Machine Tool Spindle Control Model for Cycle Time and Overheating
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Solution Overview
Problem
Existing technologies fail to optimize machine tool control information for reducing cycle time while preventing overheating, leading to potential productivity declines in machine tools.
Innovation Solution
A learning model construction device and control information optimization device that acquire and analyze input data and temperature information to construct a learning model for adjusting control information, such as spindle operation patterns and parameters, to minimize overheating and achieve shorter cycle times through supervised learning and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the cutting feedrate is raised or the time constant of acceleration/deceleration is lowered to reduce cycle time, then productivity is improved, but the drive device generates excessive heat and may overheat causing damage or malfunction
Solution Approach 1:
The system performs preliminary actions by causing the drive unit to standby for a predetermined time before the temperature rises to a critical level. This proactive approach prevents overheating before it occurs, allowing the system to maintain higher cutting feedrates and shorter cycle times without risking drive device damage.
Solution Approach 2:
The system dynamically adjusts the cutting feedrate and drive unit operation based on real-time temperature conditions. By making the cutting feedrate variable rather than fixed, the system can optimize productivity during cool periods while preventing overheating when temperature thresholds are approached.
2Reliability
If the drive device is temporarily stopped to cool down when temperature rises, then overheating is prevented, but machining is interrupted and productivity declines
Solution Approach 1:
The system introduces a standby period before the drive unit actually stops operation. By causing the drive unit to standby for a predetermined time first, the system allows temperature to stabilize or decrease slightly, then resumes operation before critical overheating occurs, maintaining machining continuity while protecting the drive device.
Solution Approach 2:
The system maintains continuous machining operation by using the standby period to prevent temperature rise rather than stopping the drive unit. This keeps the useful action of machining continuous while still managing temperature through controlled pauses in drive unit operation.
3Temperature
If the cutting feedrate is reduced to prevent overheating, then drive device temperature is controlled, but cycle time increases and productivity declines
Solution Approach 1:
The system makes the cutting feedrate dynamic rather than fixed, adjusting it based on real-time temperature monitoring and drive unit standby timing. This allows the system to maintain high feedrates during cool periods for short cycle times, while reducing feedrate only when necessary to prevent overheating.
Solution Approach 2:
The system changes operational parameters (cutting feedrate, drive unit standby time) based on temperature conditions. By varying these parameters dynamically, the system optimizes the balance between temperature control and productivity, achieving short cycle times when possible while preventing overheating.
Data Source
AI summary
A learning model is constructed for adjusting control information so that a cycle time becomes shorter while also avoiding the occurrence of overheating. A learning model construction device includes:an input data acquisition means that acquires, as input data, control information including a combination of an operation pattern of a spindle and parameters related to machining in a machine tool, and temperature information of the spindle prior to performing the machining based on the control information;a label acquisition means for acquiring temperature information of the spindle after having performed the machining based on the control information as a label; and a learning model construction means for constructing a learning model for temperature information of the spindle after having performed machining based on the control information, by performing supervised learning with a group of the input data and the label as training data.