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<p>This study conducted multi-physical coupling analyses on the cooling channel of the built-in spindle in machine tools. To enhance the accuracy of the analytical model, the boundary conditions were adjusted based on actual machine tool tests and validations. The optimal combination of cooling efficiency and energy saving was then determined using the response surface methodology. The research was divided into three main sections. First, structural temperature and thermal error were simulated using multi-physical coupling analyses. The accuracy of the model prediction was verified through an actual machine tool test, and the model parameters were adjusted according to the data on inlet/outlet coolant temperatures, pressures, and structural temperatures to control the model’s thermal error. The deviation between actual measurement and model predictions is within 5%. Second, the response surface experiment method was employed for multi-factor optimization analysis. The optimal combination that minimizes coolant flow while requiring the fewest calculations was identified. The findings revealed that the bearing channel coolant flow is the primary influencing factor, with the model’s prediction accuracy reaching 82.48%. When maintaining the same thermal error observed in actual machine measurements, coolant flow could be reduced by 10.3%. Third, the energy consumption and carbon emissions of the cooler under various coolant flow combinations were calculated. For the coolant flow combination that maintained the same thermal error as the actual machine measurement, monthly electricity consumption could be reduced by 94.48 kWh, resulting in a reduction in carbon emissions by 57.25&#xa0;kg <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\text{CO}}_{2}\text{e}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mtext>CO</mtext> <mn>2</mn> </msub> <mtext>e</mtext> </mrow> </math></EquationSource> </InlineEquation>. This demonstrates that implementing appropriate energy-saving measures to reduce coolant flow leads to a reduction in carbon emissions and provides considerable environmental and economic benefits which leads to sustainable manufacturing.</p>

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Optimization of Machine Tool Spindle Cooling for Enhancement of Thermal Prediction Accuracy and Energy Efficiency

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Cheng-Kai Huang,
  • Tsung-Chia Chen,
  • Kun-Ying Li,
  • Yuan-Hong Tsai,
  • Swami Nath Maurya

摘要

This study conducted multi-physical coupling analyses on the cooling channel of the built-in spindle in machine tools. To enhance the accuracy of the analytical model, the boundary conditions were adjusted based on actual machine tool tests and validations. The optimal combination of cooling efficiency and energy saving was then determined using the response surface methodology. The research was divided into three main sections. First, structural temperature and thermal error were simulated using multi-physical coupling analyses. The accuracy of the model prediction was verified through an actual machine tool test, and the model parameters were adjusted according to the data on inlet/outlet coolant temperatures, pressures, and structural temperatures to control the model’s thermal error. The deviation between actual measurement and model predictions is within 5%. Second, the response surface experiment method was employed for multi-factor optimization analysis. The optimal combination that minimizes coolant flow while requiring the fewest calculations was identified. The findings revealed that the bearing channel coolant flow is the primary influencing factor, with the model’s prediction accuracy reaching 82.48%. When maintaining the same thermal error observed in actual machine measurements, coolant flow could be reduced by 10.3%. Third, the energy consumption and carbon emissions of the cooler under various coolant flow combinations were calculated. For the coolant flow combination that maintained the same thermal error as the actual machine measurement, monthly electricity consumption could be reduced by 94.48 kWh, resulting in a reduction in carbon emissions by 57.25 kg \({\text{CO}}_{2}\text{e}\) CO 2 e . This demonstrates that implementing appropriate energy-saving measures to reduce coolant flow leads to a reduction in carbon emissions and provides considerable environmental and economic benefits which leads to sustainable manufacturing.