An automated method of inspecting a pipe includes: positioning the pipe with respect to a laser scanner using a positioning apparatus; scanning a size of the positioned pipe by the laser scanner; identifying a specification and historical data of the pipe's type by inputting the scanned size to an artificially intelligent module trained through machine learning to match input size data to standardized pipe types and output corresponding specifications and historical data of the pipe types; scanning dimensions of the positioned pipe by the laser scanner using a dimension portion of the identified historical data; comparing the scanned dimensions with standard dimensions from the identified specification; detecting a dimension nonconformity when the scanned dimensions are not within acceptable tolerances of the standard dimensions; and in response to detecting the dimension nonconformity, generating an alert and updating the dimension portion of the identified historical data to reflect the detected dimension nonconformity.

Systems, methods, and apparatus for tracking location of an inspection robot are disclosed. An example apparatus for tracking inspection data may include an inspection chassis having a plurality of inspection sensors configured to interrogate an inspection surface, a first drive module and a second drive module, both coupled to the inspection chassis. The first and second drive module may each include a passive encoder wheel and a non-contact sensor positioned in proximity to the passive encoder wheel, wherein the non-contact sensor provides a movement value corresponding to the first passive encoder wheel. An inspection position circuit may determine a relative position of the inspection chassis in response to the movement values from the first and second drive modules.

Systems and methods of identifying a coating on a bone material are provided. The systems and methods comprise providing a bone material and a scanning device; adjusting a distance between the bone material and the scanning device; scanning the bone material using the scanning device; and transmitting a scanned data from the scanning device to a processor configured to analyze the scanned data, and display the analyzed scanned data to identify the coating on the bone material based on the scanned data when the coating meets or fails to meet a predetermined parameter.

A film-thickness measuring method capable of substantially extending a wavelength range of a spectrum of reflected light from a workpiece, and accurately measuring a film thickness is disclosed. The film-thickness measuring method includes: pressing a workpiece against a polishing pad, while rotating a polishing table that supports the polishing pad, to polish the workpiece; during the polishing of the workpiece, directing light to the workpiece from a liquid-seal sensor and a transparent-window sensor disposed in the polishing table and receiving reflected light from the workpiece by the liquid-seal sensor and the transparent-window sensor; and determining a film thickness of the workpiece based on a spectrum of the reflected light from the workpiece.

Disclosed are apparatuses and methods for measuring a thickness. The apparatus for measuring a thickness including a light source that emits a femto-second laser, an optical coupler through which a portion of the femto-second laser is incident onto a target and other portion of the femto-second laser is incident onto a reference mirror, a detector configured to receive a reflection signal reflected on the reference mirror and a sample signal generated from the target and configured to measure a thickness of the target based on an interference signal between the reflection signal and the sample signal, and a plurality of optical fiber lines configured to connect the light source, the optical coupler, and the detector to each other may be provided.

Disclosed is a coating apparatus. A first imaging unit captures an image of the substrate disposed on the stage. A second imaging unit captures an image of the substrate disposed on the stage with a narrower viewing angle and higher resolution than the first imaging unit. The control unit performs a pre-alignment processing of capturing an image of a circular pre-alignment mark formed on the substrate using the first imaging unit and performing positioning of the substrate by controlling the moving mechanism and the rotating mechanism based on the captured image and, after the pre-alignment processing, performs a fine-alignment processing of capturing an image of a circular fine-alignment mark formed on the substrate using the second imaging unit and performing positioning of the substrate by controlling the moving mechanism and the rotating mechanism based on the captured image.

An inspection robot incudes a robot body, at least two sensors, a drive module, a stability assist device and an actuator. The at least two sensors are positioned to interrogate an inspection surface and are communicatively coupled to the robot body. The drive module includes at least two wheels that engage the inspection surface. The drive module is coupled to the robot body. The stability assist device is coupled to at least one of the robot body or the drive module. The actuator is coupled to the stability assist device at a first end, and coupled to one of the drive module or the robot body at a second end. The actuator is structured to selectively move the stability assist device between a first position and a second position. The first position includes a stored position. The second position includes a deployed position.

A method for measuring the displacement of a surface relative to a base reference. The method includes generating point cloud data between a reference point and a surface disposed distally from the reference point to define a three dimensional image of the surface. The method also includes determining the location and direction of key reference parameters of the object defined by the three dimensional image from the point cloud data. The method also includes obtaining base reference data in a prescribed co-ordinate system in respect of a base reference relative to the surface. Further, the method provides for processing the point cloud data and the base reference data, orientating the point cloud data relative to the key reference parameters defined by the base reference data and transforming the point cloud data into a co-ordinate system coinciding with the base reference data.

A coating production line system for coating work pieces comprises a coating powder, a coating apparatus, an inspection unit to measure the thickness of the applied coating, a conveyor unit to move the work pieces through the system, and a control unit to use thickness requirements and coating parameters to control the coating apparatus based on said coating parameters with a machine learning instance. A database comprises coating powder characteristics parameter as input vector for the machine learning instance for generating an output vector to control the coating apparatus being a first additional part vector. The control unit determines the coating quality based on a comparison between the thickness data acquired from the inspection unit and the retrieved thickness requirement data as second additional part vector. The first and second additional part vectors are fed back as additional parts to the next input vector for the machine learning instance.

A system includes a memory, and at least one processing device, operatively coupled to the memory, to facilitate an etch recipe development process by performing operations including obtaining, from an optical detector, first material thickness data for a first material and second material thickness data for a second material resulting from an iteration of an etch process using an etch recipe. The first material is located at a first reflectometry measurement point and the second material is located at a second reflectometry measurement point different from the first reflectometry measurement point. The operations further include determining one or more etch parameters based on at least the first material thickness data and the second material thickness data.