VERNICE: Neural Network for Paint Mixing
Machine learning system to predict paint formulas from specific colors in RGB format
A paint manufacturing company had 20,000 formulas in their database. Each paint uses 13 different mixtures to achieve a specific color. When a customer requests a particular RGB color, finding or creating the right formula requires time and expensive testing.
We developed a neural network-based system that predicts paint formulas starting from the desired color. The network is trained on the entire historical database and suggests the exact proportions of the 13 required mixtures.
The system is supported by an interactive dashboard that communicates with a backend based on serverless asynchronous queue management, using AWS SQS and AWS Lambda, to ensure a fluid and responsive experience.
Events on the SQS bus trigger the invocation of an asynchronous worker (AWS Lambda) that executes the artificial intelligence script and updates a database with the status and details of each executed task.
The historical database becomes predictive intelligence that accelerates formulation, reduces errors, and cuts the costs of physical mixing tests. Because even paints deserve a brain.
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