Machine Tool Stress Classification for Low-Memory Load Tracking
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Solution Overview
Problem
Existing manufacturing processes face challenges in efficiently assessing and recording the stress on machine tools over their service life, particularly due to increasing memory requirements when operating parameters change quickly, and the inability to accurately depict real loads and consumption without reconstructing machining operations.
Innovation Solution
A method involving the measurement of operating parameters during machining, calculation of stress parameters, and their classification into predefined size classes, allowing for efficient storage and analysis of stress events without significant memory increase, enabling diagnostic and maintenance applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operating parameters are measured and stored in detail during machining, then the accuracy of load and consumption assessment is improved, but the memory requirements increase significantly
Solution Approach 1:
The patent segments the continuous operating parameter data into discrete stress parameter categories (e.g., cutting forces, torques, powers, temperatures) and further divides them into size classes. Instead of storing all raw data points, only the categorized stress parameters with their frequency of occurrence are stored, dramatically reducing memory requirements while preserving essential information for load assessment.
Solution Approach 2:
The patent extracts only the essential stress-related information from the complete set of operating parameters. By calculating stress parameters from measured operating parameters and storing only these derived values with their size class frequencies, the system extracts the critical load assessment data while discarding redundant information, thus reducing memory consumption.
2Loss of information
If raw operating parameter data is stored, then complete information about machining operations is preserved, but data security and user privacy are compromised
Solution Approach 1:
The patent extracts only the essential stress parameter information needed for load assessment while removing or anonymizing data that could identify specific machining operations or workpieces. By storing only aggregated stress parameter frequencies rather than complete operational data, the system maintains necessary information for maintenance purposes while protecting user privacy and data security.
Solution Approach 2:
The patent applies different data treatment qualities to different types of information. Critical stress parameters that affect machine tool wear and maintenance are preserved in detail, while operational details that could compromise security or privacy are aggregated or anonymized. This selective approach ensures information completeness for maintenance needs while minimizing security risks.
3Quantity of substance
If theoretical models are used to calculate loads and consumption, then memory requirements are reduced, but the accuracy of representing real conditions deteriorates
Solution Approach 1:
The patent transforms raw operating parameters into derived stress parameters through calculation (e.g., converting motor power and speed into torque, or combining multiple sensor readings into composite stress indices). This parameter transformation maintains the physical meaning and accuracy of load representation while reducing data volume, as the calculated stress parameters directly reflect actual machine tool conditions without requiring extensive raw data storage.
Data Source
AI summary
A method of manufacturing includes machining a workpiece with a machine tool. A plurality of size classes are predefined for at least one stress parameter of the machine tool. The method includes, during the machining: A) measuring at least one operating parameter of the machine tool; B) calculating the at least one stress parameter from the at least one measured operating parameter; and C) storing a number of times the at least one stress parameter is within each of the size classes.