ACCELERATING THE THERMOPLASTICS WELDING PROCESS USING MULTI-SOURCE MACHINE LEARNING

• Main Authors: Sarkar, Soumalya, Mondal, Sudeepta, Gangloff, John Joseph, Zhao, Wenping, Xing, Lei
• Format: Patent
• Language: English; French; German
• Published: 03.07.2024
• Subjects: AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; COUNTING; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PERFORMING OPERATIONS; PHYSICS; REGULATING; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDEDFOR; SHAPING OR JOINING OF PLASTICS; TRANSPORTING; WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE, IN GENERAL

A system having a set of instructions executable by the system 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 networks; an instruction to involve uncertainty quantification associated with predictions which provide a quantified estimate of how much the 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 the multi-source machine learning model in the implementation of carbon fiber reinforced thermoplastic polymer induction welding; and an instruction to perform induction welding with an optimized recipe.