A control system (1) according to the present invention comprises: a control device (100) that monitors the operation of a plurality of moving parts for machining a workpiece (155), and controls the operation of the plurality of moving parts in each control cycle by issuing command values to the plurality of moving parts; and an inspection device (200) for inspecting the workpiece (155). The control device (100) comprises: an identification unit (160) for identifying, based on inspection results of the inspection device (200) and the command values issued to the plurality of moving parts, which moving part from among the plurality of moving parts has caused an abnormality in the inspection results; and a storage unit (170) for collecting and storing data on the moving part that has been identified by the identification unit (160) and caused the abnormality in the inspection results.

System and methods are disclosed for improving manufacturing processes, improving manufactured products, and improving deployed devices. The devices can be reconfigurable holographic antennas (“antennas”). Manufacturing test results for a plurality of antennas is collected. Field performance data and antenna management information is collected for a plurality of deployed antennas. Query selection criteria, machine learning correlation criteria and a minimum correlation threshold are passed to a server to query for matching records from a data store and to perform machine learning on the query results to generate improvements to processes, manufactured antenna performance and deployed antenna performance.

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.

A data analyzing device, a data analyzing method, and a quality improvement system are provided. The data analyzing device includes a combination generating module, a ratio calculation module, and a weighting calculation module. The data analyzing device performs relevance analysis of to-be-analyzed data associated with a production operation procedure to generate relevance information. The ratio calculation module calculates a ratio parameter according to a total outlier product quantity corresponding to risky combinations in the relevance information and a total product quantity in the to-be-analyzed data. The weighting calculation module calculates a weighting parameter according to a number of the risky combinations in the relevance information and a total number of combinations of production control factors in the to-be-analyzed data. A production equipment selectively adjusts settings of the production operation procedure according to the ratio parameter and the weighting parameter.

A plant operating condition setting support system for supporting the setting of an operating condition of a plant that performs a process formed by devices includes: control devices that subject controlled devices to feedback control respectively; and an operating condition setting support device that provides integrated support for the setting of the control devices, which perform feedback control tasks respectively and independently. The operating condition setting support device includes: a measured value multiple acquisition unit that acquires measured values indicating states of the controlled devices, respectively; and a control device adjustment parameter determination unit that determines, based on the measured values acquired, control device adjustment parameters used by each of the control devices to determine manipulation variables for control that should be input to the controlled devices, according to a policy learned by deep reinforcement learning.

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.

Provided are a correlation determination unit to set, as a first apparatus, one apparatus among a plurality of apparatuses, set, as a first quality value, a quality value indicating a quality of an intermediate product processed by the first apparatus, set, as a second apparatus, an apparatus among the plurality of apparatuses that belongs to a process prior to the process to which the first apparatus belongs, set, as a second quality value, a quality value of an intermediate product processed by the second apparatus and determine whether correlation exists between the first quality value and the second quality value; a set determination unit to determine, using a quality standard value BQ indicating a standard of quality of the intermediate product, whether the first apparatus and the second apparatus are a set of apparatuses for which a first error between the first quality value and the quality standard value BQ and a second error between the second quality value and the quality standard value BQ cancel each other out when it is determined that correlation exists between the first quality value and the second quality value; and a process plan generation unit to generate a process plan using the set of apparatuses.

To provide a manufacturing process analysis method for specifying a hindering factor that causes a variation in product performance and for stabilizing product performance. A manufacturing process analysis method comprises: a step for collecting product data indicating the quality of a product and process data indicating manufacturing conditions of a product; a step for standardizing the process data so that the data are converted into an intermediate function; a step for performing principal component analysis on the intermediate function to derive a principal component load amount and a principal component score of the process data; a step for applying cluster analysis to the principal component score to classify manufacturing process lots into a plurality of groups; a step for determining relative merit of each group on the basis of product data soundness corresponding to the principal component score belonging to the group; and a step for specifying a hindering factor that contributes to the relative merit of the group.

Provided is a management system for performing quality management on manufacturing equipment. A management system that includes: an acquisition component that acquires state information for manufacturing equipment that is subject to management; a determination component that, on the basis of the acquired state information, determines whether an event has occurred; a display component that displays both an image that schematically or realistically represents a manufacturing line included in the manufacturing equipment and, separated into the four perspectives Machine, Man, Material, and Method, indexes that indicate the likelihood that there is a factor that caused an event; and an output component that, when a specific event has occurred, outputs, in response to a user selection, analysis results for each of one or more factors belonging to each of the four perspectives, the analysis results indicating a likelihood of having caused the specific event that occurred.

A method for compensating for the deflection of a milling cutter during the machining of a workpiece by a numerically controlled machine tool having a plurality of axes includes: executing a learning cut on a test workpiece having a known geometry by the milling cutter mounted on a tool spindle in a climb milling mode, and in doing so, ascertaining a correlation between a quantity that is proportional to the torque of the drive of the tool spindle and the deflection of the milling cutter normal to a surface of the test workpiece, the deflection being determined by comparing the actual contour of the test workpiece to a setpoint contour. This is followed by storing of the correlation for the milling cutter and machining of the workpiece by the milling cutter in a climb milling mode, while utilizing the stored correlation for compensating for the deflection of the milling cutter by applying a positional correction that is proportional to the quantity to a setpoint position of the axes of the machine tool.