Cutting Tool Condition Research Articles

Gaussian process regression model incorporated with tool wear mechanism

Cutting tool condition directly affects machining quality and efficiency. In order to avoid severely worn tools used during machining process and fully release the remaining useful life in the meanwhile, a reliable evaluation method of remaining useful life of cutting tools is quite necessary. Due to the variation of cutting conditions, it is a challenge to predict remaining useful life of cutting tools by a unified model. In order to address this issue, this paper proposes a method for predicting the remaining useful life of cutting tools in variable cutting conditions based on Gaussian process regression model incorporated with tool wear mechanism, where the predicted value at adjacent moments is constrained to a linear relationship by the covariance matrix of Gaussian model based on the assumption of progressive tool wear process, so the wear process under continuous changing conditions can be modelled. In addition to that, the input feature space and the output of the model are also enhanced by considering the tool wear mechanism for improving prediction accuracy. Machining experiments are performed to verify the proposed method, and the results show that the proposed could improve the prediction of tool remaining useful life significantly.

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition; a worn tool generally produces low-quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 min of machining, which was a necessary amount of time to exceed (300 μm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level, and vibration comportment. First, spectral kurtosis has been calculated for the twenty-one performed runs signals; this step has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it did not give significant information about wear evolution. The signals have then been decomposed with ICEEMDAN and the energy of the high-frequency modes has been calculated. It has been found that the energy of the optimal frequency ICEEMDAN modes has increased in proportion to the increase of surface roughness degradation and thus, to tool wear increase. Therefore, IMF’s energy can be used for tool wear condition monitoring.

Root cause and vibration analysis to increase veneer manufacturing process efficiency: a case study on an industrial peeling lathe

Root cause and vibration analysis is applied to a veneer peeling lathe to increase its efficiency and reduce wood waste. Excessive veneer thickness variations, repetitive damage of machine component and high vibration levels of the lathe are identified as interrelated issues. A root cause analysis is conducted to finally select both design and operating factors, which were then investigated to quantify their effects on the vibratory lathe response and process efficiency. On-site vibration measurements for different operating speeds, cutting tool conditions and log sizes are analyzed from RMS, kurtosis and crest factors for a 95% confidence interval. Based on the statistical significance, the effect of the operating conditions on the vibration energy level and impulsivity of the lathe is obtained. The analysis confirms that (1) the vibration level of the peeling process is significantly affected by the operating speed and cutting tool condition; (2) the signal impulsivity characterized by kurtosis varies with the process speed; and (3) the log size affects process efficiency. In addition, finite element modal analysis is applied to the lathe carriage structure to identify whether a transient resonance occurs during the peeling process. Finally, the study recommends the optimal lathe operating speed found for reducing vibration levels and incomplete log peeling.

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process.

This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.

Experimental study on cutting flexible textile materials and the sharpening process of a vibrating blade

Blade wear is an essential factor that influence textile cutting quality and precision and it is important to seek an appropriate method to predict the cutting tool conditions. In this study, the vertical force range of the blade are measured to determine the vibrating blade’s abrasive wear during different cutting conditions of superimposed plies of a flexible material “Denim”. The relationship between cutting force against the abrasive wear is studied and identified through the evolution of the roughness and the cutting edge radius, which provides a technical foundations for online monitoring of the blade’s wear conditions and cutting quality. Moreover, this study analyzes the sharpening conditions of three different HSS blades in order to regenerate the suitable tool’s cutting edge. It is shown that with incorporation of the vertical cutting force factor we can predict conveniently the tool wear condition and start the sharpening procedure at the accurate time.

Review on tool condition classification in milling: A machine learning approach

A cutting tool sustains & preserves the economy of machining activity. Inclusion of unforeseen faults in the cutting tool affects machining accuracy, product quality, and overall efficiency. The efficient supervision of a cutting tool has become essential in modern manufacturing scenario. Health monitoring is a key aspect of prognostic maintenance. It facilitates monitoring of a cutting tool condition for changing in certain parameters, to diagnose the noteworthy change in it. In an era of Artificial Intelligence (AI), the applicability of Machine Learning (ML) in health monitoring is truly commanding as it examines the history and presents signatures for prediction of pre-defined classes. Moreover, advancements in instrumentations, signal processing, data science & analytics have captured the attention of researchers. This paper presents a review of health monitoring techniques applied for cutting tools particularly using vibration as a signal acquisition parameter and machine learning approach used for fault classification and prediction.

An Improved Deep Learning Model for Online Tool Condition Monitoring Using Output Power Signals

Something like normal functionality of tools in a manufacturing process is typically designed to ensure reliability, where fast and accurate identification of tool abnormal operation plays a vital role in intelligent manufacturing. In this study, a novel method is proposed to assess the cutting tool condition, which consists of a convolutional neural network with wider first-layer kernels (W-CONV), and long short-term memory (LSTM). The analysis benefits from the use of output power signals from the cutting tool, since they can be obtained easily and efficiently, enabling the proposed method to be applicable in practical operation for online condition monitoring. Moreover, effectiveness of the proposed method is investigated, using test data from cutting tools at various tool wear conditions. Results demonstrate that with the proposed method, tool wear condition can be identified accurately and efficiently. Furthermore, with test data collected at cutting tools with different sizes, the robustness of the proposed method can be further clarified.

Application of laser interferometry to analyze cutting tool state during machining

The efficiency of a cutting tool can be enhanced through stress–strain and temperature studies. Existing mathematical methods implement simplified boundary conditions, and experimental methods that are either inapplicable to real working conditions or lack the necessary accuracy. This study aims to develop novel experimental methods for stress–strain and temperature field analyses. The approaches entail recording the side deformation fields of the cutting tool by laser interferometry during its operation, separating the deformation fields caused by the cutting forces and heating, as well as calculating the stress–strain and temperature fields using the Young’s modulus, Poisson’s ratio, and coefficient of linear thermal expansion of the tool material. The advantages of these methods include their applicability under real cutting conditions and the possibility to study the stress–strain and temperature fields of a tool during non-stationary operation by high-speed video recording. The study proves the efficiency of the proposed methods by the orthogonal machining of difficult-to-cut steel disc using a cemented carbide tool with positive rake angle. As a result, the temperature and principal stress fields in the tool were determined. Developed methods can help in the study of cutting tool efficiency.

A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC)

A cutting tool condition is always considered as a key contributor to the machining operation. In-process failure of the tool directly affects the surface roughness of a workpiece, the power consumption of prime mover, and endurance of the process, etc. Thus supervisory system envisioning its health prediction is drawing industry attention. This needs to be adopted by a framework which institutes knowledge built data with the intent to predict defects earlier and prevent it from failure. In this context, the application of ‘Machine Learning’ (ML) methodology would assist the classification of tool condition and its prediction. In an attempt to monitor the multipoint tool insert health, an ML-based approach is presented in this paper. The time-domain vibration response for defect-free and various faulty configurations of four insert tool were collected during face milling performed on a vertical machining center (VMC). Further statistical features were extracted by designing an event-driven algorithm in Visual Basic Environment (VBE). Features exhibited in the Decision Tree (DT) generated by the J48 algorithm serve as ‘most significant’ amongst all extracted features; hence were selected for further classification. Finally, various tool conditions were categorized using six different ‘supervised-tree-based’ algorithms and a comparative study is presented to find the best possible classifier.

Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

In the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

Development of family of artificial neural networks for the prediction of cutting tool condition

Development of family of artificial neural networks for the prediction of cutting tool condition

Tool Condition Monitoring when Hard Machining

Article presents results of research activities in the area of the cutting tool condition monitoring using force signals as indices of tool wear and tool failure when hard turning. Cutting force authentically reflects changes in the tool during cutting with good accuracy. Relationship between cutting force and wear depends on work-piece/cutting tool interface and on changes of cutting conditions. Therefore, modelling as well as monitoring of tool wear is quantitative different for each work-piece/cutting tool interface and highly sensitive from cutting conditions changes, which obstruct their mutual comparison. The passive (radial) force became the largest among the three force components when cutting of hardened steel. However, the most sensitive to the changes of flank and nose wear was the mean feed force component although the passive force indicates significant increase, too. The character of dynamic value of force signal change when damage of cutting tip is presented.

Influence of Size Effect on Cutting Edge Rounding and Surface Roughness in Micro-Milling of Ti-6Al-4V

The quality of cutting depends most on the cutting tool condition. Towards having a good quality finish, cutting tolerance becomes a major concern, especially when machining at micro-scale where highly precise cutting is desired. This research investigates the size-effect during the micro-milling of Ti-6Al-4V under dry condition where the observations were made on cutting edge rounding (CER) and workpiece surface roughness. The result showed that the lower the feed rate, the greater rounding on the cutting edges were observed. Similar trend in result was obtained when measuring the surface roughness. The best feed rate for both observations was at 60 mm/min, where this setting has brought the mechanism to shearing, as the ratio between undeformed chip thickness and cutting edge radius started at 1.

In-process tool condition forecasting based on a deep learning method

It is widely acknowledged that machining precision and surface integrity are greatly affected by cutting tool conditions. In order to enable early cutting tool replacement and proactive actions, tool wear conditions should be estimated in advance and updated in real-time. In this work, an approach to in-process tool condition forecasting is proposed based on a deep learning method. A long short-term memory network is designed to forecast multiple flank wear values based on historical data. A residual convolutional neural network is built to enable in-process tool condition monitoring, using raw signals acquired during the machining process. The integration of them enables in-process tool condition forecasting. Median-based correction and mean-based correction are adopted to improve the accuracy. IEEE PHM 2010 challenge data has been used to illustrate and validate this approach. Experimental study and quantitative comparisons showed that future flank wear values could be precisely forecasted during the machining process. The proposed approach contributes to prompt and reliable cutting tool condition forecasting, which will support the decision-making about cutting tool replacement in data-driven smart manufacturing.

A Study on Cyber-physical System Architecture to Predict Cutting Tool Condition in Machining

A Study on Cyber-physical System Architecture to Predict Cutting Tool Condition in Machining

Milling cutter condition monitoring using machine learning approach

The cutting tool condition drives the economy of machining processes in manufacturing industry. The failures in cutting tool are unbearable and affect the drive of machine tool which reduces life. Hence it necessitates reducing power consumption using monitoring cutting tool condition and hence requires an efficient supervision to monitor and predict faults. Simply stated, the condition which curtails cutting tool life highlighted before it turns into a tool wear, breakage and failure. This ensures optimized and effective use of a cutting tool, saves maintenance/repair time, enhances constancy in a process etc. The recent development in Machine Learning (ML) and its applicability for condition monitoring approach has drawn attention of researchers. Machine learning examines existing and past indications to predict conditions in future. This paper presents machine learning based condition monitoring of milling cutter of vertical machining centre (VMC). The vibration signals acquisition of 4 insert milling cutter is carried out with healthy and various fault conditions. The Visual Basic (VB) code and script is used to extract statistical features and decision tree algorithm is used to select relevant features. The different conditions of milling cutter are classified using tree family classifiers. The effort made in this work is to check applicability of ML approach for milling cutter fault diagnosis for reducing power consumption of drive of machine tool.

Tool wear classification using time series imaging and deep learning

Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

Minimization of Cutting Force by Optimizing the Cutting Parameters Using Taguchi Method in Turning

The changes in magnitude of cutting force demonstrate the different occurrences in turning. The pattern of cutting force signal illustrates the cutting tool conditions and also corresponds to the changes in the cutting parameters. Obtaining an optimal cutting condition would significantly minimize the cutting force in turning, can reduce respective power consumption and consequently, extent the life of cutting tool. Choosing the optimal cutting parameters in turning would safeguard the cutting tool from catastrophe and could ensure a better surface finish of workpiece. From the orthogonal array and S/N ratio, the cutting speed of 120 m/min, feed rate of 0.32 mm/rev and depth of cut of 1 mm is found to be optimum cutting parameters. The magnitude of cutting force is found to be minimal and the surface finish is seen to be comparatively low at this cutting condition. The tool life at this cutting condition is found to be the longest for that particular tool – workpiece combination.

Chip Morphology in Turning of AZ91D Magnesium Alloy under Different Machining Conditions

In recent years, there has been an increase in research into industrial applications, especially in the machining of lightweight metals. In this study, the effect of processing of AZ91D magnesium alloy in different parameters (machining medium, cutting speed, depth of cut, feed rate and cutting tool types) on the force, chip formation and cutting tool conditions were investigated. The experiments were designed using the L18 orthogonal array of the Taguchi method. Thanks to the Taguchi method, time and cost savings were achieved by reducing the number of tests required. Then, the effect of the turning parameters on the force values was obtained by analysis of variance. The most important parameters affecting the force values were the depth of cut and the feed rate. As a result, only the cutting speed has a significant effect on the chip type.

FE modelling of CFRP machining- prediction of the effects of cutting edge rounding

Manufacturing of carbon fibre components for the aerospace industry often requires an edge trimming operation for pre-assembly. In CFRP machining it is necessary to have a sharp cutting edge to prevent machining defects such as fibre pull-out, surface pitting, matrix burning and un-cut fibres. Counter to this requirement, carbon fibres, which are highly abrasive, generate rapid rounding of the cutting tool edge. In this work, generated machining forces due to cutting edge rounding, in a milling process, will be predicted by numerical simulation for different cutting edge radius. Models have implemented adaptive convergence control and progressive re-meshing of the tool-chip interface. FE models have been applied successfully to predict the effects of machining parameters with an unworn cutting tool, with a maximum difference of 28 % between FE and experiment. However, the prediction of cutting force was found to be under-predicted for the used cutting tool condition.