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"Tool life"
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Tool use in animals : cognition and ecology
\"The last decade has witnessed remarkable discoveries and advances in our understanding of the tool using behaviour of animals. Wild populations of capuchin monkeys have been observed to crack open nuts with stone tools, similar to the skills of chimpanzees and humans. Corvids have been observed to use and make tools that rival in complexity the behaviours exhibited by the great apes. Excavations of the nut cracking sites of chimpanzees have been dated to around 4-5 thousand years ago. Tool Use in Animals collates these and many more contributions by leading scholars in psychology, biology and anthropology, along with supplementary online materials, into a comprehensive assessment of the cognitive abilities and environmental forces shaping these behaviours in taxa as distantly related as primates and corvids\"-- Provided by publisher.
Book
Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM
To enhance production quality, productivity and energy consumption, it is paramount to predict the remaining useful life (RUL) of a cutting tool accurately and efficiently. Deep learning algorithm-driven approaches have been actively explored in the research field though there are still potential areas to further enhance the performance of the approaches. In this research, to improve accuracy and expedite computational efficiency for predicting the RUL of cutting tools, a novel systematic methodology is designed to integrate strategies of signal partition and deep learning for effectively processing and analysing multi-sourced sensor signals collected throughout the lifecycle of a cutting tool. In more detail, the methodology consists of two sub-systems: (i) a Hurst exponent–based method is developed to effectively partition complex and multi-sourced signals along the tool wear evolution, and (ii) a hybrid CNN-LSTM algorithm is designed to combine feature extraction, fusion and regression in a systematic means to facilitate the prediction based on segmented signals. The system was validated using a case study with a large set of databases with multiple cutting tools and multi-sourced signals. Comprehensive comparisons between the proposed methodology and some other mainstream algorithms, such as CNN, LSTM, DNN and PCA, were carried out under the conditions of partitioned and unpartitioned signals. Benchmarks showed that, based on the case study in this research, the prediction accuracy of the proposed methodology reached 87.3%, which is significantly better than those of the comparative algorithms.
Journal Article
Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision
One of the essential requirements for intelligent manufacturing is the low cost and reliable predictions of the tool life during machining. It is crucial to monitor the condition of the cutting tool to achieve cost-effective and high-quality machining. Tool conditioning monitoring (TCM) is essential to determining the remaining useful tool life to assure uninterrupted machining to achieve intelligent manufacturing. The same can be done by direct and indirect tool wear measurement and prediction techniques. In indirect methods, the data is acquired from the sensors resulting in some ambiguity, such as noise, reliability, and complexity. However, in direct methods, the data is available in images resulting in significantly less chances of ambiguity with the proper data acquisition system. The direct methods, which provide higher accuracy than indirect methods, involve collecting images of worn tools at different stages of the machining process to predict the tool life. In this context, a novel tool wear prediction system is proposed to examine the progressive tool wear utilizing the artificial neural network (ANN). Experiments were performed on AISI 4140 steel material under dry cutting conditions with carbide inserts. The cutting speed, feed, depth of cut, and white pixel counts are considered as input parameters for the proposed model, and the flank wear along with remaining tool life is predicted as the output. The worn tool images were captured using an industrial camera during the turning operation at regular intervals. The ANN training set predicts the remaining useful tool life, especially the sigmoid function and rectified linear unit (ReLU) activation function of ANN. The sigmoid function showed an accuracy of 86.5%, and the ReLU function resulted in 93.3% accuracy in predicting tool life. The proposed model’s maximum and minimum root mean square error (RMSE) is 1.437 and 0.871 min. The outcomes showcased the ability of image processing and ANN modeling as the potential approach for developing a low-cost industrial tool condition monitoring system that can measure tool wear and predict tool life in turning operations.
Journal Article
Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
Real-time tool wear prediction and its remaining useful life (RUL) estimation is an important part of the development of a smart machining system while it is practically complex. A two-step framework proposed based on the statistical correlation of the experimentally measured cutting tool vibration data with the flank wear progression and estimation of the cutting tool RUL by the construction of stochastic tool life probability predictive models. The machining experiments are conducted on the IN718 superalloy with uncoated WC tools under the varied conditions of cutting speed and feed to acquire the data of flank wear and associated tool vibration data. The results of confirmation experiments show the statistical correlation constructed is practically viable for in-process flank wear prediction at any time of instance during machining with any set cutting conditions using the real-time tool vibration monitoring. The in-process observation of 1.5 g tool acceleration during machining with 60 m/min cutting speed and 0.1 mm/tooth feed signifies 15% of the cutting tool failure probability, and its remaining useful life is 12.91 min. For 50% of tool reliability machining with 0.1 mm/tooth feed and 60, 90 and 120 m/min cutting speed, tool accelerations of 2.01, 3.08 and 3.98 g reflect that the respective exhausted tool lives are 12, 8 and 6 min and the respective remaining useful lives are 8, 6 and 5 min. Hence, based on the presented analysis and results, it is envisaged the proposed framework is reliable and robust for in-process cutting tool condition prediction based on the real-time tool vibration monitoring for its adoption to develop a smart machining system with autonomous decision-making capability.
Journal Article
Prediction of PCBN tool life in hard turning process based on the three-dimensional tool wear parameter
The ideal scenario for the implementation of a machining process is to be able to predict tool performance without the need to conduct practical experiments. However, in an industrial environment, each set of machining conditions is unique, since the machine-tool conditions, machined material, cutting tool, and fixture system can vary. This can lead to differences between the predicted values and practical results. In this context, the aim of this research was to show and discuss a tool performance test methodology and a tool-life prediction model using the three-dimensional (volumetric) wear parameter
W
RM
(volume of material removed from the tool) applied to hard turning with PCBN tools. The wear parameter
W
RM
is measured at the beginning of the tool life (up to 25%) by focus variation microscopy (FVM). The tool wear rate (WR
RM
) is then calculated based on the ordinary least squares (OLS) method, and the tool life is estimated (TW
RM
) adopting the volume of material removed from the tool (WR
Mmax
) as the criteria for the end of tool life. The tool-life model developed was capable of predicting the tool life with errors below 4% at the higher values of cutting speed adopted (
v
c
= 150–187.5 m/min), that is, the cutting speeds applied industrially. The methodology adopted and the model developed represent a significant time reduction in the experimental machining tests, streamlining the research and development of the cutting tool grades, as well as the machining process optimization.
Journal Article
Optimizing cutting speeds in a machining bottleneck stage in order to reach the takt time with the minimum machining cost
The increasing demand for machined components of superior quality at competitive costs has driven machine and cutting tools manufacturers to innovate in machine tool technologies and cutting tools materials. Consequently, optimizing the machining process becomes an important subject of research. Particularly, optimizing machining parameters like cutting speed, depth of cut and feed rate stands out. Literature review highlights that optimization considers various “objective functions” such as minimum production cost, maximum production rate, and minimum energy consumption. However, one aspect not objectively considered is the customer's demand within a specific timeframe, known as takt time. The relevance, originality and contribution of this work is the development of a method that, considering the current machining parameters and the takt time for a given machining stage, defines the optimized cutting speed for each cutting tool to meet the takt time at the lowest production cost. An algorithm based on classical equations for calculating machining times and costs for machining processes with multiple cutting tools was developed. A software in Excel receives process data, “x and K” coefficients from Taylor’s life equation and calculates the optimized cutting speeds. The present method was applied in a heavy machining industrial environment using actual production data and machining parameters. Taylor coefficients x and K were obtained through cutting tool life tests carried out on the part under analysis. Results showed potential machining cost savings of up to 6.2% compared to traditional methods for obtaining takt time on shop floor.
Journal Article
Tool Wear in Nickel-Based Superalloy Machining: An Overview
Nickel-based superalloys have been widely used in the aerospace, petrochemical, and marine fields and others because of their good oxidation resistance, corrosion resistance, stability, and reliability at various temperatures. However, as a nickel-based superalloy is a kind of processed material, in the cutting process a large amount of cutting heat is generated due to the interaction between the tool and the workpiece. At the same time, the low thermal conductivity of the workpiece causes a large amount of cutting heat to accumulate at the contact point, resulting in serious tool wear, reduced tool life, frequent tool changes, and other problems, which increase the production cost of the enterprise. This paper introduces the tool wear mechanisms (abrasive wear, adhesive wear, plastic deformation, chemical wear, etc.) in the machining process of nickel-based superalloys and summarizes the research status of failure mechanisms, tool wear optimization, etc. Based on a review of the existing research, it was found that the purpose of adding tool coatings, optimizing tool materials and cutting parameters, or improving the cutting environment is to control the heat during the processing of nickel-based superalloys to improve the tool environment and prolong the service life. The development prospects of tool wear prevention measures in the field of nickel-based alloy machining are also described.
Journal Article
An improved approach to tool life promotion concerning cutting edge microgeometry
The severe wear of cemented carbide tool can lead to problems such as short tool life and poor part surface integrity. Fortunately, it has been found that the tool wear may be reduced by modifying the cutting edge microgeometry. However, there are a few studies that offer specific instructions for this modification. In addition, it is costly to realize this for machining tools through merely experiments. Therefore, in this paper, an improved approach is proposed by combining the finite-element (FE) simulation method with a user-defined wear rate model. To achieve this, a new wear rate model was proposed at first, regarding the wear mechanisms of the carbide tool during machining of Inconel 718 alloy. Then, four sets of carefully designed cutting tests and FE simulations were carried out to calibrate and verify the new model. At last, extensive FE simulations were conducted to investigate the influence of tool edge microgeometry on tool life. The simulated results indicate that the tool life increases with the increase of the form factor K within a given range. This finding on optimized tool edge was further verified by performing a group of cylindrical turning experiments. The experimental measurements reveal that the tool life has increased by 24% and 50% maximum when increasing K from 0.9 to 2.8 under the two given cutting conditions.
Journal Article
Remaining Useful Life Prediction of Cutting Tools Based on a Depthwise Separable TCN-BiLSTM Model with Temporal Attention
The prediction of remaining tool life is critical in practical manufacturing, as it enables early identification of tool wear conditions, preventing downtime and quality issues caused by tool failure. This, in turn, helps reduce costs and improve production efficiency. To address the limitations of traditional models in modeling complex temporal dependencies and capturing critical moment information, a hybrid deep learning model based on a TCN-BiLSTM-Attention architecture was proposed. The model first enhanced low-level features from raw multivariate signals using an initial feature extraction module. Local temporal patterns were then extracted by an improved Temporal Convolutional Network (TCN), followed by a Bidirectional Long Short-Term Memory (BiLSTM) network to capture global sequential dependencies. An attention mechanism was further introduced to strengthen the model’s ability to focus on and select critical features over time, enabling deeper fusion and modeling of complex temporal dynamics. Finally, a multi-layer regression network was applied to predict the remaining tool life. Experimental validation on the PHM 2010 dataset demonstrates that the proposed model outperforms other comparative models. Among the three evaluation metrics (RMSE, MAE, and R2), the optimal results are 0.0184, 0.0127, and 0.9959 respectively, which fully proves that the model exhibits excellent prediction performance. Ablation studies further confirm the contribution of each module to the overall performance improvement.
Journal Article
Sensor data anomaly detection and correction for improving the life prediction of cutting tools in the slot milling process
Effective cutting tool life prediction is significant for ensuring processing quality and improving production efficiency. Data-driven prediction methods have been widely used. However, traditional methods assume that there are high-quality sensor data, whereas, in practice, factors such as poor installation of sensors and environmental interference often lead to poor-quality data, leading to unreliable analysis results and incorrect decisions. Thus, in this paper, a sensor data anomaly detection and correction method is proposed. It mainly includes four parts: data preprocessing, abnormal data detection, correction of detected abnormal data, and tool life prediction and evaluation. First, the raw condition monitoring data are preprocessed for feature extraction and health index (HI) construction. Second, the HIs of historical training samples are clustered based on the dynamic time warping (DTW) algorithm, and the abnormal data are detected based on error calculation with a preset error threshold. Third, the detected abnormal data are optimized via similarity matching using
k
-nearest neighbors with dynamic time warping (KNN-DTW). Finally, the optimized data are used for tool life prediction and evaluation. The proposed method has been tested on real data acquired from a turbine factory. The comparison results show that the prediction effect can be significantly improved after adopting the proposed method, which verifies the necessity of sensor data anomaly detection and correction.
Journal Article