Neural Network Spray Parameter Optimization

The patent creates digital copies (synthetic images) of spray-coating patterns that mimic real physical measurements. These synthetic images are generated from operational parameters through a forward model, allowing the neural network to learn from大量 simulated data without requiring equivalent physical testing. This copying approach enables the system to achieve high measurement precision for parameter optimization while avoiding the time-consuming trial and error process with actual spray-coating operations and testing parts

The patent performs preliminary action by pre-training the neural network on a large dataset of synthetic spray-coating patterns generated before actual use. The forward model pre-computes the relationship between operational parameters and spray patterns, creating a comprehensive training dataset in advance. When deployed, the system can immediately provide optimized parameters without requiring time-consuming physical trials, as the learning is already completed in the preliminary training phase

The patent uses synthetic image copying to represent diverse spray-coating scenarios without needing physical testing parts for each scenario. The forward model generates synthetic patterns that cover a wide range of operational parameters and spray conditions, allowing the neural network to learn comprehensive parameter relationships. This approach achieves high adaptability and versatility in parameter optimization while eliminating the need for large quantities of physical testing parts

The patent systematically varies operational parameters in the synthetic data generation process to cover comprehensive parameter spaces. The forward model allows efficient exploration of multiple parameter combinations (paint flow, atomization air, shaping air, rotation speed, distance from surface) by simply changing input parameters rather than physically reconfiguring equipment for each test. This enables comprehensive parameter coverage with virtual experiments instead of physical testing parts

The patent implements feedback by using the neural network to analyze spray-coating pattern images and generate optimized operational parameters. The system takes digital representations of spray patterns as input, processes them through the trained network, and outputs refined parameter recommendations. This closed-loop feedback mechanism transforms previously unused digital data into actionable parameter optimization, improving both data utilization and parameter provision efficiency

The patent replaces manual parameter adjustment and analysis with an automated neural network system. Instead of operators manually analyzing spray pattern images and adjusting parameters, the deep learning model automatically processes digital pattern data and generates optimized parameters. This substitution of mechanical/manual processes with intelligent automation enables effective utilization of digital data and dramatically improves productivity in parameter provision