Machine Tool Component Evaluation Under Flexible Processing Conditions
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Solution Overview
Problem
Current machine tool evaluation methods are inflexible, on-site, and based on experience, leading to high error and uncertainty in evaluating machine tools and their components, and are unable to comprehensively assess machine tools under various flexible processing conditions.
Innovation Solution
A machine tool evaluation method that determines target components, acquires relevant data, preprocesses and features extracts it, and performs multi-level evaluations based on type and attribute information, enabling comprehensive and accurate assessments of machine tools across various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If manual inspection and scheduled maintenance are used for machine tool evaluation, then the evaluation can be performed regularly, but the evaluation accuracy and reliability are poor due to being experience-based and inflexible
Solution Approach 1:
The patent replaces manual inspection methods with an automated evaluation system that collects operational data, monitoring data, and maintenance data through sensors and information processing devices. This substitution of mechanical/manual evaluation with automated data-driven analysis significantly improves evaluation reliability while maintaining regular evaluation frequency.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting operational data from sensors, analyzing it through processing devices, and using the results to adjust and improve evaluation accuracy over time. The feedback loop enables the system to learn from actual machine tool performance and refine evaluation models.
2Device complexity
If condition monitoring methods are used that monitor only overall operation trend with single type signal and single analysis method, then the monitoring system remains simple, but the evaluation accuracy is insufficient and cannot reflect real machine tool state
Solution Approach 1:
The patent segments the evaluation system into multiple independent components: operational data collection, monitoring data collection, maintenance data collection, and综合分析. Each component handles specific data types independently, then integrates them for comprehensive evaluation. This segmentation allows the system to process multiple signal types and analysis methods while maintaining manageable system complexity.
Solution Approach 2:
The evaluation system is designed with multi-functionality to handle diverse data types including operational parameters, vibration signals, temperature data, and maintenance records. The system can perform multiple analysis functions simultaneously, adapting to different machine tool types and evaluation requirements, thereby improving accuracy without proportionally increasing complexity.
3Device complexity
If single variable or single feature is used for machine tool evaluation, then the analysis process remains simple, but the evaluation cannot comprehensively reflect machine tool performance and changes under various processing conditions
Solution Approach 1:
The patent transitions from single-variable evaluation to multi-dimensional evaluation by incorporating operational data, monitoring data, and maintenance data as different dimensions. Each dimension provides unique insights into machine tool state, and their integration creates a comprehensive evaluation model that adapts to various processing conditions while maintaining systematic analysis.
Data Source
AI summary
A machine tool evaluation method, a machine tool evaluation system and medium are disclosed. The method includes: determining a target component of a machine tool; and for each target component: acquiring type information and attribute information of the target component; determining and acquiring, based on type information and the attribute information, target operating condition data, target state monitoring data, and target design parameter data corresponding to the target component and generating an original data set based on the target operating condition data, the target state monitoring data and the target design parameter data; preprocessing the original data set to obtain a target data set; performing feature extraction on the target data set to obtain a feature data set; and performing a multi-level evaluation on the target component, and generating an evaluation result of the target component; and generating an evaluation result of the machine tool based on the evaluation results.


