Machine Tool Thermal Displacement Correction via Operational Replay
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Solution Overview
Problem
Users face challenges in calculating thermal displacement amounts for machine tools on-site due to the need for extensive data collection, which is impractical and environment-specific, making it difficult to derive accurate equations for thermal displacement correction using conventional machine learning methods.
Innovation Solution
A method that involves obtaining operational status information from a user's machine tool, reproducing the same operational status on a manufacturer's machine tool of the same type, measuring temperature and thermal displacement, and using machine learning to calculate parameters for thermal displacement correction, allowing for accurate updates without stopping the machine tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measured values for thermal displacement amount are obtained by stopping the machine tool and using measurement devices, then the accuracy of thermal displacement estimation is improved, but the availability of the machine tool deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing thermal displacement measurement data during machine tool operation. Temperature sensors and displacement sensors continuously monitor and store thermal displacement data in a database during normal operation, so that when correction is needed, pre-collected data can be used immediately without stopping the machine tool for measurements.
Solution Approach 2:
The patent uses copying by creating a virtual model of the machine tool's thermal behavior through machine learning. A neural network is trained using historical operational data and thermal displacement measurements to create a digital twin that can predict thermal displacement in real-time, replacing the need for physical measurement devices and stopping the machine tool.
2Measurement precision
If an enormous amount of data regarding operating state and thermal displacement amount is collected to obtain an optimal equation by machine learning, then the accuracy of thermal displacement estimation is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies universality by designing a data collection system that serves multiple functions simultaneously. The same sensors and data collection infrastructure used for general machine tool monitoring and control are also utilized for gathering thermal displacement data, eliminating the need for separate dedicated measurement systems and reducing overall system complexity.
Solution Approach 2:
The patent implements self-service by enabling the machine tool to automatically collect, store, and process its own operational data and thermal displacement measurements. The system uses its existing sensors and controllers to gather data during normal operation, and the machine learning model automatically trains on this data without requiring external intervention or complex external processing systems.
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
Enables accurate thermal displacement correction on-site without reducing machine tool availability, improving machining accuracy by leveraging a larger dataset for machine learning and ensuring parameter accuracy.
Implementation Method 1
structures constituting the machine tool are thermally deformed by the ambient temperature in which the machine tool is installed and by heat generated by motion mechanisms of the machine tool operating
Data Source
AI summary
Provided is a thermal displacement correction method using a machine learning method but making it possible to, on a user side, calculate a thermal displacement amount appropriate to a machine tool of the user and correct the thermal displacement. In a machine tool on a target user side, a thermal displacement amount between workpiece and tool corresponding to a temperature at a preset measurement point is calculated based on a parameter defining a relation between the temperature and the thermal displacement amount, and a positioning position for workpiece and tool is corrected in accordance with the calculated thermal displacement amount. On a manufacturer side, operational status information of the machine tool on the target user side is obtained, an operational status identical to the obtained operational status on the target user side is reproduced with a machine tool of a same type as the machine tool on the target user side based on the obtained operational status information, a temperature at a measurement point identical to the measurement point on the machine tool on the target user side and a thermal displacement amount between workpiece and tool are measured during reproduction, and the parameter is calculated by machine learning based on the measured temperature and thermal displacement amount. The parameter in the machine tool on the target user side is updated with the calculated parameter.