Machine Tool Thermal Displacement Compensation Using Machine Learning
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Solution Overview
Problem
Existing machine tool thermal displacement compensation techniques are not highly accurate and are costly due to the need for multiple temperature sensors, time delays in heat measurement, and varying machine structures, leading to complex compensation formulas and increased production and maintenance costs.
Innovation Solution
A machine learning device that optimizes thermal displacement estimation calculation formulas using machine learning algorithms, such as neural networks and regularization methods, based on measured temperature and operating data to derive accurate compensation values, reducing the need for extensive sensor placement and minimizing production and maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple temperature sensors are installed to acquire characteristic values about the machine tool, then measurement precision is improved, but device complexity and production cost increase
Solution Approach 1:
The invention extracts only the essential thermal characteristics needed for compensation by using a single temperature sensor to measure ambient temperature, then deriving the necessary thermal parameters through machine learning models rather than installing multiple sensors throughout the machine tool structure
Solution Approach 2:
The invention replaces the traditional mechanical approach of using multiple physical temperature sensors with a computational approach using machine learning algorithms that process ambient temperature data and operating state data to estimate thermal displacement
2Measurement precision
If multiple measurement instruments are added to locate temperature sensors at appropriate positions, then measurement precision is improved, but production cost and maintenance cost increase
Solution Approach 1:
The single temperature sensor serves multiple functions by measuring ambient temperature that correlates with various thermal conditions in the machine tool, eliminating the need for multiple dedicated sensors at different locations
Solution Approach 2:
The machine learning system automatically identifies and adapts to the optimal sensor placement and measurement strategy through learning from operating data, eliminating the need for manual instrumentation planning and installation
3Adaptability or versatility
If compensation formulas are changed to adapt to different machine types and environmental conditions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The invention uses dynamic machine learning models that automatically adapt to different machine types and environmental conditions by learning from operating data, replacing static compensation formulas with adaptive computational models
Solution Approach 2:
The machine learning system changes its internal parameters and model structure based on the specific machine type and environmental conditions through the learning process, allowing a single system to handle multiple configurations without manual formula changes
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 solution achieves highly accurate thermal displacement compensation at a lower cost by optimizing thermal displacement estimation and compensation formulas through machine learning, adapting to varying machine environments and structures, and streamlining sensor usage.
Implementation Method 1
relative thermal displacement caused between a tool and a workpiece by thermal expansion of a machine element in the machine tool
Data Source
AI summary
A machine learning device includes: a measured data acquisition unit that acquires a measured data group; a thermal displacement acquisition unit that acquires a thermal displacement actual measured value about a machine element; a storage unit that uses the measured data group acquired by the measured data acquisition unit as input data, uses the thermal displacement actual measured value about the machine element acquired by the thermal displacement acquisition unit as a label, and stores the input data and the label in association with each other as teaching data; and a calculation formula learning unit that performs machine learning based on the measured data group and the thermal displacement actual measured value about the machine element, thereby setting a thermal displacement estimation calculation formula used for calculating the thermal displacement of the machine element based on the measured data group.