Machine Learning Winding Condition Generator for Web Tension Control

The patent applies parameter changes by using machine learning to determine optimal winding tension values based on multiple factors including web properties, winding conditions, and target quality requirements. Instead of using fixed high tension to prevent misalignment, the system dynamically adjusts tension parameters to achieve the minimum necessary tension for quality winding, thereby preventing both misalignment and damage.

The patent replaces the traditional mechanical approach of theoretically calculating winding tension with a machine learning-based system. The learning model processes web parameters and winding conditions to predict optimal tension values, substituting theoretical mechanical models with data-driven predictions that better reflect actual winding behavior and achieve quality results with lower, safer tensions.

The machine learning model dynamically adjusts winding tension parameters based on specific web properties and winding conditions. For each winding scenario, the system determines the precise tension level needed to prevent misalignment without excessive tension that causes damage, optimizing the balance between these two quality aspects through learned patterns from training data.

The system uses feedback from training data consisting of actual winding results to continuously improve tension predictions. By learning from historical winding outcomes including both successful and problematic cases, the model refines its ability to predict optimal tension settings that prevent both misalignment and damage in various winding scenarios.

The patent replaces theoretical mechanical models with a machine learning-based prediction system. The learning model processes web parameters and winding conditions to directly output optimal tension values, eliminating the need for complex theoretical calculations while achieving higher accuracy by learning from actual winding data and patterns.

The system creates a digital copy of the winding process through machine learning models that replicate the relationship between input parameters and optimal tension settings. By training on historical winding data, the model learns to predict outcomes without physical trial-and-error, copying successful winding patterns into predictive algorithms.

The system performs preliminary action by pre-training machine learning models using historical winding data before actual production winding. The learning model accumulates knowledge from past winding outcomes, enabling it to predict optimal tension settings instantly for new winding scenarios without requiring actual trial-and-error testing on the winding apparatus.

The patent uses copying by creating virtual training data that replicates actual winding scenarios and outcomes. This digital copy of the winding process allows the system to learn optimal settings in a virtual environment, transferring this knowledge to real-world applications without repeated physical experimentation.