Application of coolants during tool-based machining – A review

Yang, Khor Zheng; Pramanik, Alokesh; Basak, A.K.; Dong, Yu; Prakash, Chander; Shankar, S.; Dixit, Saurav; Kumar, Kaushal; Vatin, Nikolai Ivanovich

doi:10.1016/j.asej.2022.101830

Cited by 50 publications

(20 citation statements)

References 84 publications

(90 reference statements)

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“…Conversely, Zubora 67H, being a semi-synthetic emulsion, cannot maintain its viscosity at higher temperatures. This distinct behaviour underscores the effectiveness of Zubora 67H in facilitating easier chip removal, aligning with discussions from prior investigations [39].…”

Section: Rheological Behaviorsupporting

confidence: 83%

Evaluating the Tribological Behaviour in Cutting Operations Using a Modified Ball-on-Disc Open Tribotester

Nassef,

Pape,

Poll

et al. 2024

Lubricants

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Predicting the tribological behaviour in the secondary shear zone in the metal-cutting processes is considered a significant challenge in contemporary research. This work investigated the frictional performance in the secondary shear zone of a planing process using a modified ball-on-disc open tribometer. The values of the coefficient of friction (COF) were tracked between an AISI4140 + QT disc (chip) and a cemented carbide ball (cutting tool) coated with TiAlN under three contact pressures of 0.5, 1, and 2 GPa at a range of sliding speeds starting from 0.2 m/s to 1.6 m/s. The tests were conducted under both dry and lubricated conditions using three commercial cutting fluids of CSF 35 straight oil, Vasco 6000, and Zubora 67H emulsions. Also, the MWFs were tested for their rheological properties and wettability. The tribometer results validated the same COF trend as that in the actual metal-cutting experiments, particularly at 0.5 and 1 GPa in dry conditions. Moreover, Zubora 67H emulsion is proven to be the optimal choice due to it reducing the COF between the rubbing contacts by up to 78%. Furthermore, it showed the lowest contact angle and viscosity index, revealing its ability to easily penetrate, especially at higher temperatures, within the secondary cutting zone.

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Section: Rheological Behaviorsupporting

confidence: 83%

Evaluating the Tribological Behaviour in Cutting Operations Using a Modified Ball-on-Disc Open Tribotester

Nassef,

Pape,

Poll

et al. 2024

Lubricants

View full text Add to dashboard Cite

show abstract

“…Heat management can simply be waiting for the work piece to cool down either naturally, or with forced air, water cooling or any other viable method. 13 For performing laser cuts waiting times can be implemented between consecutive cuts to allow cooling down of the material, these waiting times are considered to be a loss in the process time. When the mentioned waiting times add up during mass production it becomes an issue for many manufacturers, even if the time frame is in seconds.…”

Section: Motion On-the-fly Scientific Approachmentioning

confidence: 99%

High-power laser material processing utilizing high-speed combined motion of scanner and linear axes system

Jamalieh,

Entessari,

Bohrer

2024

High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XIII

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An innovative approach to enhance high-power laser material processing through the integration of a high-speed motion system combining galvo scanner and linear axes is presented. The research focuses on its application in Printed Circuit Board (PCB) depaneling with lasers, showcasing its precision and efficiency. The combined motion system, featuring a galvo laser beam scanner and multi-axis linear actuator structure, is custom-designed to meet the specific demands of PCB depaneling. By combining the strengths of both components, complicated cutting patterns are achieved while minimizing thermal and mechanical stresses on the workpiece. Key to the system's success is the seamless integration of motion components, facilitating precise coordination and synchronization during laser processing. This ensures real-time transmission of control commands and feedback signals across multiple axes, optimizing the system's accuracy and efficiency.The motion system enables combined kinematic laser processing with fieldbus cycle times of 250 µs and galvo 2D repositioning time each 25 µs. This is achieved with own developed electronics and FIFO buffering of interpolated XY positions to achieve the desired trajectory within microseconds. This paper describes the analytical model used to achieve the combined motion as well as validation of motion and cutting velocities within the system. The experimental results show excellent process efficiency and high cut quality for several tested materials.As a conclusion, a high-speed motion system with coupled kinematics shows substantial advantages in laser materials processing thus revolutionizing manufacturing processes.

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“…Sustainable transportation systems, especially those dependent on electric power, exhibit intricate interplay among diverse elements, including battery properties, vehicle kinetics, ambient variables, and driving circumstances. [6]- [10] Effective energy management in these systems requires advanced algorithms that can accurately forecast energy use and optimize energy utilization in real-time.…”

Section: Background and Contextmentioning

confidence: 99%

Predictive machine learning for optimal energy management in sustainable transportation systems

Ivanovich Vatin,

Manasa

2024

MATEC Web Conf.

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This study explores the use of predictive machine learning techniques to enhance energy management in sustainable transportation systems, with a specific emphasis on electric vehicles (EVs). The analysis of EV specifications has shown a wide variety of battery capacities, ranging from 55 kWh to 75 kWh. These capacities have a direct impact on the energy storage capacity and the possible driving range of the vehicles. The range of vehicle weights, ranging from 1400 kg to 1700 kg, emphasized the possible effects on energy consumption rates and overall efficiency. The performance capabilities were shown with maximum speeds ranging from 160 km/h to 200 km/h. The energy consumption rates ranged from 0.18 kWh/km to 0.25 kWh/km, suggesting different levels of efficiency. An analysis of energy management data revealed that the lengths traveled varied from 180 km to 220 km, while the average speeds ranged from 50 km/h to 60 km/h. These variations directly affected the rates at which energy was used. The vehicles exhibited higher efficiency metrics by attaining energy consumption rates of 4.0 km/kWh to 5.6 km/kWh. The analysis of ambient variables indicated temperature fluctuations ranging from 20°C to 30°C, as well as a variety of terrain types that impact driving conditions and energy requirements. Predictive machine learning models demonstrated high accuracies, with Mean Absolute Error (MAE) values ranging from 0.13 to 0.18 kWh/km, Root Mean Squared Error (RMSE) values ranging from 0.18 to 0.22 kWh/km, and R-squared (R^2) scores ranging from 0.80 to 0.88. These results emphasize the need of using predictive machine learning to estimate energy consumption, optimize energy management systems, and address sustainable transportation concerns in order to improve the efficiency and sustainability of electric vehicles.

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Application of coolants during tool-based machining – A review

Cited by 50 publications

References 84 publications

Evaluating the Tribological Behaviour in Cutting Operations Using a Modified Ball-on-Disc Open Tribotester

Evaluating the Tribological Behaviour in Cutting Operations Using a Modified Ball-on-Disc Open Tribotester

High-power laser material processing utilizing high-speed combined motion of scanner and linear axes system

Predictive machine learning for optimal energy management in sustainable transportation systems

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