A skid state determination device is a skid state determination device for determining a state of a plurality of skids for supporting a workpiece to be processed by a laser processing machine, with a supporting surface formed by top points of a plurality of protrusions, and includes: imaging means configured to be able to image at least a part of a skid table provided with the plurality of skids; and state determination means configured to determine the state of the skids based on image information obtained by imaging the skid table with the imaging means, and the state determination means executes: first determination processing for determining the state of the skids based on the image information obtained by imaging the skid table with the imaging means, before the workpiece is carried onto the skid table; and second determination processing for determining the state of the skids based on the image information obtained by imaging the skid table with the imaging means, after the workpiece is carried out from the laser processing machine but before a product obtained by processing the workpiece is carried out from the skid table.

A harsh environment camera system. The system can include an enclosure having an electronics compartment and a camera compartment with a camera opening. A camera module is disposed within the camera compartment and one or more electronic components capable of generating heat are positioned in the electronics compartment. A compressed gas connector, connectable to a gas source, can be mounted on the enclosure. A metering orifice having an inlet and an outlet is in fluid communication with the connector. The metering orifice is operative to cool a gas flowing therethrough, thereby condensing moisture from the gas. A gas conduit is in fluid communication with the outlet of the orifice and extends through the electronics compartment to the camera compartment. A portion of the gas conduit is positioned in close proximity to the one or more electronic components, whereby the gas is heated prior to entering the camera compartment.

Devices and methods for inspection of various mechanical instruments to determine their level of wear and re-usability, particularly surgical and dental instruments, are provided.

According to an exemplary embodiment of the present disclosure, a method of setting up a work piece based on a vision is present. The method of setting up a work piece based on a vision comprises: calculating a start point of a work piece from the image of a work piece within a vision screen formed by a camera; capturing an image when a tool is captured in the vision screen; and calculating an offset value of the tool from the captured image.

An end mill inspection device, which inspects an end mill having a blade part formed in a curved convex shape or an arc shape, includes: an imaging data acquisition unit that acquires imaging data obtained by capturing an image of a blade part of the end mill by an imaging unit; a contour extraction unit that extracts the contour of the blade part on the basis of the imaging data acquired by the imaging data acquisition unit; and a curvature radius calculation unit that calculates a curvature radius of the contour on the basis of the contour of the blade part extracted by the contour extraction unit.

A method includes acquiring an image of a cutter on a drill bit, determining a cutter dull condition based on the image of the cutter using a machine-learning method, wherein the machine-learning method is trained using a set of training images and a set of known cutter dull conditions, wherein each of the set of known cutter dull conditions is associated with one or more of a set of cutters depicted in the set of training images and determining a cutter degradation severity based the image of the cutter. The method also includes generating bit modification instructions based the cutter dull condition and the cutter degradation severity.

A method comprising identifying a cutter on a drill bit based on a drill bit image, assigning a grading value of the cutter based on a classification model of a machine learning system, wherein the classification model is generated based on a set of training cutter images associated with drill bit characteristics indicators, determining a surface parameter based on a surface of the cutter, generating a comparison value based on the surface parameter, and mapping a set of cutter information to the cutter on the drill bit, wherein the set of cutter information comprises the grading value and the comparison value.

The present disclosure provides an internal cooling/external cooling-switching milling minimum-quantity-lubrication intelligent nozzle system and method, relating to the field of milling lubrication. The system includes: a vision system, configured to acquire a real-time milling depth of a workpiece and send the real-time milling depth to a lubrication manner controller for processing; a lubrication system, including an internal cooling system and an external cooling system connected together to a cutting fluid supply source through a reversing device; and the lubrication manner controller, configured to communicate with the vision system and the lubrication system respectively, and control the reversing device to act according to a set milling depth threshold and data acquired by the vision system, so as to adjust and switch to the internal cooling system or the external cooling system to work. Milling depth data of a machine tool is collected, the milling depth data is transmitted to a control center for data analysis and processing, the data is compared with an initially set internal cooling/external cooling switching threshold to obtain the most suitable cooling and lubrication manner under current machining conditions of the machine tool, and the control center controls the internal cooling and external cooling systems according to the obtained result to realize intelligent switching of the cooling and lubrication manner between internal cooling and external cooling.

A multi-station self-positioning floating clamping and workpiece automatic flip intelligent fixture system includes a linear motion device, a workpiece automatic flip device and a self-positioning floating clamping device. A bottom portion of the workpiece automatic flip device is connected to the linear motion device, which drives the workpiece automatic flip device to move horizontally; the self-positioning floating clamping device clamps the workpiece, the workpiece automatic flip device is arranged opposite to the self-positioning floating clamping device, and the workpiece automatic flip device clamps the workpiece and drives the workpiece to turn over; and the workpiece automatic flip device includes a rotary cylinder, which is connected to a hydraulic cylinder, a piston rod of the hydraulic cylinder is connected to a mechanical claw opening-and-closing finger through a hinge mechanism, and the hydraulic cylinder reciprocates to drive the mechanical claw opening-and-closing finger to open and close to clamp or release the workpiece.

A smart tool includes a body having a working output. A first controller is disposed within the body and connected to a plurality of sensors. The plurality of sensors includes at least one camera having a field of view at least partially capturing the working output. A neural network is trained to analyze an image feed from the at least one camera and trained perform at least one of classifying at least one of a working tool and an extension connected to the working output, classifying a component and/or a portion of a component interfaced with the working output, and determining a positioning of the smart tool. The neural network is stored in one of the first controller and a processing unit remote from the smart tool.