Virtually-integrated wire harness design and automated production systems and methods that achieve completely integrated data management by automatically producing scripts to dynamically propagate production commands and data to various subsystems for handling assembling necessary circuits and wire harness layout boards to produce corresponding batches of wire harnesses while script-based methods control configuring, testing, and using wire harness layout boards, and assembling, testing, reworking, and delivering wire harnesses. Augmented reality is used to assist in the assembly of layout boards and wire harnesses.

A display method of a manufacturing status to visualize a manufacturing status of products each manufactured through processing performed by a plurality of apparatuses included in a manufacturing line includes identifying, for each product to be manufactured in a certain manufacturing unit, a starting time or an ending time or both of the processing performed on the product by a first apparatus and a starting time or an ending time or both of the processing performed on the product by a second apparatus based on log information of the first apparatus and log information of the second apparatus, the first and the second apparatuses being included in the manufacturing line, the second apparatus performing the processing after the processing performed by the first apparatus, using a processor.

The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

The present invention provides methods and systems for manufacturing process control of photovoltaic products. Some embodiments relate to a method for tracking wafers for photovoltaic products with respect to which production tool processed them and their position within that production tool. Some embodiments relate to measuring and characterizing the critical-to-quality parameters of the partially-finished photovoltaic products emerging from the production tool in question. Some embodiments relate to display and visualization of the measured parameters on a computer screen, such that the parameters of each production unit can be directly observed in the context of which production tools processed them, which location within a specific production tool they were located in during processing, and which batch, or in the case of continuous processing, what time, the unit(s) was/where processed.

A product inspection device and method for correctly calculating consumer and producer risks irrespective of the type of distribution of products. A characteristic value representing a predetermined product characteristic is measured for each product as a product measurement value, and a standard deviation of measurement variations in measurement results is calculated as a measurement value standard deviation. The products are determined to be conforming based on whether the measured product measurement value falls within a range of a product standard. Consumer and producer risks are calculated based on the measurement variations. The calculated consumer and producer risks are respectively and successively added up and it is determined whether the number of products having undergone the adding have reached a predetermined number. If so, the added up consumer risk and producer risk are divided by the number of products to calculate a final consumer risk and a final producer risk.

There are included a probe that can be arranged in an imaging field of view, a horizontal drive section for causing the probe to contact a side surface of a workpiece on a stage, a display section for displaying a model image, a contact position designation section for receiving designation of contact target position information in the model image, a characteristic amount information setting section for setting characteristic amount information, a measurement setting information storage section for storing a plurality of pieces of contact target position information and the characteristic amount information, and a measurement control section for identifying a position and an attitude of the workpiece from a workpiece image by using the characteristic amount information, for identifying a plurality of contact target positions on the side surface of the workpiece where the probe should contact, based on the identified position and the identified attitude of the workpiece.

A test case is executed on a product using a hardware machine, the product being configured in manufacturing according to a configuration, the test case being related to an estimation function. A set of data points resulting from the executing is collected. Using the estimation function, an estimated time needed for the executing is computed. Using the set of data points, an actual time used for the executing is computed. A determination is made whether a difference between the estimated time and the actual time is within a tolerance. Responsive to the difference not being within the tolerance, the estimation function is adjusted using the set of data points and at least one other set of data points, the other set of data points being generated by executing the test case on a second product configured according to a second configuration at a previous time.

Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.

A device manufacturing method, the method comprising: obtaining a measurement data time series of a plurality of substrates on which an exposure step and a process step have been performed; obtaining a status data time series relating to conditions prevailing when the process step was performed on at least some of the plurality of substrates; applying a filter to the measurement data time series and the status data time series to obtain filtered data; and determining, using the filtered data, a correction to be applied in an exposure step performed on a subsequent substrate.

A control method includes: monitoring a statistic obtained by performing a multivariate analysis on a plurality of parameters; extracting, from the plurality of parameters, a predetermined number of higher-order parameters in terms of an influence degree on fluctuation of the statistic; generating a plurality of experimental patterns according to an experimental design method; acquiring a measurement result of a specific parameter indicating quality of a product when at least one device is controlled according to each of the plurality of experimental patterns; setting a new target value of the predetermined number of higher-order parameters in order to stabilize a value of the specific parameter within a management range based on the measurement result; and controlling the at least one device such that the predetermined number of higher-order parameters approaches the new target value.