Injection Molding Time-Series Correlation for Defect Analysis
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Solution Overview
Problem
Existing injection molding support devices fail to effectively improve molding defects by merely referring to a forming defect countermeasure matrix, necessitating a more comprehensive analysis of the causes of such defects.
Innovation Solution
A molding management apparatus that connects to a molding apparatus, allowing for the selection of objective variables, calculates correlations between time series data and explanatory variables, and displays these data sets along a common time axis to facilitate easy analysis of molding defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a molding defect countermeasure matrix is used to analyze molding defects, then a systematic approach to defect analysis is provided, but the analysis process becomes complex and time-consuming
Solution Approach 1:
The patent segments the analysis process by dividing multiple molding parameters into distinct explanatory variables that can be independently monitored and analyzed. Each parameter (injection pressure, temperature, timing) is separated into its own time series data set, allowing operators to analyze individual parameter correlations with defects rather than dealing with a monolithic complex analysis system.
Solution Approach 2:
The patent creates visual copies of time series data through graphical displays that replicate the temporal relationships between parameters and defects. By displaying multiple time series data sets alongside defect occurrence timing on a common time axis, the system provides a visual copy of the underlying data that makes correlation analysis intuitive and reduces the perceived complexity of the analysis process.
2Measurement precision
If multiple parameters are monitored to identify defect causes, then analysis accuracy improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and pre-processing time series data for multiple parameters during normal production. The data acquisition section continuously gathers injection pressure, temperature, timing, and other parameter data, and the storage section preserves this data for future analysis. This preliminary data preparation eliminates the need for time-consuming data collection during actual defect analysis, as all necessary data is already prepared and stored.
Solution Approach 2:
The display section creates visual copies of multiple time series data sets that can be simultaneously viewed against defect occurrence timing. This visual representation allows operators to quickly assess correlations between parameters and defects without manually analyzing raw data, significantly reducing analysis time while maintaining high accuracy through comprehensive parameter monitoring.
3Measurement precision
If comprehensive time series data analysis is performed, then defect cause analysis accuracy improves, but the ease of operation decreases
Solution Approach 1:
The patent creates visual copies of complex time series data through graphical displays that show parameter variations alongside defect occurrence timing on a common time axis. This visual representation transforms abstract numerical data into intuitive graphical patterns that operators can easily interpret, maintaining high analysis accuracy while dramatically improving ease of operation.
Solution Approach 2:
The patent replaces manual data analysis mechanisms with automated computational mechanisms. The control section automatically calculates correlation coefficients between multiple parameters and defect timing, and the display section automatically generates visual representations. This substitution of automated information processing for manual analysis maintains comprehensive analytical capability while making the system easy to operate through simple defect reporting and automatic visualization.
Data Source
AI summary
A molding management apparatus includes an operation section as a selection section that selects an objective variable, a correlation calculation section that calculates a correlation between time series data corresponding to an objective variable and a plurality of time series data sets corresponding to explanatory variables describing the objective variable, and a display section for displaying a plurality of time series data sets corresponding to the explanatory variable in a display mode based on correlation, and displaying the time series data corresponding to the objective variable and the plurality of time series data sets corresponding to the explanatory variable displayed in a display mode based on correlation along a common time axis.