Wire EDM Axis Feed Control with Adaptive Threshold
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Solution Overview
Problem
Wire electric discharge machines face challenges in preventing mechanical section damage due to erroneous operator actions, as existing solutions require setting a proper threshold value for alarm stops, which is burdensome and affects operability, especially with varying environmental conditions like temperature and lubricant viscosity.
Innovation Solution
A machine learning device is integrated into the wire electric discharge machine to adjust the movement command and abnormal load threshold based on real-time state data, using reinforcement learning to optimize axis feed commands and prevent damage by learning from environmental and operational states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the threshold value is set to a large value to prevent false alarms during normal operation, then the operability is improved, but the mechanical section may become damaged before the alarm stop activates
Solution Approach 1:
The patent applies dynamics by making the threshold value adjustable and adaptable rather than fixed. The system dynamically adjusts the threshold based on actual operating conditions, allowing it to be higher during normal operation to prevent false alarms and lower during risky operations to prevent damage, thus resolving the contradiction between operability and protection.
Solution Approach 2:
The patent changes the parameter of the threshold value based on detected operation states. By monitoring operation patterns and adjusting the threshold parameter accordingly, the system achieves both high operability during normal operation and adequate protection during abnormal conditions, preventing mechanical damage while avoiding false alarms.
2Reliability
If the threshold value is set to a small value to stop the movable axis before mechanical section damage, then the protection of mechanical section is improved, but the alarm stop is activated due to slight load fluctuation in normal operation, leading to deterioration in operability
Solution Approach 1:
The system dynamically adjusts the threshold value based on the detected operation state. During normal operation with stable load patterns, the threshold is set higher to avoid false alarms. During operations with higher risk of damage, the threshold is lowered to provide adequate protection, thus resolving the contradiction between protection and operability.
Solution Approach 2:
The system uses feedback from load monitoring and operation state detection to continuously adjust the threshold value. By analyzing the relationship between operation patterns and load fluctuations, the system learns to set appropriate thresholds that prevent false alarms during normal operation while providing protection during abnormal conditions.
3Object-affected harmful factors
If manual application of lubricant is performed to reduce axial loading during high-speed movement, then the axial loading is reduced and alarm prevention is improved, but the preparation work becomes troublesome and operability deteriorates
Solution Approach 1:
The system applies lubricant automatically without requiring manual intervention from the operator. The lubricant application mechanism is integrated into the system and operates autonomously based on detected movement conditions, eliminating the troublesome preparation work while effectively reducing axial loading during high-speed movements.
Solution Approach 2:
The system performs preliminary lubricant application before high-speed movement begins. By detecting the start of movement and applying lubricant in advance, the system ensures proper lubrication is in place before high-speed operation, reducing axial loading and preventing false alarms without requiring manual preparation.
4Adaptability or versatility
If adjustment of movement command and threshold value is performed manually in accordance with environmental state, then the adaptability to environmental conditions is improved, but the burden on the operator becomes considerably heavy
Solution Approach 1:
The system automatically detects environmental conditions and adjusts the movement command and threshold value without requiring manual intervention. The control unit monitors temperature, humidity, and other environmental factors, and autonomously optimizes the parameters, maintaining adaptability while eliminating the heavy burden on the operator.
Solution Approach 2:
The system uses feedback from environmental sensors to continuously adjust movement commands and threshold values. By monitoring environmental conditions and their impact on axial loading, the system automatically adapts its operation parameters, achieving environmental adaptability without requiring operator involvement in the adjustment process.
Data Source
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AI summary
A wire electric discharge machine includes a machine learning device that learns an adjustment of an axis feed command of the wire electric discharge machine. The machine learning device determines an adjustment amount of the axis feed command by using data related to a movement state of an axis, and adjusts the axis feed command based on the determined adjustment amount of the axis feed command. Subsequently, the machine learning device performs machine learning of the adjustment of the axis feed command based on the determined adjustment amount of the axis feed command, the data related to the movement state of the axis, and a reward calculated based on the data related to the movement state of the axis.