Manufacturing Condition Optimization Using Divided-Product Quality Prediction

The patent applies universality by designing a machine learning model that can handle multiple product types through standardized preprocessing. The system divides different products into comparable segments and uses unified processing procedures, allowing the same model architecture to serve multiple functions across different product types while maintaining prediction accuracy for each specific product

The patent employs parameter changes by adjusting the division granularity and processing parameters based on product characteristics. The system dynamically modifies segmentation parameters and processing depth to adapt to different product types, enabling the model to maintain high prediction accuracy across diverse products without requiring separate specialized models for each product type

The patent applies preliminary action by performing automated preprocessing including division of product data, extraction of relevant features, and initial parameter optimization before the actual prediction task. This preliminary preparation work automates what would otherwise require manual trial and error, reducing both time and cost while keeping the core model relatively simple

The patent replaces the mechanical trial-and-error process with an automated computational system that performs preprocessing, feature extraction, and optimization algorithms. This substitution eliminates the need for physical prototyping and manual testing, dramatically reducing work and cost while maintaining model simplicity through automated rather than complex manual procedures