Tool Breakage Detection Research Articles

Tool Failure Detection in CFRP/Ti6Al4V hybrid stacks drilling using Discrete Wavelet Transform

The study presents a procedure for detecting tool failure in drilling of CFRP/Ti6Al4V aeronautical structural components. To accurately detect tool breakage, a method based on the Daubechies 2 Discrete Wavelet Transform (DWT) is proposed to analyze the spindle power consumption signals in the time-frequency domain. The signals are collected from the automatic drilling machine of the industrial system. At each level of decomposition, statistical measures are calculated to extract signal features that are then used as inputs to a classifier algorithm to identify signal patterns or anomalies. The Recall and Precision scores of 1 indicate the effectiveness of the proposed method.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring (TCM) is a key technology for intelligent manufacturing. The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs. Recently, an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed. Different from traditional signal feature-based monitoring, the data from sensors are utilized to build a dynamic process model. Then the nonlinear output frequency response functions, a concept which extends the linear system frequency response function to the nonlinear case, over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions. In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools, in the present study, a multivariate control chart is proposed for TCM based on the frequency-domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis. The feature dimension is reduced by principal component analysis first. Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises. The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wears of solid carbide end mills. The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Material recognition for fault diagnosis in machine tools using improved Mel Frequency Cepstral Coefficients

Identification of material type in structural cracks is crucial for fault diagnosis and structural integrity assessment in multi-component operation systems. In the current literature, acoustic emission (AE) signal analysis has been extensively explored as an effective method for crack monitoring and fatigue damage assessment. Most existing research on crack diagnosis is primarily focused on determining the crack formation in structures using the AE signals corresponding to known materials. However, the material recognition at any given cracking conditions using AE signals has lagged behind, hindering fault detection and damage repair in complex and multi-material structures. This research investigates the feasibility of the synergistic use of Mel Frequency Cepstral Coefficients (MFCC) and K-nearest neighbor (KNN) algorithms for identifying the material type in any arbitrary cracking processes. In the proposed method, MFCC and first-order differential MFCC parameters are extracted from the real-time AE signals. Material-specific cracking characteristics are identified and tuned for material recognition using a KNN classifier. The proposed method is validated using the AE signals of different types of materials acquired under uniaxial tension, and its effectiveness in identifying tool breakage is evaluated through case studies in the milling process. The result indicates that the recognition accuracy of up to 96.21 % can be achieved in tool breakage detection in milling operations by extracting material-specific fracture characteristics.

Unsupervised Detection of Tool Breakage: A Novel Approach Based on Time and Sensor Domain Data Analysis

Unsupervised Detection of Tool Breakage: A Novel Approach Based on Time and Sensor Domain Data Analysis

A comparison of four machine learning techniques and continuous wavelet transform approach for detection and classification of tool breakage during milling process

In machining, the tool condition has to be monitored by condition monitoring techniques to prevent damage by the use of tools and the workpiece. Cutting forces acting on the tool between zero and maximum values cause the cutting edge to crack and break. Predetection of this situation in the cutting tool is very important to prevent any negative situation that may occur. This study introduces a vibration-based intelligent tool condition monitoring technique to detect involute form cutter faults such as tool breakage at different levels during gear production on a milling machine. Machine learning algorithms such as artificial neural network, random forest, support vector machine, and K-nearest neighbor were used to detect the broken teeth and its level of breakage. According to the results obtained, it was observed that all the algorithms are successful in detecting faults in different teeth; also they have identification advantages according to different fault levels. In addition, the time and frequency domain analysis and continuous wavelet transform were used to determine the local faults. The developed machine learning-based detection performances compared the classical time and frequency domain analyses and continuous wavelet transform to prove the effectiveness and precision of the proposed methods. The results showed that all of the machine learning techniques have satisfactory performance to be used as fast and precise detection tools without complex calculations for detecting tool breakage.

On-Machine Tool Condition Monitoring System Using Image Processing

An automation system of machine tools can free operators from simple and hard labor by the automatic loading and unloading of workpieces, and allowance of unmanned operation during nights and holidays. To achieve complete shop floor operations, various works that are manually performed by operators need to be automated as well. It is also crucial to accurately monitor tools to detect tool wear and chip winding during machining. In this study, we propose an on-machine tool condition monitoring system that measures tool length and diameter using images to detect tool breakage and winding chips.

A Study on Tool Breakage Detection During Milling Process Using LSTM-Autoencoder and Gaussian Mixture Model

A Study on Tool Breakage Detection During Milling Process Using LSTM-Autoencoder and Gaussian Mixture Model

CFRP drilling process control based on spindle power consumption from real production data in the aircraft industry

Ongoing challenges in advanced manufacturing highlight the need of improved control of the production system, higher production speed, lower tool wear, top quality standards together with reduced material waste to minimize associated costs, time and environmental footprint. The organization of production resources through the integration of data along the value chain using Information Technologies is required to achieve these challenges. Thus, the emergence of the fourth industry revolution brings with it the organization of productive resources through computational intelligence and connectivity. This research seeks to analyze the spindle power consumption in Carbon-fiber-reinforced polymer composites (CFRPs) drilling operations as a process control indicator in terms of tool wear. This signal stands out among others available because it can be obtained in real time, with high quality and through a non-intrusive methodology. In particular, the study is framed in the real production system in factories of Airbus. The industrial process data were directly collected from the manufacturing plant in Getafe (in the Madrid-Spain region) and correspond to more than 3000 holes drilled with diamond-coated tungsten carbide tools. The variability of machining conditions in the aeronautical component drilling process and inherent noise level of signals obtained in industrial environments required the development of a data wrangling methodology to structure and clean the information. As a result, different magnitudes were obtained from spindle power consumption signal related to tool wear with low levels of dependence on drilling conditions. The conclusions of this work are directly applicable to the control of industrial production systems within the framework of Industry 4.0, searching new improvement opportunities through Data analytics and Artificial Intelligence such as tool breakage detection or the optimization of cycle times.

Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation

ABSTRACT Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added.

Wear and Breakage Detection of Integral Spiral End Milling Cutters Based on Machine Vision.

Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.

An imbalanced data learning method for tool breakage detection based on generative adversarial networks

An imbalanced data learning method for tool breakage detection based on generative adversarial networks

Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning

Monitoring of the cutting area with different type of sensors requires confirmation for composing sensor fusion to obtain longer tool life and high-quality product. The complex structure of machining and interaction between variables affect the influence of parameters on quality indicators. Using multiple sensors provide comparison of information acquired from different resources and make easier to decide about tool and workpiece condition. In this experimental research for the first time, five different sensors were adopted to a lathe for collecting data to measure the capability of each sensor in reflecting the tool wear. Cutting forces, vibration, acoustic emission, temperature and current measurements were carried out during turning of AISI 5140 with coated carbide tools. Considering the graphical investigation, the successes of sensors on detection of progressive flank wear and tool breakage were investigated. Besides, the effects of cutting parameters on measured variables were interpreted considering graphs. According to results, temperature and acoustic emission signals seem to be effective about 74% for flank wear. In addition, fuzzy logic based prediction of flank wear was performed with the assistance of temperature and acoustic emission sensors with high accuracy which demonstrates their availability for sensor fusion. Tool breakage occurs instantly which can prevent with the assistance of sensor signals and tangential and feed cutting forces, acoustic emission and vibration signals seem as reliable indicators for approaching major breakage. Sensor fusion based turning provides confirmed information which enables more reliable, robust and consistent machining.

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.

CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling

A tool breakage monitoring (TBM) system needs to detect tool breakage promptly in an unattended automation workshop. Traditional TBM systems that employ external sensors to acquire diagnostic signals such as spindle power for making judgments are inconvenient since extra sensors should be installed. Moreover, the signals from the external sensors are independent of the computer numerical control (CNC) system, and it is difficult to label them with the corresponding machining task information so that the target data can be segmented automatically. This paper proposes an incremental cost-sensitive support vector machine (ICSSVM) tool breakage monitoring method based on CNC internal data, which is inherently machining-task labeled and can be accessed directly from the CNC system without extra sensors. To satisfy the dataset’s integrity at the initial stage of model training, a simulation method that is based on the actual tool breakage characteristics is applied to generate simulated tool breakage data. The ICSSVM method combines cost-sensitive SVM (CSSVM) and modified incremental SVM (ISVM) to solve the imbalanced classification problem, which increases the misclassification probability of the minority class, train the model incrementally from the absence of samples, and guarantee high algorithm efficiency as the size of the dataset increases. It is proved that the ICSSVM algorithm has better algorithmic efficiency compared to the batch cost-sensitive SVM (BCSSVM). It is also proved that the ICSSVM algorithm has better imbalanced classification performance than batch SVM (BSVM), as assessed by the receiver operator characteristic (ROC) curves. The industrial practicability of the proposed method is verified by actual machining with a CNC system integrated with the TBM module.

Smart-sensor for tool-breakage detection in milling process under dry and wet conditions based on infrared thermography

The breakage of tools in the process of machining by removal of chip in industrial machinery is an important issue to consider because of the costs that can be generated by a setback in production or damage in parts of the machine-tool. High temperatures are one of the main causes that contribute to the damage of the tools and this problem has been researched in multiple studies; however, the use of refrigerant has not been considered due to its inherent interference in the process. The contribution of this work is the development of a smart-sensor for the timely detection of rupture of the tool in the milling process including refrigerant, which is specially designed with the application of infrared thermography and processing of thermal matrix obtained instead of the pseudo-color as commonly used. The outcome of this experimentation shows that with the smart-sensor, the condition of the tool in dry conditions and with coolant was obtained. Also, the smart-sensor provides the heat curves, images represented in different color palettes, the thermogram, and the average of the thermograms obtained during the monitoring of the cutting process.

Tool breakage detection from 2D workpiece profile using vision method

In-process tool breakage monitoring can significantly save cost and prevent damages to machine tool. In this paper, a machine vision approach was employed to detect the tool fracture in commercial aluminium oxide ceramic cutting tool during turning of AISI 52100 hardened steel. The contour of the workpiece profile was captured with the aid of backlighting during turning using a high-resolution DSLR camera with a shutter speed of 1/4000 s. The surface profile of the workpiece was extracted to sub-pixel accuracy using the invariant moment method. The effect of fracture in ceramic cutting tools on the surface profile signature of the machined workpiece using autocorrelation was studied. Fracture in the aluminum oxide ceramic tool was found to cause the peaks of autocorrelation function of the workpiece profile to decrease rapidly as the lag distance increased. The envelope of the peaks of the autocorrelation function was observed to deviate significantly from one another at different workpiece angles when the tool has fractured due to the continuous fracture of ceramic cutting insert during machining.

이송모터 전류신호를 이용한 공구파손 검출

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

The effect of cryogenic cooling and minimum quantity lubrication on end milling of titanium alloy Ti-6Al-4V

The cooling down of cutting temperature in machining is very important for the improvement of tool life, especially when dealing with work materials that have low thermal conductivity such as titanium alloy. In this study designed to investigate the machining performance of a variety of cooling methods, cryogenic, Minimum quantity lubrication (MQL), and flood cooling are performed on solid end milling of titanium alloy, Ti-6Al-4V. In particular, the effect of internal and external spray methods on cryogenic machining is analyzed with a specially designed liquid nitrogen spraying system by evaluating tool wear and cutting force at cutting conditions. The cutting force is also analyzed for tool breakage detection. As a result, the combination of MQL and internal cryogenic cooling improves tool life by up to 32% compared to conventional cooling methods. The cutting force is also reduced significantly by this combination of cooling and lubrication strategy of side end milling.

A PNN self-learning tool breakage detection system in end milling operations

With the advance of technology over the years, computer numerical control (CNC) has been utilized in end milling operations in many industries such as the automotive and aerospace industry. As a result, the need for end milling operations has increased, and the enhancement of CNC end milling technology has also become an issue for automation industry. There have been a considerable number of researches on the capability of CNC machines to detect the tool condition. A traditional tool detection system lacks the ability of self-learning. Once the decision-making system has been built, it cannot be modified. If error detection occurs during the detection process, the system cannot be adjusted.To overcome these shortcomings, a probabilistic neural network (PNN) approach for decision-making analysis of a tool breakage detection system is proposed in this study. The fast learning characteristic of a PNN is utilized to develop a real-time high accurate self-learning tool breakage detection system. Once an error occurs during the machining process, the new error data set is sent back to the PNN decision-making model to re-train the network structure, and a new self-learning tool breakage detection system is reconstructed. Through a self-learning process, the result shows the system can 100% monitor the tool condition. The detection capability of this adjustable tool detection system is enhanced as sampling data increases and eventually the goal of a smart CNC machine is achieved.

Detection of tool breakage during hard turning through acoustic emission at low removal rates

The paper deals with a new method for the detection and prediction of the catastrophic tool failure (CTF) of ceramic inserts through an acoustic emission (AE) technique and associated chip formation analysis during the hard turning of bearing steel 100Cr6. The capability of different AE sensors with respect to the different processes in the cutting zone during hard turning is discussed as an initial study. Then a new concept of process monitoring is suggested based on the application of two sensors and rationing of chosen parameters such as AErms, AEstrength and AEabsolute energy. The specific character of the formed chip is associated with raw AE signals. Conventional data processing of AE signals does not enable the different phases of tool wear to be clearly recognised or the prediction of CTF, while the new suggested concept does. This concept is based on the rationing of AE features recorded in different frequency ranges in connection with the different processes in the cutting zone during chip separation.