Computer modeling for detection of discontinuities and remedial actions in fastening systems

A system for training neural network to predict a likelihood of at least one discontinuity or anomaly being present within a joining created in a welding system, comprising:

• a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising:
• receiving a plurality of attributes corresponding to the joining;
• generating a training dataset based on the plurality of attributes corresponding to the joining and a plurality of attributes corresponding to previous joinings having at least one discontinuity or anomaly, the plurality of attributes corresponding to previous joinings comprising at least a voltage value, a current value, and a lift value for each previous joining, the lift value indicating a distance a weld stud is raised above a base material during the previous joining;
• training the neural network using the training dataset, the neural network trained to predict the likelihood of the at least one discontinuity or anomaly and respective likelihoods of potential causes of the at least one discontinuity or anomaly;
• executing the neural network using a plurality of second attributes corresponding to a second joining as input, the neural network generating one or more attributes associated with at least one second discontinuity or anomaly of the second joining, each of the one or more attributes identifying a respective likelihood of a respective physical condition of the welding system causing the at least one second discontinuity or anomaly of the second joining;
• and
• transmitting, to a joining machine, an instruction to modify at least one joining parameter of its welding based on the one or more attributes generated by the neural network.

A system for training neural network to predict a likelihood of at least one discontinuity or anomaly being present within a joining created in a welding system, comprising:

• a joining machine;
• and
• a processor in communication with the joining machine, the processor configured to:
• receive a plurality of attributes corresponding to the joining;
• generate a training dataset based on the plurality of attributes corresponding to the joining and a plurality of attributes corresponding to previous joinings having at least one discontinuity or anomaly, the plurality of attributes corresponding to previous joinings comprising at least a voltage value, a current value, and a lift value for each previous joining, the lift value indicating a distance a weld stud is raised above a base material during the previous joining;
• train the neural network using the training dataset, the neural network trained to predict the likelihood of the at least one discontinuity or anomaly and respective likelihoods of potential causes of the at least one discontinuity or anomaly;
• execute the neural network using a plurality of second attributes corresponding to a second joining as input, the neural network generating one or more attributes associated with at least one second discontinuity or anomaly of the second joining, each of the one or more attributes identifying a respective likelihood of a respective physical condition of the welding system causing the at least one second discontinuity or anomaly of the second joining;
• transmit, to the joining machine, an instruction to modify at least one joining parameter of its welding based on the one or more attributes generated by the neural network.