Rapid closed-loop control based on machine learning

A method performed by a computing device of generating a classifier to label data of a target data type, the data representing an activity, the method comprising:

• receiving bootstrap data and example data, the bootstrap data and the example data having a correlation in space or time, the bootstrap data and the example data representing the activity, the example data being of the target data type;
• receiving from a user a labeling of the bootstrap data to generate labeled bootstrap data;
• automatically labeling the example data based on the correlation to generate training data;
• and
• training the classifier using a machine learning technique based on the training data
• wherein the activity is a manufacturing process and the bootstrap data is snapshot data of the manufacturing process and the example data is video data of the manufacturing process.

A method performed by a computing device of generating a neural network classifier to label data of a target data type, the data representing an activity, the method comprising:

• receiving bootstrap data and example data, the bootstrap data and the example data having a correlation in space or time, the bootstrap data and the example data representing the activity, the example data being of the target data type;
• receiving from a user a labeling of the bootstrap data to generate labeled bootstrap data;
• automatically labeling the example data based on the correlation to generate training data;
• and
• training the neural network classifier based on the training data
• wherein the activity is a manufacturing process and the bootstrap data is from a first stream of sensor data of the manufacturing process and the example data is from a second stream of sensor data of the manufacturing process.

A method performed by a computing device of generating a classifier to label data of a target data type, the method comprising:

• receiving seed data having a seed data type;
• receiving from a user a labeling of the seed data to generate labeled seed data;
• training a bootstrap classifier to label data of the seed data type using the labeled seed data;
• receiving bootstrap data and example data, the bootstrap data and example data representing different data streams, the bootstrap data being of the seed data type and the example data being of the target data type;
• applying the bootstrap classifier to label the bootstrap data
• automatically labeling the example data based on the labeled bootstrap data to generate training data;
• and
• training a production classifier to label data of the target data type using the training data.

A method performed by a computing device of generating a classifier to label data of a target data type, the method comprising:

• receiving seed data having a seed data type;
• receiving from a user a labeling of the seed data to generate labeled seed data;
• training a bootstrap classifier to label data of the seed data type by applying a machine leaning technique with the labeled seed data as input;
• receiving bootstrap data and example data, the bootstrap data and example data representing different data streams, the bootstrap data being of the seed data type and the example data being of the target data type;
• applying the bootstrap classifier to label the bootstrap data to generate training data;
• and
• training a production classifier to label data of the target data type by applying a machine learning technique with training data as input
• wherein the bootstrap data is snapshot data of a manufacturing process and the example data is video data of the manufacturing process.

A method performed by a computing device of generating a classifier to label data of a target data type, the method comprising:

• receiving seed data having a seed data type;
• receiving from a user a labeling of the seed data to generate labeled seed data;
• training a bootstrap classifier to label data of the seed data type using the labeled seed data;
• receiving bootstrap data and example data, the bootstrap data and example data representing different data streams, the bootstrap data being of the seed data type and the example data being of the target data type;
• applying the bootstrap classifier to label the bootstrap data to generate training data;
• training a neural network as a production classifier to label data of the target data type using the training data;
• and
• applying the production classifier to label newly collected example data during the activity and providing instructions to alter the activity in real time
• wherein the bootstrap data is from a first stream of sensor data of the activity and the example data is from a second stream of sensor data of the activity.

A method performed by a computing device of generating a classifier to label data of a target data type, the method comprising:

• receiving seed data having a seed data type;
• receiving from a user a labeling of the seed data to generate labeled seed data;
• training a bootstrap classifier to label data of the seed data type using the labeled seed data;
• receiving bootstrap data and example data, the bootstrap data and example data representing different data streams, the bootstrap data being of the seed data type and the example data being of the target data type;
• applying the bootstrap classifier to label the bootstrap data to generate training data;
• training a production classifier to label data of the target data type using the training data;
• and
• applying the production classifier to label newly collected example data during the activity and providing instructions to alter the activity in real time
• wherein the bootstrap classifier and the production classifier are selected from a group consisting of a support vector machine, a decision tree, a k-nearest neighbor algorithm, a linear regression algorithm, a relevance vector machine, and ensembles of classifiers
• wherein the bootstrap data is from a first stream of sensor data of the activity and the example data is from a second stream of sensor data of the activity and
• wherein a label indicates whether a characteristic of an output of the activity is acceptable or not acceptable.

One or more computing devices for generating a classifier to control an additive manufacturing process, a computing device comprising:

• one or more memories storing computer-executable instructions for controlling a computing device, the instructions including:
• a production classifier to label production data with a label, the production classifier being trained using labeled example data as training data, the labeled example data being automatically labeled based on labeled bootstrap data, the bootstrap data and the example data collected from sensors of an additive manufacturing process, the example data being of a target data type;
• and
• a component that
• receives production data from sensors during an additive manufacturing process, the production data being of the target data type;
• applies the production classifier to label the production data;
• and
• controls in real time the additive manufacturing process based on the labels;
• and
• one or more processors for executing the instructions stored in the one or more memories.

One or more computing systems for controlling production equipment during an additive manufacturing process, the one or more computing systems comprising:

• one or more computer-readable storage mediums storing computer-executable instructions for controlling the one or more computing system to:
• access a production classifier to label production data with a label, the production classifier being trained using machine learning with labeled example data as training data, the labeled example data being automatically labeled based on labeled bootstrap data, the bootstrap data and the example data collected from sensors of an additive manufacturing process, the example data being of a target data type;
• and
• receive production data from sensors of the production equipment during an additive manufacturing process, the production data being of the target data type;
• apply the production classifier to label the production data;
• and
• control in real time the production equipment during the additive manufacturing process based on the labels;
• and
• one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.

One or more computing systems for training a neural network for use in controlling production equipment during manufacturing process, the one or more computing systems comprising:

• one or more computer-readable storage mediums storing computer-executable instructions for controlling the one or more computing system to:
• access bootstrap data and example data, the bootstrap data and the example data having a correlation in space or time, the bootstrap data and the example data representing the activity, the example data being of the target data type, the bootstrap data and the example data collected from production equipment during a manufacturing process;
• receive from a user a labeling of the bootstrap data to generate labeled bootstrap data;
• automatically label the example data based on the correlation to generate training data;
• and
• train the neural network based on the training data;
• and
• one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.

One or more computing systems for identifying whether corrective action is needed when manufacturing a product, the one or more computing systems comprising:

• one or more computer-readable storage mediums storing computer-executable instructions for controlling the one or more computing system to:
• access a production classifier for labelling production data to indicate whether corrective action is needed, the production classifier being trained using labeled example data as training data, the labeled example data being automatically labeled based on labeled bootstrap data;
• and
• receive production data from sensors during the manufacturing of the product;
• and
• apply the production classifier to label the production data to indicate whether corrective action is needed;
• and
• one or more processors for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.