Molding Machine Movable-Part Monitoring with Adaptive Abnormality Thresholds
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Solution Overview
Problem
Existing methods struggle with setting appropriate threshold values for determining operation abnormalities in high-speed molding processes, leading to increased risk of mold or machine damage and production stops, as it heavily depends on operator skill and varies with molding conditions.
Innovation Solution
An operation abnormality detection method and device that calculates first and second threshold values based on statistical averages and standard deviations, using equations (1) and (2) to easily set thresholds for detecting abnormalities in movable parts of molding machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-speed molding is implemented to increase productivity, then productivity is improved, but the risk of damage to mold or machine increases remarkably
Solution Approach 1:
The system continuously monitors operation parameters of movable parts during high-speed molding cycles and provides real-time feedback. When parameters deviate from normal ranges, the system generates warnings or alerts operators, enabling timely intervention to prevent mold or machine damage while maintaining high-speed operation
Solution Approach 2:
The system performs preliminary detection and warning before actual damage occurs. By monitoring operation parameters and comparing them against threshold values, the system identifies abnormal conditions early in the molding process, allowing preventive actions to be taken before catastrophic failures happen
2Measurement precision
If threshold values are set manually by operators to detect operation abnormalities, then detection accuracy may be improved, but the workload and difficulty of setting appropriate threshold values increases
Solution Approach 1:
The system automatically determines appropriate threshold values for abnormality detection without requiring manual operator input. It collects operation parameter data during normal molding cycles, analyzes the data distribution, and autonomously sets threshold values that adapt to different molding conditions and movable parts, eliminating the burden of manual threshold configuration
Solution Approach 2:
The system dynamically adjusts threshold values based on changing molding conditions and accumulated operational data. By monitoring parameter variations across different cycles and conditions, the system adapts threshold settings to maintain optimal detection accuracy without requiring manual reconfiguration for each molding scenario
Data Source
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AI summary
An operation abnormality detection method for detecting an operation abnormality in a molding device having a movable part includes: a step (S100) for acquiring a prescribed measured value relating to operation of the movable part and calculating, as statistical information, an average value of prescribed information values based on the prescribed measured values: a step (S102) for calculating a first threshold and a second threshold; a step (S108) for acquiring a current measured value and comparing a current information value based on the current measured value with the greater of the first threshold and the second threshold; and a step (S120) for issuing a warning if the current information value at least exceeds the greater of the first threshold and the second threshold.