Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

Abstract: A method includes receiving raw data and generating a manufacturing data packet (MDP) that includes at least a portion of the raw data. Generating the MDP includes associating metadata with the raw data and associating a timestamp with the raw data. The timestamp is synchronized to a common reference time. A data model associated with the MDP is obtained. The data model includes one or more predefined data types and one or more predefined data fields. A first data type from the one or more predefined data types is determined based at least in part on characteristics of the raw data. An algorithm is determined based at least in part on the first data type. The MDP is processed according to the algorithm to produce an output. The first data type is associated with the raw data. The output is associated with a data field of the first data type.

Abstract: A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

Abstract: A computer-implemented method for designating a portion of a machine-vision analysis to be performed on a worker. A set of machine-vision algorithms is obtained for analyzing a digital image of a product. An overall time estimate is determined that represents the processing time to analyze the digital image using the entire set of machine-vision algorithms. If the overall time estimate is greater than a threshold value, then an algorithm time estimate for each of two or more algorithms of the set of machine-vision algorithms is obtained. A rank associated with each of the two or more algorithms is computed based on the algorithm time estimates. A designated algorithm to be performed on the worker is selected based on the rank associated with each of the two or more algorithms. The digital image may then be analyzed on the worker using the designated algorithm.

Abstract: A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A computer-implemented method for designating a portion of a machine-vision analysis to be performed on a worker. A set of machine-vision algorithms is obtained for analyzing a digital image of a product. An overall time estimate is determined that represents the processing time to analyze the digital image using the entire set of machine-vision algorithms. If the overall time estimate is greater than a threshold value, then an algorithm time estimate for each of two or more algorithms of the set of machine-vision algorithms is obtained. A rank associated with each of the two or more algorithms is computed based on the algorithm time estimates. A designated algorithm to be performed on the worker is selected based on the rank associated with each of the two or more algorithms. The digital image may then be analyzed on the worker using the designated algorithm.

Abstract: A machine-vision system for monitoring a quality metric for a product. The system includes a controller configured to receive a digital image from an image acquisition device. The controller is also configured to analyze the digital image using a first machine-vision algorithm to compute a measurement of the product. The system also includes a vision server connected to the controller, and configured to compute a quality metric and store the digital image and the measurement in a database storage. The system also includes a remote terminal connected to the vision server, and configured to display the digital image and the quality metric on the remote terminal.

Abstract: A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

Abstract: A computer-implemented method for designating a portion of a machine-vision analysis to be performed on a worker. A set of machine-vision algorithms is obtained for analyzing a digital image of a product. An overall time estimate is determined that represents the processing time to analyze the digital image using the entire set of machine-vision algorithms. If the overall time estimate is greater than a threshold value, then an algorithm time estimate for each of two or more algorithms of the set of machine-vision algorithms is obtained. A rank associated with each of the two or more algorithms is computed based on the algorithm time estimates. A designated algorithm to be performed on the worker is selected based on the rank associated with each of the two or more algorithms. The digital image may then be analyzed on the worker using the designated algorithm.

Abstract: A computer-implemented method for designating a portion of a machine-vision analysis to be performed on a worker. A set of machine-vision algorithms is obtained for analyzing a digital image of a product. An overall time estimate is determined that represents the processing time to analyze the digital image using the entire set of machine-vision algorithms. If the overall time estimate is greater than a threshold value, then an algorithm time estimate for each of two or more algorithms of the set of machine-vision algorithms is obtained. A rank associated with each of the two or more algorithms is computed based on the algorithm time estimates. A designated algorithm to be performed on the worker is selected based on the rank associated with each of the two or more algorithms. The digital image may then be analyzed on the worker using the designated algorithm.