Tool Wear in Machining

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 10018

Special Issue Editor


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Guest Editor
Faculty of Mechanical Engineering and Technology, Rzeszow University of Technology, Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland
Interests: wood cutting; cutting tools wear; delamination; cutting signals; tool monitoring system; machine learning in machining systems

Special Issue Information

Dear Colleagues,

Drilling, milling and turning are the main machining processes that are of particular interest to metal and plastic specialists. Both the aviation and automotive industries require high-precision processing of the manufactured elements. Therefore, a lot of research is being carried out on these treatment techniques. This ensures constant development: machining techniques are constantly improved, new tools are developed, and new areas of application are sought. Measurements of cutting resistance, acoustic emission, and vibrations in the cutting process can be effective methods used to assess the wear condition of a cutting tool. One of the most significant advances in the manufacturing environment is the increasing use of tool- and process-monitoring systems. Today, many different types of sensors are available in combination with signal-processing technologies, and many advanced signal- and information-processing techniques have been invented and reported in scientific articles. However, only a few found their way into industrial applications. As such, we encourage all cutting process researchers to take part in this Special Issue of Machines, to present the state of knowledge in the field of measuring the wear of cutting tools, modern coatings used for cutting tools, and methods of processing signals from the cutting zone.

Dr. Krzysztof Szwajka
Guest Editor

Manuscript Submission Information

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Published Papers (5 papers)

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Research

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31 pages, 142483 KiB  
Article
Wear of Abrasive Tools during CMC Machining
by Franck Andrés Girot Mata, Mario Alfredo Renderos Cartagena, Unai Alonso Pinillos and Borja Izquierdo Aramburu
Machines 2023, 11(11), 1021; https://doi.org/10.3390/machines11111021 - 13 Nov 2023
Viewed by 1002
Abstract
Machining CMCs under productivity conditions while limiting tool wear and material damage is a challenge for applications such as jet aircraft engines and industrial turbines. This contribution focused on developing a method to characterize the wear of abrasive tools based on fractal dimensions. [...] Read more.
Machining CMCs under productivity conditions while limiting tool wear and material damage is a challenge for applications such as jet aircraft engines and industrial turbines. This contribution focused on developing a method to characterize the wear of abrasive tools based on fractal dimensions. This solution allows characterization of the state of the tool after each machining and identification of the type of damage to the tool (regular wear of the diamond grains, cleavage, or breakage) and its influence on the cutting forces, in addition to damage to the machined material and the quality of the machined surface. Thus, the chipped area and the maximum chipping are directly associated with the fractal dimension of the tool surface and the metal removal rate of the process. The quality of the surface (Sa, Sz, and Sq) is associated with the fractal dimension of the surface of the tool characterizing the state of the grinding wheel and the radial depth of cut ae characterizing the engagement of the tool in the CMC material. Moreover, the results also demonstrated that the use of an abrasive tool associated with cutting conditions close to milling and not grinding is a viable solution. Full article
(This article belongs to the Special Issue Tool Wear in Machining)
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14 pages, 4185 KiB  
Article
Detection for Disc Cutter Wear of TBM Using Magnetic Force
by Jialin Han, Hongjiang Xiang, Qiuyue Feng, Jiangbo He, Rong Li and Wensheng Zhao
Machines 2023, 11(3), 388; https://doi.org/10.3390/machines11030388 - 15 Mar 2023
Viewed by 1324
Abstract
To replace the worn-out cutter of tunnel boring machines timely, it is crucial to inspect the cutter’s wear. In this work, a novel detection method based on magnetic force is proposed to overcome the drawback of nonlinearity in current detecting technology. The principle [...] Read more.
To replace the worn-out cutter of tunnel boring machines timely, it is crucial to inspect the cutter’s wear. In this work, a novel detection method based on magnetic force is proposed to overcome the drawback of nonlinearity in current detecting technology. The principle is that the magnetic force between the cutter and the permanent magnet linearly decreases with increasing wear. Firstly, the magnetic force is investigated by the finite element simulation to find the optimal placement of the permanent magnet to realize both high linearity and sensitivity. Secondly, a highly-sensitive force sensor with an S shape is designed to measure the magnetic force. The four strain gauges in the force sensor are combined into a Wheatstone bridge to suppress the common-mode effect, such as temperature. Experimental testing on the magnetic force is performed to verify the feasibility of the detection method. The testing result shows that the magnetic force linearly decreases with the increasing wear loss at a rate of −793 mN/mm. The accuracy of the detecting method approaches 1 mm, which is of the same order of magnitude as those in previous studies. Full article
(This article belongs to the Special Issue Tool Wear in Machining)
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19 pages, 4241 KiB  
Article
Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory
by Xingang Xie, Min Huang, Yue Liu and Qi An
Machines 2023, 11(1), 94; https://doi.org/10.3390/machines11010094 - 11 Jan 2023
Cited by 6 | Viewed by 2273
Abstract
Herein, to accurately predict tool wear, we proposed a new deep learning network—that is, the IE-Bi-LSTM—based on an informer encoder and bi-directional long short-term memory. The IE-Bi-LSTM uses the encoder part of the informer model to capture connections globally and to extract long [...] Read more.
Herein, to accurately predict tool wear, we proposed a new deep learning network—that is, the IE-Bi-LSTM—based on an informer encoder and bi-directional long short-term memory. The IE-Bi-LSTM uses the encoder part of the informer model to capture connections globally and to extract long feature sequences with rich information from multichannel sensors. In contrast to methods using CNN and RNN, this model could achieve remote feature extraction and the parallel computation of long-sequence-dependent features. The informer encoder adopts the attention distillation layer to increase computational efficiency, thereby lowering the attention computational overhead in comparison to that of a transformer encoder. To better collect location information while maintaining serialization properties, a bi-directional long short-term memory (Bi-LSTM) network was employed. After the fully connected layer, the tool-wear prediction value was generated. After data augmentation, the PHM2010 basic dataset was used to check the effectiveness of the model. A comparison test revealed that the model could learn more full features and had a strong prediction accuracy after hyperparameter tweaking. An ablation experiment was also carried out to demonstrate the efficacy of the improved model module. Full article
(This article belongs to the Special Issue Tool Wear in Machining)
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15 pages, 3521 KiB  
Article
ShuffleNet v2.3-StackedBiLSTM-Based Tool Wear Recognition Model for Turbine Disc Fir-Tree Slot Broaching
by Shenshun Ying, Yicheng Sun, Fuhua Zhou and Lvgao Lin
Machines 2023, 11(1), 92; https://doi.org/10.3390/machines11010092 - 11 Jan 2023
Cited by 1 | Viewed by 2122
Abstract
At present, deep learning technology shows great market potential in broaching tool wear state recognition based on vibration signals. However, traditional single neural network structure is difficult to extract a variety of different features simultaneously and has low robustness, so the accuracy of [...] Read more.
At present, deep learning technology shows great market potential in broaching tool wear state recognition based on vibration signals. However, traditional single neural network structure is difficult to extract a variety of different features simultaneously and has low robustness, so the accuracy of wear status recognition is not high. In view of the above problems, a broaching tool wear recognition model based on ShuffleNet v2.3-StackedBiLSTM is proposed in this paper. The model integrates ShuffleNet v2.3, which has been channel shuffling, and StackedBiLSTM, a long and short-term memory network, to effectively extract spatial and temporal features for tool wear state recognition. Based on the innovative recognition model, the turbine disc fir-tree slot broaching experiment is designed, and the performance index system based on confusion matrix is adopted. The experimental research and results show that the model has outstanding accuracy, precision, recall, and F1 value, and the accuracy rate reaches 99.37%, which is significantly better than ShuffleNet v2.3 and StackedBiLSTM models. The recognition speed of a single sample was improved to 8.67 ms, which is 90.32% less than that of the StackedBiLSTM model. Full article
(This article belongs to the Special Issue Tool Wear in Machining)
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Review

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30 pages, 3280 KiB  
Review
Approaches for Preventing Tool Wear in Sheet Metal Forming Processes
by Tomasz Trzepieciński
Machines 2023, 11(6), 616; https://doi.org/10.3390/machines11060616 - 03 Jun 2023
Cited by 5 | Viewed by 2477
Abstract
Sheet metal forming processes, the purpose of which is to give the shaped material appropriate mechanical, dimensional and shape properties, are characterised by different values of unit pressures and lubrication conditions. Increasing the efficiency of tool work by increasing their durability, efficiency and [...] Read more.
Sheet metal forming processes, the purpose of which is to give the shaped material appropriate mechanical, dimensional and shape properties, are characterised by different values of unit pressures and lubrication conditions. Increasing the efficiency of tool work by increasing their durability, efficiency and reliability is still one of the main indicators of increasing production efficiency. Tool wear in metal forming technologies significantly differs from the character of wear in other methods of metalworking, such as machining. This article presents the characteristics of tool wear mechanisms used in sheet metal forming. Possibilities of increasing the durability of tools by applying coatings produced by laser techniques, chemical vapour deposition and chemical vapour deposition are also discussed. Great emphasis is placed on self-lubricating and functional materials and coatings. Current trends in lubricants and lubrication methods in sheet forming, including tool texturing, are also presented. Full article
(This article belongs to the Special Issue Tool Wear in Machining)
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