System and method for designing curing processes using generative ai

A computer-implemented method for designing curing processes, the method comprising:

• obtaining design requirements using a requirement gathering agent;
• determining potential ranges of recipes and operating conditions with an operations specialist agent based on the obtained design requirements, material options, and design options;
• identifying suitable material options and assessing corresponding properties with a material specialist agent to meet design objectives;
• defining dimensions and shape aspects of the design using a design specialist agent based on the gathered requirements, material options, and operating conditions;
• extracting and reasoning relevant information specific to design, operation, and materials with a knowledge processing and retrieval agent;
• formulating a final requirement specification with an experiment enabler agent;
• generating a first set of experiments based on the final requirement specifications using an experiment designer;
• performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions;
• updating, by a predictive model optimizer agent implementing a continual learning framework associated with the prediction model design agent, the prediction model in real-time;
• and
• optimizing and updating, by a process optimizer agent associated with the predictive model optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.

A non-transitory computer-readable storage medium having stored thereon computer executable instruction which when executed by one or more processors, cause the one or more processors to carry out operations for designing curing processes, the operations comprising perform the operations comprising:

• obtaining design requirements using a requirement gathering agent;
• determining potential ranges of recipes and operating conditions with an operations specialist agent based on the obtained design requirements, material options, and design options;
• identifying suitable material options and assessing corresponding properties with a material specialist agent to meet design objectives;
• defining dimensions and shape aspects of the design using a design specialist agent based on the gathered requirements, material options, and operating conditions;
• extracting and reasoning relevant information specific to design, operation, and materials with a knowledge processing and retrieval agent;
• formulating a final requirement specification with an experiment enabler agent;
• generating a first set of experiments based on the final requirement specifications using an experiment designer;
• performing, by a predictive model design agent implementing a prediction model, real-time predictions for dynamic conditions;
• updating, by a predictive model optimizer agent implementing a continual learning framework associated with the prediction model design agent, the prediction model in real-time;
• and
• optimizing and updating, by a process optimizer agent associated with the predictive model optimizer agent, the prediction model iteratively based on feedback from experiments and simulations.