AI Injection Molding Control for Disturbance-Driven Quality Drift
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Solution Overview
Problem
Existing injection molding systems face challenges in maintaining consistent product quality due to disturbances such as changes in ambient temperature/humidity or material properties, leading to variations in molding conditions set by operators.
Innovation Solution
An AI-based injection molding system that utilizes a deep-learning-based molding quality maintenance model to acquire current injection state data, determine if molding quality is maintained, and automatically adjust molding conditions to match target data, ensuring consistent product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If operators manually adjust molding conditions to respond to disturbances, then product quality can be improved, but inconsistency in quality occurs due to different operator settings
Solution Approach 1:
The system continuously monitors injection state data (viscosity profile, injection pressure) and compares it with target values, automatically adjusting molding conditions based on the deviation detected through this feedback loop to maintain consistent product quality
Solution Approach 2:
The injection molding machine performs self-adjustment of molding conditions through an automated control system that responds to real-time injection state data, eliminating the need for manual operator intervention and ensuring consistent decision-making
2Manufacturing precision
If molding conditions are changed frequently to adapt to disturbances, then product quality is maintained, but system stability deteriorates
Solution Approach 1:
The system uses feedback control to make targeted, proportional adjustments only when deviation from target injection state data is detected, avoiding unnecessary changes and maintaining stability while ensuring quality
Solution Approach 2:
The molding conditions are made dynamically adjustable in response to real-time injection state data, allowing the system to adapt to disturbances while maintaining overall stability through controlled, data-driven changes
3Manufacturing precision
If deep-learning-based automatic control is implemented, then molding quality consistency is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual operator judgment and mechanical adjustment with an automated control system using deep-learning algorithms, substituting human expertise with computational intelligence to achieve consistent quality decisions
Solution Approach 2:
The system performs self-learning and self-adjustment through deep-learning algorithms that automatically analyze injection state data and determine optimal molding conditions without external intervention, improving quality while managing complexity through automation
Data Source
AI summary
An artificial intelligence-based injection molding system in which molding conditions can be changed to manufacture fair-quality products when defective products are manufactured because of disturbances during the injection molding including an injection molding machine which performs injection molding by injecting a molding material into a mold; an injection state data acquisition unit for acquiring, during injection molding, current injection state data that includes the viscosity profile of the molding material injected into the mold and/or the injection pressure value thereof; a determination unit, which inputs the current injection state data into a molding quality maintenance model trained with predetermined target injection state data, so as to determine whether to maintain molding quality; and a molding condition setting unit for changing a preset molding condition so that the current injection state data follows the target injection state data, when the determination unit determines not to maintain the molding quality.