A computer implemented method for the maintenance optimization of a fleet or group of turbomachinery assets is disclosed. The method comprises the step of model training and setup, aiming at setting configurations parameters, that can be executed offline, and the step of online calculation on new input data, which is based on detected data and extracted statistical features. An anomaly identification and classification follow, thus calculating a risk assessment, for estimating the risk that an anomaly might cause any event that requires a maintenance task to be executed on one or more assets of the fleet.

A system for manufacturing a composite structure includes a tool with a surface for supporting component elements of the composite structure. The surface is divided into a multi-dimensional array of thermal zones for in-process control of the temperature of a component element (e.g. resin) of the composite structure. The sensors sense a characteristic of the component element and provide sensor data, which is applied to a machine-learning algorithm configured to generate control data to achieve a defined quality goal. A controller then independently controls the thermal zones to locally heat, cool or maintain the temperature of the component element according to the control data to advance the component element or composite structure to the defined quality goal. This may be performed over a plurality of instances during which the machine-learning algorithm learns to increase advancement of the component element or composite structure to the defined quality goal.

A method and system for automating CNC manufacturing is provided, comprising: receiving, at a server over a network, metadata from a plurality of CNC machines, the metadata from each CNC machine being automatically generated by a CNC control of the CNC machine as a result of an operator loading a CAD file of a first part to be formed by the CNC machine into CAM software of the CNC control and using the CAM software to define manufacturing process parameters and tool path parameters for forming the first part; training, by the server, a supervised machine learning model using the metadata as labeled training data to produce a trained model; and transmitting, by the server to at least one CNC machine of the plurality of CNC machines, model generated manufacturing process parameters and tool path parameters generated by the trained model for forming a second part.

A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.