Machine Tool Thermal Displacement Control With Learned Correction Coefficients
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Solution Overview
Problem
Existing machine tools face challenges in accurately correcting thermal displacement caused by various heat sources, leading to inaccuracies in positioning between the cutting edge of a tool and a workpiece.
Innovation Solution
A controller and machine tool system that utilizes data collection circuitry for machining data, temperature circuitry for temperature data, dimension data input circuitry, learning data generation circuitry, and machine learning circuitry to generate a correction coefficient for thermal displacement based on temperature data and learning data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple temperature sensors are mounted on components to correct thermal displacement, then the correction accuracy improves, but the device complexity increases
Solution Approach 1:
The patent divides thermal displacement correction into separate components: drive-system thermal displacement correction and environment temperature-system thermal displacement correction. Each component is measured and corrected independently, allowing complex thermal effects to be broken down into manageable segments that can be addressed with appropriate sensing and calculation methods.
Solution Approach 2:
The patent introduces a thermal displacement correction amount setting changer as an intermediary device that coordinates between multiple temperature sensors and the control system. This intermediary processes temperature data from multiple sensors and generates appropriate correction amounts, reducing the complexity of directly managing multiple sensors and their data.
2Measurement precision
If temperature data is collected at frequent intervals to improve correction accuracy, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The patent performs preliminary thermal displacement correction by calculating correction amounts in advance based on temperature trends and thermal characteristics. The system predicts thermal displacement and applies correction before the actual displacement occurs, reducing the need for frequent real-time measurements and processing while maintaining accuracy.
Solution Approach 2:
The patent implements periodic temperature measurement and correction updates rather than continuous monitoring. Temperature data is collected at predetermined intervals, and correction amounts are updated periodically based on accumulated data and thermal models, balancing measurement precision with time efficiency.
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
The system effectively corrects thermal displacement by using machine learning to determine accurate correction coefficients, thereby improving the accuracy of positioning between the cutting edge and the workpiece.
Implementation Method 1
Components of a machine tool undergo thermal expansion due to heat generated by operating the machine tool and external heat around the machine tool. Thermal expansion is known to cause an error in the positioning between the cutting edge of a tool and a workpiece machined
Data Source
AI summary
A controller includes data collect circuitry configured to collect machining data including a date and a time when at least one machined portion of a workpiece has been machined by a machine tool, temperature circuitry configured to obtain, at predetermined time intervals, temperature data at positions on the machine tool, dimension data input circuitry configured to receive dimension measurement data which includes a dimension of the machined portion after the machined portion has been machined, learning data generate circuitry configured to generate learning data based on the machining data and the dimension measurement data, and machine learning circuitry configured to execute a machine learning based on the temperature data and the learning data to obtain a correction coefficient based on which a displacement caused by a change in a temperature of the machine tool is corrected according to a thermal displacement correction equation.