Machine Tool Temperature Prediction for Thermal Displacement Correction
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Solution Overview
Problem
Existing methods struggle to accurately predict environmental temperature changes in machine tool installations, especially when air conditioning and heat insulation properties of the plant environment are involved, leading to inaccuracies in thermal displacement correction and machining accuracy.
Innovation Solution
An environmental temperature change prediction device and method that classify temperature change trends into patterns using machine body and peripheral temperature data, outside temperature data, and classification rules, generating appropriate prediction models to forecast future temperature changes based on plant environment patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If thermal displacement correction is used to compensate for temperature changes, then machining accuracy can be maintained under small temperature variations, but machining accuracy deteriorates when the installation environment temperature changes significantly
Solution Approach 1:
The system performs preliminary classification of plant environment patterns and generation of prediction models before actual machining operations. By pre-analyzing temperature data and categorizing environment patterns (e.g., air-conditioned vs. non-air-conditioned plants), the system prepares appropriate prediction models in advance, enabling accurate thermal displacement compensation even under large temperature changes.
Solution Approach 2:
The system changes the parameters of the prediction model based on the classified plant environment pattern. Different environment patterns (air-conditioned, non-air-conditioned, seasonal variations) have different temperature characteristics, so the system adjusts model parameters such as temperature coefficients, time constants, and prediction algorithms to match the specific environment, thereby maintaining accuracy across diverse conditions.
2Manufacturing precision
If empirical rules are used to conduct dimension confirmation and correction based on room temperature changes, then machining accuracy can be maintained, but the process requires manual intervention and is not systematic
Solution Approach 1:
The system performs self-service by automatically classifying plant environment patterns, selecting appropriate prediction models, and calculating thermal displacement corrections without manual intervention. The system autonomously analyzes temperature data from sensors, identifies environment patterns (air-conditioned, non-air-conditioned, seasonal), and applies the correct prediction algorithm, eliminating the need for workers to manually conduct dimension confirmation and correction based on empirical rules.
Solution Approach 2:
The system implements continuous feedback by monitoring temperature changes in real-time, comparing actual temperatures with predicted temperatures, and automatically adjusting thermal displacement corrections. The system uses feedback from temperature sensors and machining results to refine prediction model parameters, creating a closed-loop system that continuously improves accuracy without manual intervention.
3Measurement precision
If computer simulation is used to predict room temperature change by inputting building heat insulating performance and air conditioning settings, then prediction accuracy can be improved, but it is difficult to accurately input all required information in actual production sites
Solution Approach 1:
The system extracts only the essential information needed for accurate temperature prediction from the complex set of parameters required by full computer simulation. Instead of requiring all building heat insulating performance data, air conditioning settings, and equipment heat generation information, the system extracts key temperature data from sensors and outside temperature data, classifies environment patterns based on these extracted features, and generates simplified prediction models that achieve adequate accuracy without comprehensive input.
Solution Approach 2:
The system uses simple, easily obtainable temperature data from standard sensors and public weather data as substitutes for complex, difficult-to-obtain building performance data. Rather than investing in comprehensive building audits and detailed equipment specifications, the system uses readily available temperature measurements and classification rules to create effective prediction models that are easier to implement and maintain.
Data Source
AI summary
An environmental temperature change prediction device includes an environmental temperature acquisition unit, an outside temperature acquisition unit, a plant environment pattern setting unit, a prediction model generating unit, and an environmental temperature change prediction unit. The environmental temperature acquisition unit measures a machine body temperature. The plant environment pattern setting unit defines in advance a classification rule for classifying change trends of the environmental temperature into a plurality of patterns based on data of the environmental temperature and the plant outside temperature and environmental temperature prediction models. The prediction model generating unit selects the applicable plant environment pattern and determines a parameter of the environmental temperature prediction model corresponding to the selected plant environment pattern. The environmental temperature change prediction unit predicts a change in the environmental temperature in a future by the environmental temperature prediction model generated in the prediction model generating unit.