In an embodiment, a processing device relating to an inspection of an inspection object by a photography unit is provided. A processor of the processing device calculates a plurality of photography points as positions photographing the inspection object based on shape data in which a shape of a surface of the inspection object is indicated by a point group, and information relating to a position and a normal vector on the surface of the inspection object is defined by the point group. The processor executes analysis regarding a path that passes through all of the calculated photography points and minimizes a sum of a movement cost from each of the photography points to a photography point of a next movement destination, and calculates a path corresponding to an analysis result as a path for moving the photography unit.

A manufacturing monitoring assistance device includes: a model creation unit creating a computation model when a product as a sample is normal, based on a three-dimensional form acquired from the product; a simulation unit creating a corrective computation model when the product is abnormal, by adding a sample of an abnormal portion in the product to the created computation model, and performing a simulation on each of the computation model and the corrective computation model; and a monitoring method determination unit determining a method for monitoring a manufacturing process for the product, based on an abnormality index being a difference between an output from a sensor as a result of the simulation performed on the computation model and an output from a sensor as a result of the simulation performed on the corrective computation model, and causing an output device to display the determined method and the abnormality index.

The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.

A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range, and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.

A manufacturing monitoring assistance device includes: a model creation unit creating a computation model when a product as a sample is normal, based on a three-dimensional form acquired from the product; a simulation unit creating a corrective computation model when the product is abnormal, by adding a sample of an abnormal portion in the product to the created computation model, and performing a simulation on each of the computation model and the corrective computation model; and a monitoring method determination unit determining a method for monitoring a manufacturing process for the product, based on an abnormality index being a difference between an output from a sensor as a result of the simulation performed on the computation model and an output from a sensor as a result of the simulation performed on the corrective computation model, and causing an output device to display the determined method and the abnormality index.

The present disclosure is directed to a computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors. The computer-implemented method includes defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.

The present disclosure is directed to a computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors. The computer-implemented method includes defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.

In an embodiment, a processing device relating to an inspection of an inspection object by a photography unit is provided. A processor of the processing device calculates a plurality of photography points as positions photographing the inspection object based on shape data in which a shape of a surface of the inspection object is indicated by a point group, and information relating to a position and a normal vector on the surface of the inspection object is defined by the point group. The processor executes analysis regarding a path that passes through all of the calculated photography points and minimizes a sum of a movement cost from each of the photography points to a photography point of a next movement destination, and calculates a path corresponding to an analysis result as a path for moving the photography unit.

A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range; and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.

The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations.