A crack measuring apparatus includes distance-measuring units, an image pickup unit having pixels the positions of which are identified on an imaging device, an infrared image pickup unit having pixels the positions of which are identified on an imaging device and having sensitivity to infrared rays, driving units, angle-measuring units, and an arithmetic control unit, the arithmetic control unit searches for a cracked portion from a temperature difference in an infrared image by turning the infrared image pickup unit, captures an image of the cracked portion by the image pickup unit and identifies a position of the cracked portion from a density difference in the captured image, measures the position of the cracked portion by the distance-measuring units and the angle-measuring units, and acquires three-dimensional absolute coordinates of the cracked portion.

A method of feedback controlling a 3D printing process in real time, and a system therefor are disclosed. The method includes collecting big data, generated through 3D printing experiments, related to process variables of 3D printing, measurement signals, and 3D printing quality of the 3D printing object; building an artificial neural network model by performing machine-learning based on the collected big data; evaluating whether or not a 3D printing quality of the 3D printing object is abnormal in real time based on an actual measurement signal of the 3D printing object and the artificial neural network model; and feedback controlling printing quality of the 3D printing object in real time based on the evaluation result of whether or not the 3D printing quality of the 3D printing object is abnormal.

A method of feedback controlling a 3D printing process in real time, and a system therefor are disclosed. The method includes collecting big data, generated through 3D printing experiments, related to process variables of 3D printing, measurement signals, and 3D printing quality of the 3D printing object; building an artificial neural network model by performing machine-learning based on the collected big data; evaluating whether or not a 3D printing quality of the 3D printing object is abnormal in real time based on an actual measurement signal of the 3D printing object and the artificial neural network model; and feedback controlling printing quality of the 3D printing object in real time based on the evaluation result of whether or not the 3D printing quality of the 3D printing object is abnormal.

A wireless asphalt measuring system is disclosed herein. The system takes thermal data of the asphalt via a thermal camera and distance data from a distance measuring instrument. A processing unit then calculates a metric, such as yield, based on the thermal data and distance data. The distance measuring instrument may communicate wirelessly to the processing unit and the processing unit may communicate to a local device or via the internet, eliminating the possibility of wires being damaged and rendering the system inoperable.

A portable optic metrology thermal chamber module including a housing defining a thermal chamber, with a thermally isolated environment arranged for holding an optic device under test, the housing having an optic stimulus entry aperture configured for entry of a stimulus beam, from a metrology system stimulus source through the entry aperture onto an entry pupil of the device to an image analyzer, and a module mount coupling to modularly mount the portable optic metrology thermal chamber module to a support of a metrology system of the metrology system stimulus source so as to removably couple the portable optic metrology thermal chamber module as a unit to the support in a predetermined position relative to the metrology system stimulus source, and the housing is sized and shaped so that the portable optic metrology thermal chamber module is portable as a unit for moving to and removing from the predetermined position.

A method of feedback controlling a 3D printing process in real time, and a system therefor are disclosed. The method includes collecting big data, generated through 3D printing experiments, related to process variables of 3D printing, measurement signals, and 3D printing quality of the 3D printing object; building an artificial neural network model by performing machine-learning based on the collected big data; evaluating whether or not a 3D printing quality of the 3D printing object is abnormal in real time based on an actual measurement signal of the 3D printing object and the artificial neural network model; and feedback controlling printing quality of the 3D printing object in real time based on the evaluation result of whether or not the 3D printing quality of the 3D printing object is abnormal.

A system for monitoring brick in a rotary kiln includes an infrared sensor and a computing system configured to: obtain a digital model of a brick layer of a rotary kiln having a plurality of bricks, wherein the digital model of the brick layer is based on a measured brick thickness correlated with a measured infrared temperature for each brick; obtain infrared data of the rotary kiln with the at least one infrared imaging sensor; determine the measured infrared temperature for each brick; determine a brick thickness of a first brick in the brick layer of the rotary kiln based on the measured infrared temperature assigned to the first brick with the digital model of the brick layer; and provide the brick thickness of the first brick in a brick thickness report.

An in-mold solidified shell thickness estimation apparatus includes: an input device; a model database configured to store a model formula and a parameter related to a solidification reaction of a molten steel inside a mold of a continuous casting facility; and a heat transfer model calculator configured to estimate an in-mold solidified shell thickness by calculating temperature distributions of the mold and of the molten steel inside the mold by solving a three-dimensional unsteady heat transfer equation. The heat transfer model calculator is configured to correct errors in a temperature of a mold copper plate and in an amount of heat removed from the mold, by correcting an overall heat transfer coefficient between the mold copper plate and the solidified shell.

An estimation device includes a controller that: obtains first-type temperature data for a position corresponding to an outside of a first-type position of a pipe in which a fluid flows; obtains second-type temperature data for a position corresponding to an outside of a second-type position of the pipe at which condition related to heat transfer is different than condition at the first-type position; based on the first-type temperature data and the second-type temperature data, calculates thermal resistance of a deposit formed on an inner surface of the pipe, and estimates a thickness of the deposit.

An estimation device includes a controller that: acquires a first pipe surface temperature and a second pipe surface temperature, wherein the first pipe surface temperature is an outer surface temperature of a pipe at a precipitate generation position at which a precipitate is adhered to an inner surface of the pipe through which a fluid flows, and the second pipe surface temperature is the outer surface temperature of the pipe at a reference position different from the precipitate generation position; calculates an in-pipe fluid temperature that is a temperature of the fluid at the precipitate generation position based on the second pipe surface temperature; and estimates a thickness of the precipitate based on the in-pipe fluid temperature and the first pipe surface temperature.