Accelerating the thermoplastics welding process using multi-source machine learning

A processor system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for multi-source machine learning modeling framework for process property mapping of thermoplastic composite manufacturing, the set of instructions comprising:

• an instruction to select a surrogate machine learning model from a suite of machine learning frameworks;
• an instruction to involve uncertainty quantification associated with predictions which provide a quantified estimate of how much the multi-source machine learning model can be trusted;
• an instruction to provide multi-physics process model output to the machine learning model;
• an instruction to provide heterogeneous data sources for use by the machine learning model;
• an instruction to determine estimates of optimal process parameters employing budget-constrained multi-fidelity process optimization;
• an instruction for deployment of the multi-source machine learning model in the implementation of carbon fiber reinforced thermoplastic polymer induction welding;
• and
• an instruction to perform induction welding via machine learning informed process parameter recipes.