Methods for monitoring fabrication of workpieces, analytical computing devices and methods for training neural networks to analyze robot-assisted drilling operations

An analytical computing device, comprising:

• a network interface in operative communication with a communication network;
• at least one processor and associated memory;
• the at least one processor in operative communication with a robotic manipulator via the network interface and the communication network;
• at least one application program storage device storing a drilling operation analysis application program;
• a neural network trained to analyze a data set associated with a robot-assisted drilling operation;
• and
• at least one data storage device configured to at least temporarily store the data set associated with the robot-assisted drilling operation;
• wherein the at least one processor and the network interface are configured to receive a thrust force signal from the robotic manipulator via the communication network during the robot-assisted drilling operation on a workpiece, the workpiece comprising a first material layer and a second material layer, the robot-assisted drilling operation comprising drilling a hole for a fastener through the first material layer and then through the second material layer;
• wherein the at least one processor and the associated memory, in conjunction with the at least one processor running the drilling operation analysis application program, are configured to filter noise data points and outlier data points from the thrust force signal to form a filtered thrust force signal, extract one or more thrust feature from the filtered thrust force signal and provide the one or more thrust feature to the neural network;
• and
• wherein the neural network is configured to process the one or more thrust feature, perform a regression analysis on the one or more thrust feature, predict a stiffness value for at least one of the first material layer and the second material layer of the workpiece based at least in part on the regression analysis and at least temporarily store the stiffness value in the at least one data storage device.

A method for training a neural network to analyze a robot-assisted drilling operation, comprising:

• receiving a plurality of thrust force signal data sets from a training data repository, each thrust force signal data set based on a thrust force signal generated at a robotic manipulator during a corresponding robot-assisted drilling operation on a workpiece, the workpiece comprising a first material layer and a second material layer, the robot-assisted drilling operation comprising drilling a hole for a fastener through the first material layer and then through the second material layer;
• filtering noise data points and filtering outlier data points from each thrust force signal data set to form a plurality of filtered thrust force signal data sets;
• extracting one or more thrust feature from each filtered thrust force signal data set to obtain a plurality of thrust feature data sets, each thrust feature data set comprising the one or more thrust feature;
• providing the plurality of thrust feature data sets to the neural network;
• and
• training the neural network to predict a stiffness value for at least one of the first material layer and the second material layer of the workpiece based on a regression analysis of the one or more thrust feature of the plurality of thrust feature data sets.