Injection Molding State Data Classification for Long-Term Trend Monitoring
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Solution Overview
Problem
Injection molding machines face challenges in efficiently recording and monitoring the changing trends of machine state data and singular values, such as unexpected errors, due to limited memory capacity and the loss of unique operational data during averaging processes, which hinders the investigation of malfunctions.
Innovation Solution
An injection molding information management support device with a control unit that acquires machine state data and converts it into class data values, recording frequency distributions in a storage unit, allowing for efficient recording and visualization of changing trends and singular values, reducing memory usage and enabling long-term data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all machine state data values for each molding cycle are recorded in detail, then the data completeness is improved, but the memory capacity is exceeded
Solution Approach 1:
The patent segments machine state data into multiple categories (normal operation data, abnormal operation data, singular values) and applies different recording strategies to each segment. Normal data is recorded as frequency distributions rather than individual values, while singular values are preserved individually. This segmentation resolves the contradiction by reducing overall data volume while maintaining essential information across different data types.
Solution Approach 2:
The patent transforms the parameter representation from individual data values to frequency distribution statistics (counts, averages, standard deviations). By changing how data is parameterized - from storing every individual measurement to storing aggregated statistical parameters - the system reduces memory requirements while preserving the ability to analyze machine state trends and detect abnormalities.
2Quantity of substance
If average values of machine state data are calculated to reduce data volume, then the memory usage is reduced, but the singular values and unique operational data are lost
Solution Approach 1:
The patent applies different data processing qualities to different portions of the data. For normal operation data, aggregated statistical quality (averages, standard deviations) is sufficient and appropriate. However, for abnormal and singular values, the patent preserves individual data point quality to maintain investigative capability. This local differentiation of data quality resolves the contradiction by applying averaging only where appropriate while protecting critical singular values.
Solution Approach 2:
The system dynamically determines which data to aggregate and which to preserve individually based on the operational state. During normal operation, data is aggregated for efficiency. When abnormalities or singular events occur, the system identifies and preserves these individual data points separately. This dynamic approach allows the system to optimize memory usage during normal operation while maintaining the ability to investigate anomalies.
3Measurement precision
If detailed machine state data is recorded for long-term monitoring, then the monitoring accuracy is improved, but the recording efficiency is reduced
Solution Approach 1:
The patent performs preliminary classification and aggregation of data during the acquisition phase rather than processing all raw data later. By pre-identifying singular values and abnormal operations and separating them from normal data, and by pre-calculating frequency distribution statistics, the system prepares data in an optimized format that maintains monitoring accuracy while dramatically improving recording efficiency and reducing subsequent processing requirements.
Data Source
AI summary
To easily check a changing trend of machine state data on an injection molding machine during a predetermined period and/or in every predetermined period. An injection molding information management support device for managing machine state data regarding a machine state in operation of an injection molding machine during a predetermined period and/or in every predetermined period includes a machine state data acquisition unit that acquires a machine state data value for each molding cycle in the injection molding machine, and a frequency distribution data recording unit that converts the machine state data value into a preset class data value and records the class data value in a storage unit.