NC Vibration Cutting Control for Multi-Axis Spindle Synchronization
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Solution Overview
Problem
Existing numerical control devices are unable to effectively perform vibration cutting on machine tools with multiple drive shafts, as they are designed for single drive shaft systems, limiting their capability to machine rotating workpieces efficiently.
Innovation Solution
A numerical control device incorporating a control computation unit that manages multiple drive axes and a machine learning device to predict pass/fail conditions for vibration cutting, using observation and data acquisition units to learn from state variables and pass/fail information, allowing synchronization of vibration frequencies with spindle rotation speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a numerical control device is designed for single drive shaft systems, then it can control vibration cutting effectively for that configuration, but it cannot perform vibration cutting on machine tools with multiple drive shafts
Solution Approach 1:
The numerical control device is designed with a universal control architecture that can manage both single and multiple drive shaft configurations. The control computation unit incorporates a machine learning device that learns optimal vibration cutting parameters for different drive shaft arrangements, enabling the same device to adapt to various machine tool configurations without requiring dedicated control systems for each type.
Solution Approach 2:
The control system dynamically adjusts vibration cutting parameters based on the detected drive shaft configuration. The machine learning device continuously learns from operational data to optimize control strategies for different numbers of drive shafts, transforming the static control architecture into a dynamic system that adapts to changing operational requirements.
2Manufacturing precision
If vibration cutting is performed without synchronizing vibration frequency with spindle rotation speed, then the control system is simpler, but machining precision and chip separation deteriorate
Solution Approach 1:
The control computation unit implements feedback mechanisms that continuously monitor spindle rotation speed and adjust vibration cutting parameters accordingly. The machine learning device learns the relationship between spindle speed and optimal vibration frequency, creating a closed-loop control system that maintains synchronization dynamically, thereby achieving high machining precision while managing control complexity through intelligent algorithms.
Solution Approach 2:
The system dynamically changes vibration frequency parameters based on detected spindle rotation speed. By using the machine learning device to predict optimal parameter combinations, the system automatically adjusts vibration cutting parameters to maintain synchronization, improving machining precision without requiring overly complex manual control mechanisms.
Data Source
AI summary
A numerical control device includes a control computation unit to control a spindle that is a rotation axis of a machining target, first and second drive axes to drive a first tool and a second tool, respectively, to perform vibration cutting machining on the machining target. The control computation unit includes a machine learning device to learn a pass/fail prediction in which whether the vibration cutting machining passes is predicted. The machine learning device includes: an observation unit to observe a state variable including a vibration cutting condition for the first and second drive axes for the vibration cutting machining; a data acquisition unit to acquire pass/fail information indicating whether the vibration cutting machining has passed; and a learning unit to learn the pass/fail prediction according to a data set based on a combination of the state variable and the pass/fail information.