A manufacturing management apparatus includes an operation information acquisition unit, a trace information acquisition unit, a storage unit, a manufacturing device determination unit, and a transfer destination indication unit. The storage unit stores inspection information on each article in association with the trace information. The manufacturing device determination unit creates combinations of the manufacturing devices that can perform manufacturing processes in consideration of operation information and trace information, determines a quality index from the inspection information for each of the created combinations, and determines, based on the quality index determined for each of the combinations, a combination of the manufacturing devices to be used. A transfer destination indication unit indicates a transfer destination of a part to a transfer device based on the determined combination of the manufacturing devices.

A machine tool management system includes a machine tool, a memory, a display, and a control circuit. The control circuit is configured to record, in the memory, a time point at which detection information with respect to a machine tool is detected and the detection information corresponding to the time point; determine selected detection information among the detection information to satisfy a determination condition; record, in the memory, the selected detection information corresponding to the time point; display a list of the selected detection information according to predetermined display items; extract from the memory at least part of the detection information including one of the selected detection information corresponding to one display item selected among the display items listed on the display; and to display a graph of a change over time in data included in the at least part of the detection information.

A system and method for autonomously tracing a cause of particle contamination during semiconductor manufacture is provided. A contamination analysis system analyzes tool process logs together with particle contamination data for multiple process runs to determine a relationship between systematic particle contamination levels and one or more tool parameters. This relationship is used to predict expected contamination levels associated with regular usage of the tool, and to identify which tool parameters have the largest impact on expected levels of particle contamination. The contamination analysis system also identifies process logs showing unexpected deviant particle contamination levels that exceed expected contamination levels, and traces the cause of the deviant particle contamination to particular process log parameter events.

Information representing an operating state of each of a plurality of robots, etc. operating under different operation conditions are retrieved and outputted. A retrieval system (1) includes; an acquisition means (24) for acquiring, from a plurality of machines (25) being used under respectively different operation conditions, information representing an operating state of each of the plurality of machines; an accumulation means (13) for accumulating information representing a plurality of operating states thus acquired; and a search means (12) for accepting search criteria for searching the information accumulated in the accumulation means (13), retrieving information representing an operating state agreeing with the search criteria from the accumulation means (13), and outputting a search result.

A manufacturing management apparatus includes an operation information acquisition unit, a trace information acquisition unit, a storage unit, a manufacturing device determination unit, and a transfer destination indication unit. The storage unit stores inspection information on each article in association with the trace information. The manufacturing device determination unit creates combinations of the manufacturing devices that can perform manufacturing processes in consideration of operation information and trace information, determines a quality index from the inspection information for each of the created combinations, and determines, based on the quality index determined for each of the combinations, a combination of the manufacturing devices to be used. A transfer destination indication unit indicates a transfer destination of a part to a transfer device based on the determined combination of the manufacturing devices.

The present disclosure provides techniques for improving aircraft machining process using machine learning-based predictive models and many-objective optimization algorithms. Data from a machining process is collected, where the data comprises a plurality of machining parameters and at least two performance metrics. For each respective performance metric, a respective predictive model is trained using one or more machine learning (ML) techniques, where the plurality of machining parameters are used as inputs, the respective performance metric as target outputs, and the respective predictive model learns to correlate the inputs to the target output. For each respective performance metric, a respective objective function is generated for the respective predictive model. Many-objective optimization is performed on the respective objective functions, and a set of solutions are generated for the machining process based on the many-objective optimization.