System and method for recommending a recipe in a manufacturing process

A computer-implemented method for recommending a recipe to produce a product in a manufacturing process, the computer-implemented method comprising:

• obtaining, by one or more hardware processors, experimental data from a machine, wherein the experimental data comprise at least one of:
• recipe data, sensor data, and metadata of the machine, wherein the recipe data comprise a plurality of parameters that are set for the machine to produce the product, wherein the metadata comprise a label corresponding to at least one of:
• a defective or a non-defective part of the machine, a geometry, a location of the one or more sensors, a product type, information related to maintenance, environmental parameters, information related to replacing the part of the machine and information related to the machine, and a machine part;
• generating, by the one or more hardware processors, a physics-based-simulation model based on the experimental data obtained from the machine, wherein generating the physics-based-simulation model comprises:
• extracting, by the one or more hardware processors, simulation data from one or more sensors that are installed in the machine;
• determining, by the one or more hardware processors, an error between the extracted simulation data and experimental sensor data;
• determining, by the one or more hardware processors, whether the error exceeds a threshold value;
• tuning, by the one or more hardware processors, the physics-based-simulation model iteratively by adjusting a value of a physical range of each parameter when the error exceeds the threshold value;
• generating, by the one or more hardware processors, synthetic data for a first plurality of recipes using the physics-based-simulation model, wherein the synthetic data comprise synthetic data similar to the experimental data and are utilized only when the error is within the threshold value, and wherein the first plurality of recipes is created based on physical ranges of each parameter from the plurality of parameters;
• training, by the one or more hardware processors, an artificial intelligence (AI) model using the experimental data and the synthetic data;
• determining, by the one or more hardware processors, an optimized physical range of each parameter from the physical ranges of each parameter by analyzing the experimental data and the synthetic data using the trained AI model;
• determining, by the one or more hardware processors, whether the optimized physical range of each parameter creating a second plurality of recipes is valid based on process knowledge, wherein the process knowledge comprises at least one of:
• knowledge of the manufacturing process and corresponding machines, one or more sensors, parameters set to the machine, parameters measured through the one or more sensors, and limits of the plurality of parameters;
• generating, by the one or more hardware processors, the second plurality of recipes when the optimized physical range of each parameter creating the second plurality of recipes is valid;
• validating, by the one or more hardware processors, the second plurality of recipes to extract an optimized recipe using the physics-based-simulation model;
• and
• recommending, by the one or more hardware processors, the optimized recipe for producing the product in the machine.