Machine Learning Control for Stable Workpiece Clamping
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Solution Overview
Problem
In machine tools, the clamping force on workpieces is often insufficient due to cutting resistance, leading to inaccurate machining, and existing solutions like hydraulic and pneumatic pressures require expensive equipment to maintain stability, such as air tanks and cooling devices for hydraulic systems.
Innovation Solution
A controller with a machine learning device that observes machining conditions, spindle torque, and cutting force component direction data to learn and adjust machining parameters, allowing for precise control of clamping force without expensive equipment, by associating cutting force information with spindle torque and machining conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pneumatic pressure or hydraulic pressure is used for clamping force, then the workpiece can be held during machining, but the equipment becomes expensive due to air tanks and cooling devices
Solution Approach 1:
The patent replaces the mechanical pressure systems (pneumatic/hydraulic) with a machine learning-based control system that uses sensor data to dynamically adjust clamping force. The learning unit processes machining condition data, spindle torque data, and cutting force component direction data to determine optimal clamping force, eliminating the need for expensive air tanks and cooling devices while maintaining reliable clamping.
Solution Approach 2:
The system uses the machine tool's own operational data (spindle torque, machining conditions, cutting force directions) to automatically determine and adjust clamping force requirements. The learning unit continuously learns from actual machining data and self-adjusts the clamping force without external intervention or additional expensive equipment.
2Reliability
If a larger cylinder is selected with a higher safety factor, then the workpiece can be securely clamped, but the price and weight of the machining jig increase
Solution Approach 1:
The patent transitions from a static, oversized cylinder design to a dynamic clamping force adjustment system. The learning unit continuously determines optimal clamping force based on real-time machining conditions, allowing the use of a smaller, lighter cylinder that adapts its force output dynamically rather than relying on a fixed safety factor margin.
Solution Approach 2:
The system changes the clamping force parameter dynamically based on machining conditions such as spindle torque and cutting force direction. Instead of using a fixed large cylinder size with built-in safety margin, the actual clamping force is adjusted in real-time to match the actual cutting resistance, enabling use of lighter equipment.
3Force
If hydraulic pressure is used for clamping, then the workpiece can be held firmly, but temperature increase in hydraulic oil causes viscosity decrease and leakage increase
Solution Approach 1:
The patent replaces the hydraulic pressure system entirely with a machine learning-based control system that uses electrical actuators or other non-hydraulic means to apply clamping force. This eliminates the hydraulic oil and its temperature-related viscosity and leakage problems while maintaining the ability to apply firm clamping force through learned optimization.
Data Source
AI summary
In a controller, a machine learning device, and a system that are capable of addressing change in a clamping force without use of expensive equipment, the controller includes the machine learning device that observes machining condition data indicating machining conditions for cutting, spindle torque data indicating spindle torque during the cutting, and cutting force component direction data indicating cutting force component direction information on cutting resistance against a cutting force, as state variables representing a current state of an environment, and that carries out learning or decision making with use of a learning model modelling the machining conditions for the cutting on which the cutting force that allows holding by a clamping force from a machining jig is exerted on a workpiece based on the state variables.