The present disclosure discloses an Industrial Internet of Things (IIoT) system for automatic control of production line manufacturing parameters, which comprises a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in turn. The service platform adopts centralized layout, and the management platform and the sensor network platform adopt independent layout. The present disclosure also discloses a control method of the IIoT for automatic control of production line manufacturing parameters. The present disclosure builds the IIoT based on the five platform structure, in which the sensor network platform and the management platform are arranged independently, and each corresponding platform includes a plurality of independent sub-platforms, so that the independent sensor network platform and the management platform can be used for each production line device to form an independent data processing channel and transmission channel, and reduce the data processing capacity and transmission capacity of each platform.

A method for correcting tool parameters of a machine tool for machining workpieces includes recording measurement values of measured characteristics as actual values of at least one workpiece machined with the machine tool. The measurement values are compared with the default set values of the workpiece. The measurement values of at least two measured characteristics are recorded from at least two parameters of at least one measured characteristic and/or from at least one measured characteristic and from at least one parameter. An average for a tool correction value is calculated from the measurement values and the corresponding set values, with which a correction of the machine tool is performed.

A system for estimating an abnormality includes an industrial device that controls one or more jigs such that the one or more jigs press an object to perform a work process, and processing circuitry that acquires operation data that is related to an operation of the industrial device and is measured at multiple time points after the object is pressed by the one or more jigs, and perform an estimation estimating an abnormality based on the operation data acquired.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

A quality determination device includes an acquisition unit for acquiring a drive current during tapping, from a motor provided in a machine tool that performs tapping, and a determination unit for determining acceptance or rejection of a screw hole formed by the tapping, based on the drive current, an electric power of the motor obtained from the drive current, or a torque of the motor obtained from the drive current.

An abnormality monitoring method includes obtaining multiple target machine process parameters that affect a measurement value of a preset product at a first measurement point of the preset product, constructing a measurement value prediction model corresponding to the first measurement point, calculating a degree of fit of the measurement value prediction model, aggregating the degree of fit of the measurement value prediction model, the estimated value of the first measurement point, the target machine process parameters corresponding to the first measurement point, and the parameter coefficients of the target machine process parameters, repeating the above steps for each of multiple measurement points, calculating an influence degree index value of each machine, process parameter, and outputting warning information of machine process parameters that exceed a first preset influence degree index value.

The optimization support device includes a first conversion unit that converts an operating condition parameter indicating an operating condition of a process for producing a product into a state parameter indicating a state of the process, and a second conversion unit that converts the state parameter into a quality parameter indicating a quality of the product.

A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.

A prediction model creation apparatus includes a feature amount acquisition unit that acquires values of types of feature amounts that are calculated from operating state data indicating an operating state of a production facility that produces a product, for both a normal time at which the production facility produces the product normally and a defective time at which a defect occurs in the product that is produced, a feature amount selection unit that selects a feature amount effective in predicting the defect from among the acquired types of feature amounts, based on a predetermined algorithm that specifies a degree of association between the defect and the types of feature amounts, from the values of the types of feature amounts acquired at the normal time and the defective time, and a prediction model construction unit that constructs a prediction model for predicting occurrence of the defect, using the selected feature amount.

A production system for producing products from raw materials by a production process with several steps has a number of production facilities that perform the steps and a control device. The control device determines a control target value by referring to information about group combinations specified in accordance with the relative merits of the manufacturing condition routes followed by respective lots during the production process. The relative merits are determined on the basis of quality items of the lots, classified for inter-step combinations of groups, which are classified on the basis of manufacturing conditions at the steps.