different settings each. It was found that the amount of tool wear varied for different flutes when measured from top, rake, and back faces of each flute, even for the same tool. The back and rake faces exhibit more chipping wear when machining is carried out at higher depth of cut, irrespective of the cooling conditions. At the same machining conditions for the range of parameters selected in this study, flood coolant machining resulted in slightly higher tool wear at the back and rake faces of cutting flutes and generated higher cutting speed, which may be interrelated. The gradual increase in tool wear over time resulted in an increase in surface roughness from entry to exit of a 76.2 mm machined slot. Flood coolant machining resulted in slightly smoother surface finish at the lower settings of depth of cut and feed rate, while the burr formation was marginally higher in flood coolant machining during machining at higher feed rate and depth of cut. The amount of serration was found to increase specifically with the increase of feed rate and was found to be higher for dry machining compared to flood coolant machining at the same parameter settings. Finally, the new approach of tool wear analysis from different faces of individual cutting flute could provide important guidelines for predicting tool life or potential catastrophic tool failure during machining of Inconel 718.

Detection of tool breakage during milling process through acoustic emission

Catastrophic tool failure (CTF) in milling process can cause damage to the product’s machined surface and the machine tools, leading to huge financial losses. It is therefore critical to detect CTF in advance and promptly respond to it. Because of the safety and quality requirements imposed in practice, there are far fewer failure samples than normal samples, and this disequilibrium makes it difficult to detect failures. The aim of this study is to develop a new, easy, and practical automatic system for tool breakage detection using the acoustic emission (AE) technique. Components of AE raw data are analysed to locate the moments of tool breakages and to screen the corresponding AE feature samples. A support vector machine-based cost-sensitive breakage detection model is established and optimized. The proposed model is applied and validated by experiments conducted on a factory’s milling machine. The model achieves an accuracy of 91.18% in the detection of breakages. The results show the practicability and validity of the proposed method.

A hybrid information model based on long short-term memory network for tool condition monitoring

Excessive tool wear leads to the damage and eventual breakage of the tool, workpiece, and machining center. Therefore, it is crucial to monitor the condition of tools during processing so that appropriate actions can be taken to prevent catastrophic tool failure. This paper presents a hybrid information system based on a long short-term memory network (LSTM) for tool wear prediction. First, a stacked LSTM is used to extract the abstract and deep features contained within the multi-sensor time series. Subsequently, the temporal features extracted are combined with process information to form a new input vector. Finally, a nonlinear regression model is designed to predict tool wear based on the new input vector. The proposed method is validated on both NASA Ames milling data set and the 2010 PHM Data Challenge data set. Results show the outstanding performance of the hybrid information model in tool wear prediction, especially when the experiments are run under various operating conditions.

Dynamic force signal analysis in dry finish turning of Aluminium metal matrix composites

Metal matrix composites (MMC) have found wide applications in the transportation sector. But, the presence of hard particles in the MMCs causes catastrophic tool failures. The current study presents a method to select process parameters to increase the material removal rate in finish turning of Al-MMC (Al 6061, 5% SiC, 3% C) using TiN coated carbide inserts. The key aspects of the method are (a) Using the fractional factorial method for performing experiments economically (b) Using radial force instead of cutting force (c) Using frame statistics and linear spectrum of the radial cutting force signal to select process parameters. The experiments were conducted on a precision lathe. A 6-component piezoelectric dynamometer was used to measure the cutting force.

Experimental investigation of tool wear using vibration signals: An ANN approach

Hard turning is process in which material in their hardened state of hardness range 45-70 HRC is machined. Various researchers have reported that tool wear is a major problem while turning process. Early replacement of workable tool or late replacement of worn tool may cause time and production loss, hence to avoid catastrophic tool failure, it is required to monitor the development of tool wear from the start of cutting process. In this study, tool wear is monitored by measuring vibration signals when turning hardened steel. Piezoelectric Tri-axial is used to measure vibrational signals in three perpendicular directions. Vibration signals measured using sensors in the thrust direction are more reliable to use for development of tool condition monitoring system. During turning, tool is subjected to heavy mechanical loads hence vibration is induced, presence of vibration results in poor surface finish, tool damage and undesired noise hence it needs analysis of condition of tool. FFT analyzer is used to measure vibration of cutting tool. The mathematical model developed by using regression analysis which was used to find out most favorable machining parameter based on observed facts i.e. vibration signals, surface roughness and tool wear. In this paper experimental studies was considered to develop an artificial neural network (ANN) in Matlab which can predict tool wear in turning operation of AISI 52100 steel by considering vibrations along with the changing parameter. The predicted values of tool wear for 20 different cutting conditions by using RSM and ANN are compared with experimental tool wear values and percentage error was computed. The correlation shows that ANN perform superior to RSM. The analysis using vibration signals will help in predicting and preventing the failure of tool wear which will avoid the downtime of a production system and will also increase the safety of operation.

Effect of Progressive Tool Wear on the Evolution of the Dynamic Stability Limits in High-Speed Micromilling of Ti-6Al-4V

Abstract Micromilling can fabricate complex features in a wide range of engineering materials with an excellent finish but the limited flexural stiffness of the micro-end mill can result in catastrophic tool failure. This issue can be overcome by using high rotational speeds. Note that the combination of high rotational speeds and low flexural stiffness can induce process instability which is aggravated by the accelerated wear of the micro-tools at high speeds, specifically, for Ti-alloys. The effect of progressive tool wear on the stability has been investigated in micromilling of Ti-6Al-4V. For incorporating tool wear, the cutting force coefficients are modeled as a function of initial and instantaneous cutting edge radius (CER) and feed per tooth. The initial CER of the micro-tool is considered due to the inherent variability in the tool grinding process. A significant increase (85–114%) in the instantaneous CER is observed with an increase in the length of cut. A 2DOF time-domain model based on semi-discretization method has been used to characterize the evolution of stability limits with an increase in the length of cut. The progressive tool wear affects the stability limits along with the initial CER and the feed per tooth. At higher speeds (90,000–110,000 rpm), the effect of progressive tool wear is pronounced and the stability limits reduce by ∼30% in that range.

Effects of Depth of Cut during Machining of Inconel 718 using Uncoated WC Tool

The present work examines mechanisms of tool wear during machining (turning) of Inconel 718 super alloy in dry cutting environment. Performance of uncoated WC insert is studied in connection with varying depth of cut keeping cutting speed and feed rate constant. Effects of depth of cut on cutting force, approximate temperature at the cutting zone, chip reduction coefficient, depth of flank wear etc. are studied. Various wear mechanisms of the cutting tool including abrasion, adhesion, chipping off, notching etc. are identified. Adhesion of fused chips is also identified at the rake surface which contains oxides of Fe and Nb. The highest depth of cut results in catastrophic tool failure. Area of crater wear is also found increases with increase in depth of cut.

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1-PZT Diaphragm Transducer Response and EMI Sensing Technique.

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices.

TUNGSTEN OXIDES FORMATION ON THE INTERFACE OF THE CEMENTED CARBIDE AND THE INCONEL 182 OVERLAYS AT ELEVATED TEMPERATURE UP TO 800∘C

Inconel 182 overlays is well known as a promising filler material, depositing on the surface of the critical components in nuclear power plants, light water reactor and petrochemical devices. As the deposited surface is usually non-uniform and irregular, the relative anti-impact cemented carbide tool is required. As the cemented carbide cannot withstand high temperature, the tungsten oxides are prone to form at elevated temperature, resulting in catastrophic tool failure. However, few literature reveals the tungsten oxides formation on the interface of the cemented carbide and Inconel 182 overlays at elevated temperature. Therefore, this study investigates the tungsten oxides formation on the interface of the cemented carbide (as well as its TiAlN or TiN coating for comparison) and the Inconel 182 overlays at elevated temperature up to 800∘C. The results show that the tungsten oxides WO[Formula: see text] ([Formula: see text]) are WO2, WO[Formula: see text], WO[Formula: see text] and WO3. They are formed by oxidation or reduction reactions at different combinations of the temperature and the atom percentage of oxygen.

Electroplastic Drilling of Low- and High-Strength Steels

When postforming machining operations are required on high-strength structural components, tool life becomes a costly issue, often requiring external softening via techniques such as laser assistance for press-hardened steel components. Electrically assisted manufacturing (EAM) uses electricity during material removal processes to reduce cutting loads through thermal softening. This paper evaluates the effect of electric current on a drilling process, termed electroplastic drilling, through the metrics of axial force, and workpiece temperature when machining mild low carbon steel (1008CR steel) and an advanced high strength press hardened steel. A design of experiment (DoE) is conducted on 1008CR steel to determine primary process parameter effects; it is found that electricity can reduce cutting loads at the cost of an increased workpiece temperature. The knowledge generated from the DoE is applied to the advanced high strength steel to evaluate cutting force reduction, process time savings, and tool life improvement at elevated feedrates. It is found that force can be reduced by 50% in high feedrates without observing catastrophic tool failure for up to ten cuts, while tool failure occurs in only a single cut for the no-current condition. Finally, the limitations of the developed model in electroplastic drilling are discussed along with future suggestions for industrialization of the method.