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Keywords = end milling cutter

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17 pages, 4636 KiB  
Article
Chip Flow Direction Modeling and Chip Morphology Analysis of Ball-End Milling Cutters
by Shiqiang Zhou, Anshan Zhang, Xiaosong Zhang, Maiqi Han and Bowen Liu
Coatings 2025, 15(7), 842; https://doi.org/10.3390/coatings15070842 - 18 Jul 2025
Viewed by 295
Abstract
Ball-end milling cutters are normally used for complex surface machining. During the milling process, the tool posture and cutting parameters of the ball-end milling cutters have a significant impact on chip formations and morphological changes. Based on the Cutter Workpiece Engagement (CWE) model, [...] Read more.
Ball-end milling cutters are normally used for complex surface machining. During the milling process, the tool posture and cutting parameters of the ball-end milling cutters have a significant impact on chip formations and morphological changes. Based on the Cutter Workpiece Engagement (CWE) model, this study establishes a chip flow model for ball-end milling cutters with consideration of the tool posture variation. The machining experiments of Ti-6Al-4V with a 15° inclined plane and different feed directions were carried out. The influence mechanism of time-varying tool posture on chip formation was systematically investigated. The results reveal an interaction between the chip flow direction and the cutting velocity direction. The included angle between the chip flow directions at the maximum and minimum contact points in the CWE area affects the degree of chip curling, with a smaller angle leading to weaker curling. This research provides a theoretical foundation for the optimization of posture parameters of ball-end milling cutters and expounds on the influence of the chip flow angle on chip deformation. Full article
(This article belongs to the Special Issue Cutting Performance of Coated Tools)
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23 pages, 6966 KiB  
Article
Optimizing Dual-Microstructure Parameters in Ball-End Milling Tools: Synergistic Effects and Parameter Combination Analysis
by Qinghua Li, Qingyu Guan, Yi Ji, Wenyang Xu and Tiantian Xu
Appl. Sci. 2025, 15(11), 6329; https://doi.org/10.3390/app15116329 - 4 Jun 2025
Viewed by 363
Abstract
To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back [...] Read more.
To address the issues of high cutting speeds and low surface precision during milling, this study investigates the effects of front and back cutting face microstructures on ball-end milling cutters processing 304 stainless steel. Firstly, a theoretical energy model for front and back cutting face microstructures is established to verify the feasibility of embedding microstructures. Then, finite element analyses are conducted on cutters with varying microstructure parameters on front and back cutting faces to determine reasonable parameter ranges. Parameter combinations are subsequently used to manufacture front/back microstructured cutters, which undergo FEA validation. Finally, milling experiments are designed with milling forces, tool wear, and workpiece surface roughness as evaluation metrics. The results demonstrate that front/back microstructured cutters reduce milling forces by 19.4%, cutting temperatures by 19%, and workpiece surface roughness (Sa) by 43% compared to non-microstructured cutters, while significantly mitigating tool wear. Full article
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31 pages, 7519 KiB  
Article
An Experimental Investigation into Trochoidal Milling for High-Quality GFRP Machining
by Ondřej Bílek, Martin Řezníček, Andrzej Matras, Tomáš Solařík and Lubomír Macků
Materials 2025, 18(7), 1669; https://doi.org/10.3390/ma18071669 - 5 Apr 2025
Viewed by 2630
Abstract
This study investigates the effectiveness of trochoidal (adaptive) milling in machining Glass Fiber Reinforced Polymer (GFRP), emphasizing its potential advantages over conventional milling. Six coated solid carbide end mills, each with distinct geometries, were evaluated under identical conditions to assess the cutting forces, [...] Read more.
This study investigates the effectiveness of trochoidal (adaptive) milling in machining Glass Fiber Reinforced Polymer (GFRP), emphasizing its potential advantages over conventional milling. Six coated solid carbide end mills, each with distinct geometries, were evaluated under identical conditions to assess the cutting forces, surface quality, dimensional accuracy, burr formation, chip size distribution, and tool wear. Trochoidal milling demonstrated shorter cycle times—up to 23% faster—and higher material removal rates (MRRs), while conventional milling provided superior dimensional control and smoother surfaces in certain fiber-sensitive regions. A four-tooth cutter with a low helix angle (10°) and aluminum-oxide coating delivered the best overall performance, balancing minimal tool wear with high-quality finishes (arithmetic mean roughness, Ra, as low as 1.36 μm). The results indicate that although conventional milling can exhibit a 25%-lower RMS cutting force, its peak forces and extended machining times may limit the throughput. Conversely, trochoidal milling, when coupled with an appropriately robust tool, effectively manages the cutting forces, improves the surface quality, and reduces the machining time. Most chips produced were less than 11 μm in size, highlighting the need for suitable dust extraction. Notably, a hybrid approach—trochoidal roughing followed by conventional finishing—offers a promising method for achieving both efficient material removal and enhanced dimensional accuracy in GFRP components. Full article
(This article belongs to the Special Issue Research on Metal Cutting, Casting, Forming, and Heat Treatment)
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18 pages, 9015 KiB  
Article
An End-to-End General Language Model (GLM)-4-Based Milling Cutter Fault Diagnosis Framework for Intelligent Manufacturing
by Jigang He, Xuan Liu, Yuncong Lei, Ao Cao and Jie Xiong
Sensors 2025, 25(7), 2295; https://doi.org/10.3390/s25072295 - 4 Apr 2025
Cited by 2 | Viewed by 796
Abstract
CNC machine and cutting tools are an indispensable part of the cutting process. Their life default diagnosis is related to the efficiency of the entire production process, which ultimately impacts economic performance. Many methods provided by deep learning articles have been verified for [...] Read more.
CNC machine and cutting tools are an indispensable part of the cutting process. Their life default diagnosis is related to the efficiency of the entire production process, which ultimately impacts economic performance. Many methods provided by deep learning articles have been verified for use on large cutting datasets and can help in diagnosing tools’ lifetime well; however, on small samples, the challenge of learning difficulties still emerges. The rise in large language models (LLMs) has brought changes to tool life diagnosis. This study proposes a fault diagnosis algorithm based on GLM-4, and the experimental validation on the PHM 2010 dataset and a proprietary milling cutter dataset demonstrates the superiority of the proposed model, achieving diagnostic accuracies of 93.8% and 93.3%, respectively, outperforming traditional models (SVM, CNN, RNN) and baseline LLMs (ChatGLM2-6B variants). Further robustness and noise-resistance analyses confirm its stability under varying SNR levels (10 dB to −10 dB) and limited training samples. This work highlights the potential of integrating domain-specific feature engineering with LLMs to advance intelligent manufacturing diagnostics. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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19 pages, 19174 KiB  
Article
A Model for Predicting 3D Wear Morphology of Ball-End Milling Tools: Development and Validation
by Rongyi Li, Hengyang He, Caixu Yue, Xianli Liu, Youqiang Xu, Ying Wang and Xiaohua Liu
Coatings 2025, 15(4), 394; https://doi.org/10.3390/coatings15040394 - 27 Mar 2025
Viewed by 525
Abstract
Tool wear prediction is an important research area in the machining industry, which can maximize the utilization of tools. Titanium aluminum alloy is the most commonly used material in the aerospace field, and it is difficult to process. Therefore, the tool wear in [...] Read more.
Tool wear prediction is an important research area in the machining industry, which can maximize the utilization of tools. Titanium aluminum alloy is the most commonly used material in the aerospace field, and it is difficult to process. Therefore, the tool wear in the machining process is serious and non-linear. This results in unpredictable tool wear. In this paper, a three-dimensional (3D) shape prediction method for milling wear of a ball-end milling cutter is proposed. By accurately predicting the tool wear volume, a customized tool dulling standard based on the tool damage percentage is established. Based on the tool material wear rate model and discrete analysis, the force, cutting temperature, relative contact time, and sliding speed of each element in the cutting process of the ball-end mill are solved. Combining the analysis results with the wear rate model, the original model of tool 3D wear morphology (3DWM) prediction was established. Finally, the experiment of cutting titanium aluminum alloy with a carbide tool is carried out to verify the proposed method. The results show that the approximate degree of the wear shape predicted by the model is up to 83.2%. Full article
(This article belongs to the Special Issue Cutting Performance of Coated Tools)
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24 pages, 2690 KiB  
Article
CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks
by Mühenad Bilal, Ranadheer Podishetti, Tangirala Sri Girish, Daniel Grossmann and Markus Bregulla
Sensors 2025, 25(5), 1575; https://doi.org/10.3390/s25051575 - 4 Mar 2025
Viewed by 989
Abstract
Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of [...] Read more.
Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features. Full article
(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)
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14 pages, 2599 KiB  
Article
Rotary Paraplow: A New Tool for Soil Tillage for Sugarcane
by Cezario B. Galvão, Angel P. Garcia, Ingrid N. de Oliveira, Elizeu S. de Lima, Lenon H. Lovera, Artur V. A. Santos, Zigomar M. de Souza and Daniel Albiero
AgriEngineering 2025, 7(3), 61; https://doi.org/10.3390/agriengineering7030061 - 28 Feb 2025
Viewed by 818
Abstract
The sugarcane cultivation has used heavy machinery on a large scale, which causes soil compaction. The minimum tillage has been used to reduce the traffic of machines on the crop, but there is a lack of appropriate tools for the implementation of this [...] Read more.
The sugarcane cultivation has used heavy machinery on a large scale, which causes soil compaction. The minimum tillage has been used to reduce the traffic of machines on the crop, but there is a lack of appropriate tools for the implementation of this technique, especially in sugarcane areas. The University of Campinas—UNICAMP developed a conservation soil tillage tool called “Rotary paraplow”, the idea was to join the concepts of a vertical milling cutter with the paraplow, which is a tool for subsoiling without inversion of soil. The rotary paraplow is a conservationist tillage because it mobilizes only the planting line with little disturbance of the soil surface and does the tillage with the straw in the area. These conditions make this study pioneering in nature, by proposing an equipment developed to address these issues as an innovation in the agricultural machinery market. We sought to evaluate soil tillage using rotary paraplow and compare it with conventional tillage, regarding soil physical properties and yield. The experiment was conducted in an Oxisol in the city of Jaguariuna, Brazil. The comparison was made between the soil physical properties: soil bulk density, porosity, macroporosity, microporosity and penetration resistance. At the end, a biometric evaluation of the crop was carried out in both areas. The soil properties showed few statistically significant variations, and the production showed no statistical difference. The rotary paraplow proved to be an applicable tool in the cultivation of sugarcane and has the advantage of being an invention adapted to Brazilian soils, bringing a new form of minimal tillage to areas of sugarcane with less tilling on the soil surface, in addition to reducing machine traffic. Full article
(This article belongs to the Collection Research Progress of Agricultural Machinery Testing)
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19 pages, 6578 KiB  
Article
Deep Learning Tool Wear State Identification Method Based on Cutting Force Signal
by Shuhang Li, Meiqiu Li and Yingning Gao
Sensors 2025, 25(3), 662; https://doi.org/10.3390/s25030662 - 23 Jan 2025
Cited by 1 | Viewed by 990
Abstract
The objective of this study is to accurately, expeditiously, and efficiently identify the wear state of milling cutters. To this end, a state identification method is proposed that combines continuous wavelet transform and an improved MobileViT lightweight network. The methodology involves the transformation [...] Read more.
The objective of this study is to accurately, expeditiously, and efficiently identify the wear state of milling cutters. To this end, a state identification method is proposed that combines continuous wavelet transform and an improved MobileViT lightweight network. The methodology involves the transformation of the cutting force signal during the milling cutter cutting process into a time–frequency image by continuous wavelet transform. This is followed by the introduction of a Contextual Transformer module after layer 1 and the embedding of a Global Attention Mechanism module after layer 2 of the MobileViT network structure. These modifications are intended to enhance visual representation capability, reduce information loss, and improve the interaction between global features. The result is an improvement in the overall performance of the model. The improved MobileViT network model was shown to enhance accuracy, precision, recall, and F1 score by 1.58%, 1.23%, 1.92%, and 1.57%, respectively, in comparison with the original MobileViT. The experimental results demonstrate that the proposed model in this study exhibits a substantial advantage in terms of memory occupation and prediction accuracy in comparison to models such as VGG16, ResNet18, and Pool Former. This study proposes an efficient identification method for milling cutter wear state identification, which can identify the tool wear state in near real-time. The proposed method has potential applications in the field of industrial production. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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19 pages, 6178 KiB  
Article
Considering the Bottom Edge Cutting Effect of the Carbon Fiber Reinforced Polymer Milling Force Prediction Model and Optimization of Machining Parameters
by Yiwei Zhang, Mengke Yan, Yushu Lai, Guixing Wang and Yifan Yang
Materials 2024, 17(23), 5844; https://doi.org/10.3390/ma17235844 - 28 Nov 2024
Cited by 2 | Viewed by 863
Abstract
The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of [...] Read more.
The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of the bottom edge. In this paper, the prediction of milling force in CFRP milling processes is taken as the research object. By analyzing the milling mechanism and considering the end milling cutter’s bottom cutting edge, the prediction model of milling force was established. Based on the experimental data and simulation data of milling force, the milling force coefficient was obtained by inverse calculation. Subsequently, the predicted cutting force was compared with the experimental cutting force, showing a maximum error of 14.5%, which is within a reasonable range, and the correctness of the model was verified. Furthermore, combined with the delamination damage and the milling force prediction model, a multi-objective optimization model of milling parameters was established, and the genetic algorithm was used to solve the model. The unidirectional carbon fiber plate with a fiber direction angle of 45° was selected as the optimization example. The minimum delamination damage was obtained under the cutting conditions of a spindle speed of 4903.1569 r/min, feed rate per tooth of 0.01 mm/z, and an axial depth of cut of 0.5 mm, and the experimental verification was carried out. The feasibility of the genetic algorithm in CFRP milling parameter optimization modeling was also verified. Full article
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16 pages, 10741 KiB  
Article
Wear of End Mills with Carbon Coatings When Aluminum Alloy A97075 High-Speed Processing
by Evgeny E. Ashkinazi, Sergey V. Fedorov, Artem K. Martyanov, Dmitry N. Sovyk, Victor G. Ralchenko, Artem P. Litvinov, Artem A. Ershov and Vitaly I. Konov
Metals 2024, 14(12), 1344; https://doi.org/10.3390/met14121344 - 26 Nov 2024
Viewed by 738
Abstract
It is recommended to use high-speed milling to maintain an effective material removal rate and the required cutting-edge geometry. However, on the other hand, high speed increases wear, so the surface of the cutters is modified by deposition functional coatings. The wear of [...] Read more.
It is recommended to use high-speed milling to maintain an effective material removal rate and the required cutting-edge geometry. However, on the other hand, high speed increases wear, so the surface of the cutters is modified by deposition functional coatings. The wear of end mills made of CTS12D and H10F tungsten carbides during the high-speed processing of aluminum A97075 (B95T1) was compared. To increase the durability of the tools, well-proven technologies for deposition diamond-like and polycrystalline diamond coatings in microwave plasma with different film structures, which were determined by the coating growth conditions, were used. The milling cutter corner was mostly worn out, but the nature of the wear had its characteristics. It was revealed that at a forced cutting mode of about 1000 m/min, cutters made of CTS12D alloy with a nanocrystalline diamond coating with a “cauliflower” structure and with a diamond-like film showed 10% higher resistance. The primary wear mechanism was adhesive. Images of worn cutting edges were obtained using a 3D optical digital image processing system. Full article
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20 pages, 13368 KiB  
Article
Effect of Surface-Textured AlSiTiN Coating Parameters on the Performance of Ball-End Milling Cutter in Titanium Alloy Milling
by Shucai Yang, Dongqi Yu and Dawei Wang
Coatings 2024, 14(11), 1458; https://doi.org/10.3390/coatings14111458 - 15 Nov 2024
Cited by 2 | Viewed by 1082
Abstract
In the high-speed milling of titanium alloys, the combined application of surface texture and coatings can significantly enhance the performance of cemented carbide tools. Investigating the synergistic effect of surface texture and AlSiTiN coating on tool performance is crucial for advancing the development [...] Read more.
In the high-speed milling of titanium alloys, the combined application of surface texture and coatings can significantly enhance the performance of cemented carbide tools. Investigating the synergistic effect of surface texture and AlSiTiN coating on tool performance is crucial for advancing the development of their integrated preparation process. Therefore, in this study, a cemented carbide ball-end milling cutter is taken as the research object, and a surface-textured AlSiTiN coating is applied to the rake face. The effects of texture and coating parameters on the milling performance of titanium alloys are analyzed, and a regression model is developed to optimize the relevant parameters. The results indicate that the surface texture effectively reduces the actual contact area between the tool and the chip, serves as a storage space for chips, and enhances the wear resistance of the AlSiTiN coating. The coating thickness significantly affects milling force, milling temperature, and surface wear. An increase in coating thickness improves the hardness and integrity of the coating surface, and it also strengthens the adhesion of the texture to the coating. Additionally, precise control of the laser power plays a key role in reducing the milling temperature, while both the number of scans and the scanning speed significantly influence surface wear. Furthermore, maintaining an appropriate distance from the edge is crucial for enhancing the surface roughness of the workpiece. The optimized parameters for surface texture and coating preparation are as follows: coating thickness (h) = 3.0 µm, laser power (p) = 40 W, scanning speed (v) = 1590 µm/min, number of scans (n) = 6, texture diameter (d) = 42 µm, texture spacing (l) = 143 µm, and distance from the edge (l1) = 104 µm. The optimized milling performance of the milling cutter shows a significant improvement. Full article
(This article belongs to the Special Issue Cutting Performance of Coated Tools)
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18 pages, 8365 KiB  
Article
Prediction of Delamination Defects in Drilling of Carbon Fiber Reinforced Polymers Using a Regression-Based Approach
by Mohammad Ghasemian Fard, Hamid Baseri, Aref Azami and Abbas Zolfaghari
Machines 2024, 12(11), 783; https://doi.org/10.3390/machines12110783 - 6 Nov 2024
Viewed by 1453
Abstract
Carbon fiber-reinforced polymer (CFRP) structures have been increasingly used in various aerospace sectors due to their outstanding mechanical properties in recent years. However, the poor machinability of CFRP plates, combined with the inhomogeneous behavior of fibers, poses a challenge for manufacturers and researchers [...] Read more.
Carbon fiber-reinforced polymer (CFRP) structures have been increasingly used in various aerospace sectors due to their outstanding mechanical properties in recent years. However, the poor machinability of CFRP plates, combined with the inhomogeneous behavior of fibers, poses a challenge for manufacturers and researchers to define the critical factors and conditions necessary to ensure the quality of holes in CFRP structures. This study aims to analyze the effect of drilling parameters on CFRP delamination and to predict hole quality using a regression-based approach. The design of the experiment (DOE) was conducted using Taguchi’s L9 3-level orthogonal array. The input drilling variables included the feed rate, spindle speed, and three different drill types. A regression-based model using partial least squares (PLS) was developed to predict delamination defects during the drilling of CFRP plates. The PLS model demonstrated high accuracy in predicting delamination defects, with a Mean Squared Error (MSE) of 0.0045, corresponding to an accuracy of approximately 99.6%, enabling the rapid estimation of delamination. The model’s predictions were closely aligned with the experimental results, although some deviations were observed due to tool inefficiencies, particularly with end mill cutters. These findings offer valuable insights for researchers and practitioners, enhancing the understanding of delamination in CFRPs and identifying areas for further investigation. Full article
(This article belongs to the Special Issue Recent Advances in Surface Integrity with Machining and Milling)
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10 pages, 3902 KiB  
Article
End-of-Life Prediction for Milling Cutters Based on an Online Vibro-Acoustic System
by Michele Perrelli, Romina Conte, Gabriele Zangara and Francesco Gagliardi
Machines 2024, 12(10), 703; https://doi.org/10.3390/machines12100703 - 3 Oct 2024
Viewed by 910
Abstract
Improving the capabilities of online condition monitoring systems, able to detect arising of catastrophic wear on cutting tools, has been an important target to be pursued for the metal cutting industry. Currently, different systems have been proposed, moved by the rising need of [...] Read more.
Improving the capabilities of online condition monitoring systems, able to detect arising of catastrophic wear on cutting tools, has been an important target to be pursued for the metal cutting industry. Currently, different systems have been proposed, moved by the rising need of part quality improvements and production cost control. Despite this, cutter wear development, being related to several process variables and conditions, is still really difficult to be predicted accurately. This paper presents a detection wear method based on the time-domain analysis of vibro-acoustic signals. Specifically, cutter wear monitoring, using sound signals of a milling process, was performed at a laboratory level in a well-isolated working room. Sound signals were recorded at fixed main machining parameters, i.e., cutting speed, feed rate and depth of cut. The tests were carried out starting with a new set of inserts with significant wear conditions for the investigated process configuration. Results showed a consistent overlapping between the beginning of the catastrophic wear and an evident increment in the trend of the root mean square of the monitored acoustic signal, showing the potential of the methodology in detecting a suitable time to stop the milling process and to change the worn-out cutters. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 28154 KiB  
Article
Study on the Cutting Performance and Remaining Life Prediction of Micro-Texture Ball End Milling Cutters for Titanium Alloys
by Yuhua Zhang, Yongqi Fu, Quanxi Li, Keyi Zhang and Kuo Liu
Coatings 2024, 14(7), 814; https://doi.org/10.3390/coatings14070814 - 29 Jun 2024
Cited by 3 | Viewed by 1228
Abstract
As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. [...] Read more.
As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. This heat leads to chip adhesion and exacerbates the wear of the ball end milling cutter, ultimately affecting its service life. Therefore, studying the residual life of the tool during the cutting process is essential to prevent significant impacts on the product’s surface quality due to tool damage and passivation. Most research on micro-texture cutters is based on experiments that analyze the wear patterns of cutters under various lubrication conditions and their influence on the cutting process. Different neural network prediction models are employed to enhance the accuracy and stability of tool life prediction models. However, the exploration of other superior models for predicting the life of micro-texture cutters remains ongoing. This paper is based on an experiment involving the milling of titanium alloy using a micro-pit-structured ball end milling cutter. It was found that the cutting force of the tool is higher during the initial and later wear stages. During the stable wear stage, the unevenness of the defective layer on the tool surface is reduced, increasing the contact area and reducing the surface pressure, thereby decreasing the cutting force. This study analyzes the influence of micro-pit structural parameters on the wear and milling force of the ball end milling cutter. By evaluating the wear value of the ball end milling cutter after each cut, the wear mechanism of the micro-texture cutter is identified. A deep-learning-based bidirectional long short-term memory (BiLSTM) neural network model for tool life prediction is developed. Through training and validation, the model’s accuracy and stability are continuously improved. A comparative analysis with different predictive models is conducted to determine whether the proposed model offers advantages over existing models, which is crucial for maximizing tool utilization and reducing manufacturing costs. Full article
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20 pages, 15368 KiB  
Article
Research on the Milling Performance of Micro-Groove Ball End Mills for Titanium Alloys
by Shihong Zhang, Hu Shi, Baizhong Wang, Chunlu Ma and Qinghua Li
Lubricants 2024, 12(6), 204; https://doi.org/10.3390/lubricants12060204 - 4 Jun 2024
Cited by 3 | Viewed by 1223
Abstract
Titanium alloys are widely used in various fields, but milling titanium alloy materials often leads to problems such as high milling forces, increased milling temperatures, and chip adhesion. Thus, the machinability of titanium alloys faces challenges. To improve the milling performance of titanium [...] Read more.
Titanium alloys are widely used in various fields, but milling titanium alloy materials often leads to problems such as high milling forces, increased milling temperatures, and chip adhesion. Thus, the machinability of titanium alloys faces challenges. To improve the milling performance of titanium alloy materials, this study analyzes the effective working area on the surface of the milling cutter through mathematical calculations. We design micro-grooves in this area to utilize their friction-reducing and wear-resisting properties to alleviate the aforementioned issues. The effective working area of the ball end milling cutter’s cutting edge is calculated based on the amount of milling and the installation position between the milling cutter and the workpiece. By observing the surface structure of seashells, micro-grooves are proposed and designed to be applied in the working area of the milling cutter surface. The impact of the micro-groove area on the milling cutter surface and spindle speed on milling performance is discussed based on milling simulation and experimental tests. Experimental results show that the cutting force, milling temperature, and chip resistance to adhesion produced by micro-groove milling cutters are superior to conventional milling cutters. Milling cutters with three micro-grooves perform best at different spindle speeds. This is because the presence of micro-grooves on the surface of the milling cutter improves the friction state, promoting a reduction in milling force, while the micro-grooves also serve as storage containers for chips, alleviating the phenomenon of chip softening and adhesion to the cutter. When conducting cutting tests with a milling cutter that has three micro-grooves, the milling force is reduced by 10% to 30%, the milling temperature drops by 10% to 20%, and the surface roughness decreases by 8% to 12%. Full article
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