Machine Tool Spindle Cooling Control for Accurate Thermal Displacement
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Solution Overview
Problem
Existing methods for estimating thermal displacement in machine tools are inaccurate when the spindle cooling apparatus is operated during machine operation, as they fail to account for the different thermal properties between running and stopped states, leading to potential bearing failures and increased power consumption.
Innovation Solution
A method involving a machine tool with sensors to determine the cooling state of a heat-generating portion and select an appropriate estimation model to calculate an accurate temperature rise value, which is then used to estimate thermal displacement and control the cooling apparatus, ensuring stable operation and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the spindle cooling apparatus is operated during machine operation to reduce thermal displacement, then machining accuracy is improved, but power consumption increases
Solution Approach 1:
The cooling apparatus operation is made dynamic by switching between operating and stopped states based on real-time thermal displacement estimates. The system adapts the cooling state to match actual thermal conditions, operating only when thermal displacement exceeds thresholds, thereby reducing unnecessary power consumption while maintaining machining accuracy.
Solution Approach 2:
The system implements feedback control by continuously estimating thermal displacement using temperature information from multiple sensors and the selected estimation model. This feedback loop allows the control device to monitor thermal conditions and adjust cooling apparatus operation accordingly, optimizing the balance between machining accuracy and power consumption.
2Use of energy by moving object
If the cooling apparatus operation is controlled during machine operation to reduce power consumption, then energy efficiency is improved, but thermal displacement estimation accuracy deteriorates
Solution Approach 1:
The system applies local quality by selecting different estimation models based on the cooling apparatus state. When the cooling apparatus is operating, a first estimation model is used; when stopped, a second estimation model is used. This localized adaptation of the estimation approach to the current cooling state maintains high estimation accuracy regardless of the cooling apparatus operation mode.
Solution Approach 2:
The system changes estimation parameters by switching between different estimation models according to the cooling apparatus state. This parameter change allows the system to account for the different thermal properties and cooling effects present in operating versus stopped states, maintaining estimation accuracy across varying power consumption conditions.
3Stability of the object's composition
If the cooling capacity is reduced to suppress thermal displacement fluctuation, then thermal stability is improved, but bearing temperature rises causing seizure risk
Solution Approach 1:
The system uses feedback from thermal displacement estimation to monitor bearing thermal conditions in real-time. By continuously estimating thermal displacement based on temperature information and the selected model, the control device can detect early signs of bearing overheating and adjust cooling capacity before seizure occurs, maintaining both thermal stability and bearing reliability.
Solution Approach 2:
The system takes preliminary action by continuously monitoring thermal displacement and bearing temperature conditions before critical failure occurs. The estimation models predict thermal trends, allowing the control device to proactively adjust cooling capacity to prevent bearing seizure before it happens, rather than reacting after damage occurs.
4Reliability
If heat flow sensors are installed near the bearing to detect temperature differences, then bearing failure detection is improved, but device complexity increases
Solution Approach 1:
The system uses thermal displacement estimation as an intermediary to indirectly monitor bearing temperature conditions. Instead of directly installing sensors on the bearing, the control device estimates bearing thermal state through temperature information from accessible sensors and the selected estimation model, achieving bearing monitoring without complex sensor installation.
Solution Approach 2:
The system replaces the mechanical sensor installation approach with a computational estimation approach. Instead of physically installing heat flow sensors near the bearing, the system uses mathematical models and temperature data processing to estimate bearing thermal conditions, reducing device complexity while maintaining reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate estimation and control of thermal displacement and temperature differences in bearings, preventing failures like seizure and optimizing power usage by adapting to the cooling apparatus's state during machine operation.
Implementation Method 1
a flow path is provided in a housing portion outside the bearing to flow cooling oil, and the heat of the cooling oil is removed by a cooling device
Implementation Method 2
a flow path is provided in a housing portion outside the bearing to flow cooling oil
Implementation Method 3
a rotation shaft, such as a spindle, generates heat due to friction between the rotation shaft and a bearing
Data Source
AI summary
A temperature rise value estimating method for a machine tool including a cooling apparatus configured to cool a specific portion and a plurality of sensors. The temperature rise value estimating method includes: determining a cooling state of the specific portion by determining whether the cooling apparatus is in an operating state or a stopped state and determining whether a time measured from a base point of an activation or a stoppage of the cooling apparatus has elapsed a predetermined delay time or not; selecting an estimation model corresponding to the determined cooling state of the specific portion from a plurality of estimation models prepared in advance corresponding to different cooling states of the specific portion; and calculating an estimated temperature rise value of the specific portion based on the selected estimation model and temperature data derived from a measured value acquired by the plurality of temperature sensors.