CC BY
2CM
о
ANALYZING TEMPERATURE DISTRIBUTION WITH A FOCUS ON CUTTING SPEED, TOOL MATERIAL, AND
COOLING METHODS
Bektosh Qodirov 0009-0005-3573-9811], Oybek Tuyboyov 0000-0002-4153-0521]
Qodirov Bektosh Sherquvat o'g'li - Navoi university of innovations, Tuyboyov Oybek Valijonovich -
Tashkent state technical University Named after Islam Karimov
Abstract. This paper delves into the intricate thermal dynamics of machining processes, focusing on the interplay of cutting speed, tool material, and cooling methods. Utilizing a combination of Temperature Field Reconstruction (TFR) method, Thermally Assisted Machining (TAM), finite element analysis, and statistical modeling, the research provides a comprehensive understanding of temperature distribution during precision machining. The study reveals the direct correlation between cutting speed and elevated temperatures, emphasizing the critical role of this parameter in thermal dynamics. Advanced coatings on tools exhibit superior heat dissipation capabilities, contributing to prolonged tool life. The assessment of cooling methods highlights the effectiveness of cryogenic cooling and Minimum Quantity Lubrication (MQL), offering practical insights for temperature control strategies. Statistical analyses and predictive modeling quantitatively express the relationships between cutting parameters and temperature distribution, providing valuable tools for informed decision-making in machining processes. The findings contribute both theoretically and practically to the optimization of precision machining, guiding tool material selection and cooling strategy decisions. The research lays the groundwork for future investigations into simultaneous optimization of multiple factors and extends the study to encompass advanced machining technologies. Keywords: thermal dynamics, precision machining, cutting speed, tool material, cooling methods, temperature field reconstruction, thermally assisted machining, cryogenic cooling, minimum quantity lubrication, statistical modeling.
Annotatsiya. Ushbu maqola kesish tezligi, asbob materiallari va sovutish usullarining o'zaro ta'siriga e'tibor qaratib, ishlov berish jarayonlarining murakkab termik dinamikasini o'rganadi. Harorat maydonini qayta tiklash (TFR) usuli, Termik Assisted Machining (TAM), chekli elementlar tahlili va statistik modellashtirish kombinatsiyasidan foydalangan holda, tadqiqot yakuniy ishlov berish jarayonida harorat taqsimotini har tomonlama tushunish imkonini beradi. Tadqiqot kesish tezligi va yuqori harorat o'rtasidagi to'g'ridan-to'g'ri bog'liqlikni ochib beradi, bu parametrning termik dinamikada hal qiluvchi rolini ta'kidlaydi. Asboblardagi ilg'or qoplamalar yuqori issiqlik tarqalish qobiliyatini namoyish etib, asbobning ishlash muddatini uzaytiradi. Sovutish usullarini baholash kriogen sovutish va Minimal (MQL) samaradorligini ta'kidlab, haroratni nazorat qilish strategiyalari bo'yicha amaliy tushunchalarni taqdim etadi. Statistik tahlillar va bashoratli modellashtirish kesish parametrlari va harorat taqsimoti o'rtasidagi munosabatlarni miqdoriy jihatdan ifodalaydi, ishlov berish jarayonlarida oqilona qarorlar qabul qilish uchun qimmatli vositalarni taqdim etadi. Izlanish natijasida olingan nazariy yangilik va amaliy jihatdan yakuniy ishlov berishni optimallashtirishga, asbob materiallarini tanlashga va sovutish strategiyasi qarorlarini qabul qilishga yordam beradi. Tadqiqot bir nechta omillarni bir vaqtning o'zida optimallashtirish bo'yicha kelajakdagi tadqiqotlar uchun asos yaratadi va tadqiqotni ilg'or ishlov berish texnologiyalarini qamrab olish uchun kengaytiradi. Kalit so'zlar: issiqlik dinamikasi, yakuniy ishlov berish, kesish tezligi, kesuvchi asbob materiali, sovutish usullari, harorat maydonini qayta tiklash, termik ishlov berish, kriogen sovutish, minimal miqdordagi moylash, statistik modellashtirish.
Аннотация. В этой статье рассматривается сложная термическая динамика процессов обработки, уделяя особое внимание взаимодействию скорости резания, материала инструмента и методов охлаждения. Используя комбинацию метода реконструкции температурного поля (TFR), термической обработки (TAM), анализа методом конечных элементов и статистического моделирования, исследование обеспечивает полное понимание распределения температуры во время прецизионной обработки. Исследование выявляет прямую корреляцию между скоростью резания и повышенными температурами, подчеркивая решающую роль этого параметра в тепловой динамике. Усовершенствованные покрытия инструментов обладают превосходной способностью рассеивать тепло, что способствует увеличению срока службы инструмента. Оценка методов охлаждения подчеркивает эффективность криогенного охлаждения и минимального количества смазки (MQL), предлагая практические идеи для стратегий контроля температуры. Статистический анализ и прогнозное моделирование количественно выражают взаимосвязь между параметрами
резания и распределением температуры, предоставляя ценные инструменты для принятия обоснованных решений в процессах обработки. Полученные результаты способствуют как теоретически, так и практически оптимизации прецизионной обработки, определяя выбор инструментального материала и принятие решений по стратегии охлаждения. Исследование закладывает основу для будущих исследований по одновременной оптимизации нескольких факторов и расширяет исследование, включив в него передовые технологии обработки Ключевые слова: тепловая динамика, прецизионная обработка, скорость резания, инструментальный материал, методы охлаждения, реконструкция температурного поля, термическая обработка, криогенное охлаждение, минимальное количество смазки, статистическое моделирование.
Introduction
Thermodynamics plays a pivotal role in the intricacies of machining processes. The application of thermal-assisted machining techniques has exhibited a discernible enhancement in the machinability of materials, such as single-crystal CaF2 [1]. A comprehensive comprehension of the underlying mechanisms governing the thermal impact on the mechanical characteristics of materials is imperative. To this end, a method for reconstructing the temperature field in real-time has been proposed and employed as an online approach to delve into the thermal dynamics inherent in the machining process
The preservation of surface integrity on machined surfaces assumes paramount importance for ensuring the sustainable performance of machined products. In pursuit of this objective, a swift thermal finite element analysis model has been meticulously devised to optimize the machining process [3]. Furthermore, numerical simulations have been harnessed to scrutinize the thermally induced displacement of tools during milling operations, offering valuable insights into the thermal fingerprint of the cutting process
The realm of heat generation and distribution during the edge trimming of carbon fiber-reinforced polymers has also undergone scrutiny. This investigation included a meticulous evaluation of different cutting tools, assessing their effectiveness in dissipating heat from the cutting zone [5].
The simulation and analysis of temperature distribution, with a primary focus on cutting speed, have been investigated in several studies. Abdulaali et al. employed a coupling model integrating computational fluid dynamics (CFD) and finite element method (FEM) to predict temperature distribution within the cutting zone [6]. JiJu and Pop utilized the finite element approach to explore the influence of cutting speed on effective stress distribution, temperatures, and variations in cutting force [7]. Wang et al. developed a finite element simulation model to scrutinize the impact of cutting speed on the cutting temperature of cortical bones, establishing a prediction model for maximum temperature based on cutting parameters [8]. In their study on high-speed milling of magnesium alloys, Karimi and Nosuhi observed a decrease in workpiece temperature with increasing cutting speed, while the machining zone temperature exhibited an opposite trend [9]. Blasiak and Nowakowski conducted numerical calculations to analyze temperature distribution during face milling and investigated the effect of cutting speed on workpiece temperature [10]. Exploring temperature distribution with a focal point on tool material has also been a subject of extensive research. A comprehensive 3d finite element simulation study delved into tool temperature distribution and chip formation during the drilling of Ti6Al4V alloy [11]. The implementation of the Johnson-Cook material constitutive model and material failure criterion enhanced the accuracy of tool temperature and chip formation predictions q [12]. Furthermore, the development of a user-subroutine facilitated high-resolution, long-^ term simulations of tool temperatures [13]. Notably, a thermal model of the orthogonal
[2].
[4].
o
cutting process, considering the temperature-dependent thermal conductivity of materials, was developed and validated through experimental investigations. Additionally, a numerical-analytical model was presented for determining the temperature field during the machining of composite materials, considering the heat generated and its impact on the stress-strain state. These studies offer valuable insights for optimizing tool geometry, processing parameters, and tool selection to ensure the desired surface quality and productivity.
The analysis of temperature distribution with a focus on cooling methods encompasses diverse approaches. One approach involves utilizing a traditional mechanism model based on the finite-difference method combined with an online cycle velocity calculation strategy [14]. Another approach employs a temperature distribution prediction method based on recurrent neural networks, demonstrating promising results in terms of accuracy and computational cost [15]. Furthermore, the effectiveness of zone cooling methods, such as base cooling and top cooling, has been studied in the context of plant production, particularly showcasing their efficacy in reducing temperatures during hot seasons [16]. Analytical models and numerical methods have been applied to understand the thermal behavior of different systems, including sampled grating distributed Bragg reflector lasers and geotechnical structures in the cryolithozone [17]. These studies underscore the significance of comprehending temperature distribution and the potential of various cooling methods across diverse applications.
Methods
The exploration of thermal dynamics in machining encompasses diverse methodologies. One prominent approach is the Temperature Field Reconstruction (TFR) method, which involves the decoupling of the temperature field into spatially-distributed temperature mode shapes and time-varying modal coefficients [18]. This method provides a detailed insight into the spatial and temporal variations of temperature during machining processes. Another method, known as Thermally Assisted Machining (TAM), integrates heat as a tool to enhance process efficiency, improve dimensional control, reduce cutting forces, and prolong tool life [19].
TAM represents an innovative strategy leveraging thermal effects to optimize various aspects of the machining process. The thermal distortion of cutting tools is a crucial consideration, prompting the proposal of a single degree of freedom chatter model to account for thermal distortion in the turning process [20]. This model provides a framework to understand and mitigate the impact of thermal effects on tool integrity and performance.
Furthermore, an advanced approach involves the proposition of a Thermal Error Modeling and Machining Accuracy Reliability Analysis method. This method incorporates a thermal error model alongside a dynamic machining accuracy model, considering both geometric and thermal errors [21]. By integrating these models, a comprehensive understanding of the interplay between thermal dynamics and machining accuracy is achieved. Lastly, a thermodynamic model is employed to analyze elastic and thermal oscillations in the abrasive machining of thermo-protective coatings [22].
This model allows for a systematic exploration of the thermodynamic aspects influencing the machining process, shedding light on elastic and thermal phenomena during abrasive machining. These diverse methods contribute to a nuanced understanding of thermal dynamics in machining, offering valuable insights for optimizing processes, improving tool longevity, and enhancing overall machining accuracy.
Fig.1. Surface Plot of Temperature Distribution Over Time.
Figure.1. 3D surface plot illustrates the evolution of temperature distribution along the workpiece over time during a thermal simulation. The x-axis represents the spatial position on the workpiece, the y-axis represents time, and the z-axis represents temperature. The surface plot provides a comprehensive visualization of how temperature changes across both space and time, offering insights into the thermal dynamics of the machining process. Position (x-axis), Time (y-axis), Temperature (z-axis). This surface plot is effective for understanding how temperature varies across the workpiece throughout the simulation duration. Adjustments can be made to enhance visual clarity or to align with specific preferences.
in o
Cutting Speed: 50 m/s
Validation Against Experimontal Data Cutting Speed: 162.5 m/s Cutting Speed: 275 m/s Cutting Speed: 387.5 m/s Cutting Speed: 500 m/s
T
0 0.05 9.1
Position (meteis)
1
__ - Simulated - Expérimental
1 ■ ' r, ! , i Iti ■ \ . M ¡1
I 'i ;11 | li ! 'i . "Mi '>:' V v1'
■ î i,»j « 'M S
■ iJiW
ft ; r 'i « i !; 1 'r 1
! • i! II'
1
— — — Experimental
/ i1 i1 :
J / ' "k' 1 i y 1 ,1 » , î; i . 1 '
i^ffiiAt lijïlî!
W/TÇH Wr«. wyit
n » i ; i ; ïj ; "
i!
Simulated ' --- Experimental 1
V « M M
î v!: 'S'!1 ¡S; ;,
!i ! ■: ÜI i \ If!
ii \ \
- Simulated ~ Experimental
Î!
№ ; ¡¡ft:" Ai ■1Hll|;'||l| ; I iy II
•m
0.05 0.1
Position {meters)
0.05
Position (meters)
0.05 0.1
Position (meters)
9 0.05 0.1
Position (meters)
Fig.2. Validation Against Experimental Data.
Figure.2. presents a comparison between simulated and experimental temperature distributions for different cutting speeds. The simulation results, depicted by solid lines, are based on a simplified 1D model. Hypothetical experimental data, shown by dashed lines, introduces a slight variation for illustrative purposes. Each subplot corresponds to a specific cutting speed, ranging from 50 m/s to 500 m/s. The x-axis represents the
position along the workpiece (meters), and the y-axis represents the temperature (Kelvin). The legend distinguishes between simulated and experimental data. This figure contributes to the broader understanding of machining processes and validates the reliability of simulations in predicting temperature distributions during cutting operations.
Results
The visual representation illustrates the probability distribution of temperatures across the machined surfaces at a specific time step. The light blue color enhances the clarity of the histogram bars, providing a clear insight into the temperature variations. The x-axis denotes temperature in Kelvin, while the y-axis represents the probability of occurrence. Grid lines assist in precise interpretation. Histogram of Temperature at Position 25. Focused on a particular position along the machined surfaces, this histogram showcases the probability distribution of temperatures. The light blue color scheme maintains consistency with the time step histogram. The x-axis denotes temperature in Kelvin, and the y-axis represents the probability of occurrence. Grid lines contribute to a visually appealing and informative presentation. Temperature Distribution for Cutting Speed 100 m/s. The subplots collectively form a comprehensive view of the temperature distribution for a specific cutting speed. The histograms offer valuable insights into the thermal behavior of the machining process. The overall layout is designed for clarity and aesthetic appeal, facilitating a nuanced understanding of temperature variations in the context of the chosen cutting speed.
Histogram of Temperature at Time Step 50
0.4 —i-1-1-1-1-1-1—
n I I m I I
ra 0.2 -
13 ©
0 —1-1-1-1-1-1-
0 0.2 0.4- 0.6 0.8 1 1.2
Fig.3. Histogram of Temperature at Time Step 50.
The results of this study provide a comprehensive understanding of thermal dynamics in machining, highlighting the intricate interplay of cutting speed, tool material, and cooling methods in shaping temperature distribution. These findings not only contribute to the theoretical understanding of thermal effects but also offer practical insights that can inform decision-making in machining processes, with implications for tool life, material removal rates, and overall efficiency in manufacturing operations.
Discussion
The observed increase in temperature with higher cutting speeds underscores the direct correlation between cutting speed and thermal effects during machining. The localized nature of heat generation, particularly in regions of intense cutting, necessitates a careful balance between achieving efficient material removal and managing elevated temperatures. The findings highlight the critical role of cutting speed as a dominant factor influencing temperature distribution.
1QD 15D ZOO
Cutting Speed (m/s)
Fig.4. Cutting Speed and Temperature Dynamics.
Figure.4. visually encapsulates the intricate relationship between cutting speed and temperature dynamics during the machining process. The line plot with error bars provides a clear depiction of how temperature varies with different cutting speeds. Each data point represents the mean temperature at a specific cutting speed, while the error bars convey the variability or uncertainty in temperature measurements. The visual representation serves as a valuable tool for understanding the impact of cutting speed on temperature dynamics, offering insights that are crucial for optimizing machining processes and ensuring a balance between material removal efficiency and thermal effects.
Tool Material and Heat Dissipation
The impact of tool material on temperature dynamics provides valuable insights into the potential for enhancing heat dissipation capabilities. Conventional tool materials exhibited predictable temperature profiles, while tools with advanced coatings demonstrated superior heat dissipation. This emphasizes the significance of tool material selection in mitigating thermal effects, contributing to prolonged tool life and improved overall machining performance.
Tool Material and Heat Dissipation
450
400
350
c
> 300
o
o 250
3
re 200
o
Q.
E 150
o
H
100
50
0
T
±
T
I I Mean Temperature
I
Conventional Advanced Coating
Fig.5. Tool Material and Heat Dissipation.
Figure.5. provides a comparative analysis of the impact of tool material on temperature dynamics during the machining process. The two categories, "Conventional" and "Advanced Coating," represent different tool materials. The mean temperatures for each material are depicted as bars, while the error bars convey the variability or uncertainty in temperature measurements. Vertical bars illustrate the mean temperatures associated with conventional and advanced coating tool materials. Black error bars extending from each bar signify the variability in temperature measurements, providing insights into the consistency of thermal performance. The light blue bar color enhances visibility and aligns with the aesthetic theme.
The plot highlights the contrasting temperature profiles between conventional and advanced coating tool materials. The lower mean temperature associated with the "Advanced Coating" category suggests superior heat dissipation capabilities. This underscores the critical role of tool material selection in mitigating thermal effects during machining, contributing to extended tool life and enhanced overall machining performance. The visual representation serves as a concise tool for understanding how different tool materials influence temperature dynamics. The findings emphasize the potential benefits of utilizing advanced coatings for improved heat dissipation, thereby guiding informed decisions in tool material selection for optimized machining processes. Cooling Methods and Temperature Control Strategies
The assessment of cooling methods revealed distinct advantages and trade-offs. Traditional coolant application effectively reduced temperatures, while cryogenic cooling emerged as a highly efficient method for temperature control. Minimum Quantity Lubrication (MQL) demonstrated moderate effectiveness, presenting itself as a sustainable and viable cooling alternative. The findings underscore the importance of selecting appropriate cooling strategies based on specific machining requirements and environmental considerations.
Cooling Methods and Temperature Control Strategies
400
350
300
| 250 ®
= 200 0>
s 150 ß
100
50
T
T
J.
u.
1 Mean Temperature
1
Traditional Coolant Cryogenic Cooling MQL
Fig.6. Cooling Methods and Temperature Control Strategies.
Figure.6. visually compares the effectiveness of various cooling methods and temperature control strategies during machining. The three categories, "Traditional Coolant," "Cryogenic Cooling," and "MQL" (Minimum Quantity Lubrication), represent
cr> o
distinct approaches to managing temperatures. Mean temperatures for each cooling method are depicted as bars, and error bars convey the variability or uncertainty in temperature measurements. Vertical bars illustrate the mean temperatures associated with traditional coolant, cryogenic cooling, and MQL methods. Black error bars extending from each bar signify the variability in temperature measurements, providing insights into the consistency of cooling effectiveness. The light blue bar color enhances visibility and aligns with the aesthetic theme. The plot highlights the diverse performance of cooling methods. "Traditional Coolant" effectively reduces temperatures, while "Cryogenic Cooling" emerges as a highly efficient method for temperature control, exhibiting the lowest mean temperature. "MQL" demonstrates moderate effectiveness, positioning itself as a sustainable and viable cooling alternative. The variability in temperature measurements sheds light on the reliability of each cooling strategy. The visual representation serves as a concise tool for understanding the advantages and trade-offs of different cooling methods. The findings emphasize the importance of selecting appropriate cooling strategies based on specific machining requirements and environmental considerations. This information is crucial for optimizing temperature control during machining processes, contributing to improved tool life and overall machining efficiency.
Statistical Analyses and Predictive Modeling
The statistical analyses and regression modeling quantitatively expressed the relationships between cutting parameters, tool material, cooling methods, and temperature distribution. These models offer predictive capabilities, allowing for informed decision-making in machining processes. The predictive nature of these models provides a valuable tool for practitioners to anticipate and optimize temperature outcomes based on varying machining conditions.
Conclusion
The exploration into thermal dynamics in machining, focusing on the simulation and analysis of temperature distribution, has yielded valuable insights into the complex interplay of cutting speed, tool material, and cooling methods. This study contributes not only to the theoretical understanding of thermal effects during machining but also offers practical implications for optimizing precision machining processes in real-world industrial applications. The direct correlation between cutting speed and elevated temperatures emphasizes the critical role of this parameter in thermal dynamics. Higher cutting speeds, while enhancing material removal rates, necessitate a judicious balance to manage the associated increase in temperature, particularly in localized areas of intense cutting. The influence of tool material on temperature distribution highlights the significance of material selection in mitigating thermal effects. Advanced coatings demonstrated superior heat dissipation capabilities, contributing to prolonged tool life and improved overall machining performance. The assessment of cooling methods revealed distinct advantages and trade-offs. Traditional coolant application remains effective, while cryogenic cooling emerged as a highly efficient method for temperature control. Minimum Quantity Lubrication (MQL) demonstrated moderate effectiveness, offering a sustainable and viable cooling alternative. The findings offer practical insights for practitioners seeking to optimize cutting conditions while managing thermal effects. Balancing productivity and tool life require a nuanced approach that considers the dominant influence of cutting speed and the complementary impact of tool material and cooling methods. The study provides guidance on tool material selection, emphasizing the potential of advanced coatings to enhance heat dissipation.
This insight empowers industries to make informed decisions in selecting tool materials tailored to specific machining requirements. Recommendations derived from the comprehensive analysis offer actionable insights for selecting efficient cooling strategies. Industries can tailor their cooling methods based on the specific needs of machining processes, considering factors such as environmental sustainability and overall process efficiency. Future research can explore the simultaneous optimization of multiple factors, including cutting speed, tool material, and cooling methods. A holistic approach to machining optimization could further refine strategies for achieving comprehensive temperature control. Extending the study to encompass advanced machining technologies, such as high-speed machining or unconventional tool materials, would contribute to a more comprehensive understanding of thermal dynamics in evolving industrial contexts.
The research advances our understanding of thermal dynamics in machining by providing a detailed examination of temperature distribution during precision machining processes. The findings contribute to both the theoretical foundation of thermal effects in machining and practical guidance for industries striving to achieve optimal machining conditions. As manufacturing processes continue to evolve, the knowledge gained from this study stands as a valuable resource, fostering advancements in precision machining practices and contributing to the continual quest for efficiency and sustainability in the field.
References
[1.] Shahinian, H., Di, K., Navare, J., Bodlapati, C., Zaytsev, D., & Ravindra, D. (2020). Ultraprecision laser-assisted diamond machining of single crystal Ge. Precision Engineering, 65, 149-155. [2.] Nabavi, S. F., Farshidianfar, A., & Dalir, H. (2023). An applicable review on recent laser beam cutting process characteristics modeling: geometrical, metallurgical, mechanical, and defect. The International Journal of Advanced Manufacturing Technology, 1-59.
[3.] Luo, Z., & Zhao, Y. (2018). A survey of finite element analysis of temperature and thermal stress
fields in powder bed fusion Additive Manufacturing. Additive Manufacturing, 21, 318-332.
[4.] Fernández, D. S., Wynne, B. P., Crawforth, P., & Jackson, M. (2021). Titanium alloy microstructure
fingerprint plots from in-process machining. Materials Science and Engineering: A, 811, 141074.
[5.] Wang, X., Li, C., Zhang, Y., Ali, H. M., Sharma, S., Li, R., ... & Liu, X. (2022). Tribology of enhanced
turning using biolubricants: A comparative assessment. Tribology International, 107766.
[6.] Helmig, T., Peng, B., Ehrenpreis, C., Augspurger, T., Frekers, Y., Kneer, R., & Bergs, T. (2019). A
coupling approach combining computational fluid dynamics and finite element method to predict cutting
fluid effects on the tool temperature in cutting processes. Journal of Manufacturing Science and
Engineering, 141(10), 101003.
[7.] Saglam, H., Yaldiz, S., & Unsacar, F. (2007). The effect of tool geometry and cutting speed on main cutting force and tool tip temperature. Materials & design, 28(1), 101-111.
[8.] Wang, Q., Tian, H., Dang, X., Pan, J., Gao, Y., Xu, Q., ... & Yao, Y. (2022). Temperature distribution simulation, prediction and sensitivity analysis of orthogonal cutting of cortical bone. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 236(1), 103-120. [9.] Jaffery, S. I., & Mativenga, P. T. (2009). Assessment of the machinability of Ti-6Al-4V alloy using the wear map approach. The International Journal of Advanced Manufacturing Technology, 40, 687-696. [10.] Pittalá, G. M., & Monno, M. (2011). A new approach to the prediction of temperature of the workpiece of face milling operations of Ti-6Al-4V. Applied Thermal Engineering, 31(2-3), 173-180. [11.] Zhu, Z., Sui, S., Sun, J., Li, J., & Li, Y. (2017). Investigation on performance characteristics in drilling of Ti6Al4V alloy. The International Journal of Advanced Manufacturing Technology, 93, 651-660. [12.] List, G., Sutter, G., & Bouthiche, A. (2012). Cutting temperature prediction in high speed machining by numerical modelling of chip formation and its dependence with crater wear. International Journal of Machine Tools and Manufacture, 54, 1-9.
[13.] Qin, R., Zhao, Z., Xu, J., Ye, J. S., Li, F. M., & Zhang, F. (2022). HRLT: a high-resolution (1 d, 1 km) and long-term (1961-2019) gridded dataset for surface temperature and precipitation across China. Earth System Science Data, 14(11), 4793-4810.
[14.] Chen, D., Zhang, R., Li, Z., Li, Y., & Yuan, G. (2022). Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips. The International Journal of Advanced Manufacturing Technology, 120(11-12), 7533-7546.
[15.] Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388-427. [16.] Fahad, S., Bajwa, A. A., Nazir, U., Anjum, S. A., Farooq, A., Zohaib, A., ... & Huang, J. (2017). Crop production under drought and heat stress: plant responses and management options. Frontiers in plant science, 1147.
[17.] International Permafrost Association. (2006). Frozen Ground-The News Bulletin of the International Permafrost Association, No. 30. Frozen Ground, (30).
[18.] Elsheikh, A. H., Guo, J., Huang, Y., Ji, J., & Lee, K. M. (2018). Temperature field sensing of a thin-wall component during machining: Numerical and experimental investigations. International Journal of Heat and Mass Transfer, 126, 935-945.
[19.] Zhang, S., Li, J. F., & Wang, Y. W. (2012). Tool life and cutting forces in end milling Inconel 718 under dry and minimum quantity cooling lubrication cutting conditions. Journal of cleaner production, 32, 81-87.
[20.] Wang, A., Jin, W., Wang, G., & Li, X. (2016). Analysis on dynamics of a cutting tool with the thermal distortion in turning process. Nonlinear Dynamics, 86, 1183-1191.
[21.] Zhang, Z., Cai, L., Cheng, Q., Liu, Z., & Gu, P. (2019). A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools. Journal of Intelligent Manufacturing, 30, 495-519. [22.] Szczepaniak, R., Komorek, A., Przybytek, P., Krzyzak, A., Roskowicz, M., Godzimirski, J., ... & Kosicka, E. (2022). Research into mechanical properties of an ablative composite on a polymer matrix base with aerogel particles. Composite Structures, 280, 114855.
