The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

A CPU unit is a life prediction device for a fan. The CPU unit includes a temperature calculation unit to calculate internal temperature of the CPU unit on the basis of at least one of the utilization of the CPU unit, the temperature a CPU, and the rotation speed of the fan; and a storage unit to store fan life data that indicates the life of the fan relative to temperature. A control unit includes a life prediction unit to calculate remaining life expectancy of the fan on the basis of the fan life data and the internal temperature calculated by the temperature calculation unit.

According to an example, a computing apparatus may include a processing device and a machine readable storage medium on which is stored instructions that when executed by the processing device, cause the processing device to access, from a sensing device, information pertaining to formation of a part of a 3D object in a layer of build materials upon which fusing agent droplets have been or are to be selectively deposited. The instructions may also cause the processing device to predict, based upon the accessed information, a quality of the part and output an indication of the predicted quality of the part.

A system and method of electric arc welding that includes a welding apparatus having an electric arc welder torch with sensors to determine the absolute position of the torch tip and the relative position of the torch tip to the weld joint during automatic welding. Combining absolute and relative positional data can be used to adjust the path of the robot during automated or robotic welding in response to variations in the weld joint.

A robotic electric arc welding system includes a welding torch, a welding robot configured to manipulate the welding torch during a welding operation, a robot controller operatively connected to the welding robot to control weaving movements of the welding torch along a weld seam and at a weave frequency and weave period, and a welding power supply operatively connected to the welding torch to control a welding waveform, and operatively connected to the robot controller for communication therewith. The welding power supply is configured to sample a plurality of weld parameters during a sampling period of the welding operation and form an analysis packet, and process the analysis packet to generate a weld quality score, wherein the welding power supply obtains the weave frequency or the weave period and automatically adjusts the sampling period for forming the analysis packet based on the weave frequency or the weave period.

Embodiments of systems and methods for supporting weld quality across a manufacturing environment are disclosed. One embodiment includes manufacturing cells within a manufacturing environment, where each manufacturing cell includes a cell controller and welding equipment. A communication network supports data communications between a central controller and the cell controller of each of the manufacturing cells. The central controller collects actual weld parameter data from the cell controller of each manufacturing cell, via the communication network, to form aggregated weld parameter data for a same type of workpiece being welded in each of the manufacturing cells. The central controller analyzes the aggregated weld parameter data to generate updated weld settings. The updated weld settings are communicated from the central controller to the cell controller of each of the manufacturing cells via the communication network.

Embodiments of systems and methods for supporting predictive and preventative maintenance are disclosed. One embodiment includes manufacturing cells within a manufacturing environment, where each manufacturing cell includes a cell controller and welding equipment, cutting equipment, and/or additive manufacturing equipment. A communication network supports data communications between a central controller and the cell controller of each of the manufacturing cells. The central controller collects cell data from the cell controller of each of the manufacturing cells, via the communication network. The cell data is related to the operation, performance, and/or servicing of a same component type of each of the manufacturing cells to form a set of aggregated cell data for the component type. The central controller also analyzes the set of aggregated cell data to generate a predictive model related to future maintenance of the component type.

Statistically significant event patterns predict the timing for performing entity maintenance. Event patterns are determined based on a target variable having an undesired value for a given entity when the event pattern occurs. Event patterns are filtered based on distributions of the event patterns across multiple entities and distributions of event patterns during desired operation of the entities and undesired operation of the entities. A predictive maintenance process is established having significant event patterns as the basis for maintenance tasks.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

A system, method, and computer-readable medium are disclosed for analysis and characterization of manufacturing information such as process trees or genealogies using graph theory. More specifically, using graph theory to analyze manufacturing information of a manufacturing operation allows for deep analysis of relationships between batches or units in a process tree and their closeness or distance, to identify clusters associated with specific quality characteristics or problems, to identify common antecedents of specifically labeled batches (e.g., problem batches), and/or to detect overall desirable or undesirable characteristics of the process tree (e.g., centrality, etc.).