Increase in Accuracy of a Built-in Spindle by Adaptive Cooling Control with Varied Coolant Volume and Temperature

Li, Kunying; Luo, Win‐Jet; Zeng, Yu-Ru; Huang, I-Hsing

doi:10.18494/sam.2020.3104

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…(2)(3)(4) Two coolant control methods are often adopted in cooling systems: the varied coolant volume (VOV) control method and the constant coolant volume (COV) control method. (5) Using the VOV control method rather than the COV control method allows fine-tuning of the coolant circulating flow rate in terms of the machining load and spindle rotation speed to remove heat generated in the MTs, particularly internal heat generated by the spindle. The thermal deformation can be significantly reduced in the Y-and Z-directions by 70.1 and 73.5%, respectively, by applying the VOV control method.…”

Section: Introductionmentioning

confidence: 99%

Coolant Volume Prediction for Spindle Cooler with Adaptive Neuro-fuzzy Inference System Control Method

Hsieh¹,

Maurya²,

Luo³

et al. 2022

Sensors and Materials

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Machining dynamics plays an essential role in machine tools (MTs) and machining operations, directly impacting the material removal rate, surface quality, and dimensional accuracy. With the increasing use of numerical control (NC) MTs and increasing automation of production, machining inaccuracy due to thermal deformation has become a significant issue. Furthermore, since the rotation speed and feed rate have risen, more heat is produced in MTs. In addition, since high machining precision is now required, techniques to avoid or regulate thermal deformation are also needed. Traditionally, researchers used the finite element method, support vector method (SVM), regression analysis method, neural network, and other methods to predict and compensate for the thermal deformation of MT spindles. However, these methods do not directly reduce the thermal errors caused by the heat source to improve the accuracy of MTs. Therefore, in this study, a cooling control method is proposed to reduce the impact of heat on the spindle by using a thermal suppression technique accompanied by the adaptive neurofuzzy inference system (ANFIS) control method to predict the static thermal behavior of a spindle. The root mean square error (RMSE) is used as the ANFIS evaluation index to reflect the quality of the prediction model. This method is implemented in Simulink to simulate the dynamics of the coolant during the real-time monitoring of the cutting and resting positions of the MT. Finally, a thermal deformation prediction model is generated to demonstrate that the cooling control method developed in this study can be applied to the intelligent cooling of an MT. It is shown that the developed ANFIS prediction model is efficient for controlling cooling. A simulation also shows that this method can reduce the running cost, energy consumption and accurately predict the thermal deformation of MT spindles. It is predicted that the most suitable coolant pump operating frequencies for the spindle at rotation speeds

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Section: Introductionmentioning

confidence: 99%

Coolant Volume Prediction for Spindle Cooler with Adaptive Neuro-fuzzy Inference System Control Method

Hsieh¹,

Maurya²,

Luo³

et al. 2022

Sensors and Materials

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally, the generating heat inside the MT is removed by using the cooling system in the MT [5,6]. For the cooling system, compared to the conventional constant coolant volume (COV) method, the varied coolant volume (VOV) control method allows adjustment of the coolant circulating flow rate in terms of machining load and spindle rotating speeds to remove the heat generated by the MTs [7,8]. Internal heat generated by the spindle and the thermal deformations in the y-and z-axes can be reduced by 70.1% and 73.5%, respectively, using the VOV method [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of Coolant Temperature on the Thermal Compensation of a Machine Tool

Maurya

Luo

et al. 2022

Machines

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Machine tool (MT) accuracy is an important factor in the industry and is affected by heat generation through internal and external moving parts; the electrical components used; and variable environmental temperatures. Thermal errors lead to 40–60% of all MT errors. To improve MT accuracy, efficient techniques to minimize thermal errors must be identified. This study investigated the coolant temperature effects under different rotating speeds of a standalone built-in spindle system and computer numerical control (CNC) machine with a direct-drive spindle on the accuracy of thermal deformation prediction. The z-axis thermal deformation of the standalone built-in spindle system and CNC machine with a direct-drive spindle was conducted at different spindle rotating speeds and coolant temperatures at a constant coolant flow rate of 5 LPM. All experiments were conducted in a steady and dynamic operation according to ISO 230-3. For the standalone built-in spindle system, in comparison to the Mares model, the developed new model based on the coolant temperature effect on the Mares model (Mares CT model) can improve the thermal deformation prediction accuracy by 18.17% to 39.50% at different coolant temperatures of 12 ∘C to 26 ∘C and the accuracy can be controlled within the range of 0.03 μm to 5.24 μm, while the supply coolant temperature is above 16 ∘C. However, the thermal compensation analysis of the Mares CT model for a CNC machine with a direct-drive spindle shows a thermal deformation prediction accuracy improvement of 58.30% to 66.35% at different coolant temperatures of 22 ∘C to 28 ∘C and the accuracy can be controlled within the range of 0.14 μm to 4.05 μm. To validate the feasibility of the compensation model in real machining processes, dynamic operational analysis was performed for a standalone built-in spindle system and a CNC machine with a direct-drive spindle, and the thermal deformation prediction accuracy improved by 12.19% to 35.53% with the standalone built-in spindle system and 40.25% to 60.33% with the CNC machine with a direct-drive spindle. The compensation model analysis shows that the coolant temperature has a high impact on thermal deformation prediction and markedly affects system accuracy within certain limits.

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Thermal deformation and economic analysis of a multiobject cooling system for spindles with varied coolant volume control

Maurya

Lee

et al. 2023

Int J Adv Manuf Technol

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Increase in Accuracy of a Built-in Spindle by Adaptive Cooling Control with Varied Coolant Volume and Temperature

Cited by 6 publications

References 13 publications

Coolant Volume Prediction for Spindle Cooler with Adaptive Neuro-fuzzy Inference System Control Method

Coolant Volume Prediction for Spindle Cooler with Adaptive Neuro-fuzzy Inference System Control Method

Effect of Coolant Temperature on the Thermal Compensation of a Machine Tool

Thermal deformation and economic analysis of a multiobject cooling system for spindles with varied coolant volume control

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