Systems and methods are provided for non-destructive testing (NDT) with trained artificial intelligence based processing.

Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.

A workpiece inspection and defect detection system includes a light source, a lens that inputs image light arising from a surface of a workpiece, and a camera that receives imaging light transmitted along an imaging optical path. The system utilizes images of workpieces acquired with the camera as training images to train a defect detection portion to detect defect images that include workpieces with defects. Anomaly detector classification characteristics are determined based on features of the training images. Run mode images of workpieces are acquired with the camera, and based on determined features from the images, the anomaly detector classification characteristics are utilized to determine if the images of the workpieces are classified as anomalous. In addition, the defect detection portion determines if images are defect images that include workpieces with defects and for which additional operations may be performed (e.g., metrology operations for measuring dimensions of the defects, etc.)

A vision inspection system includes a sorting platform having an upper surface supporting parts for inspection. An inspection station is positioned adjacent the sorting platform including an imaging device to image the parts in a field of view. A vision inspection controller receives images from the imaging device. The vision inspection controller includes an image histogram tool to pre-process the images to improve contrast of the images by redistributing lightness values of the images based on adaptive histogram equalization processing to generate enhanced images. The vision inspection controller processes the enhanced images based on an image analysis model to determine inspection results for each of the parts. The vision inspection controller has an artificial intelligence learning module operated to customize and configure the image analysis model based on the enhanced images.

There is provided a system and method of generating a training set usable for examination of a semiconductor specimen. The method comprises: obtaining a simulation model capable of simulating effect of a physical process on fabrication process (FP) images depending on the values of parameters of the physical process; applying the simulation model to an image to be augmented for the training set and thereby generating one or more augmented images corresponding to one or more different values of the parameters of the physical process; and including the generated one or more augmented images into the training set. The training set can be usable for examination of the specimen using a trained Deep Neural Network, automated defect review, automated defect classification, automated navigation during the examination, automated segmentation of FP images, automated metrology based on FP images and other examination processes that include machine learning.

A method for regulating a position of an object includes detecting a plurality of first alignment structures of the object under rotation of the object, wherein a plurality of second alignment structures of the object sequentially face a photosensitive element during the rotation of the object, and when the plurality of first alignment structures have reached a first predetermined state, stopping the rotation of the object and performing an image capturing procedure of the object. The image capturing procedure includes: capturing a test image of the object, wherein the test image includes an image block presenting the second alignment structure currently facing the photosensitive element; detecting the position of the image block in the test image; when the image block is located in the middle of the test image, capturing a detection image of the object.

In an embodiment, a method for detecting cracks in road segments is provided. The method includes: receiving raw range data for a first image by a computing device from an imaging system, wherein the first image comprises a plurality of pixels; receiving raw intensity data for the first image by the computing device from an imaging system; fusing the raw range data and raw intensity data to generate fused data for the first image by the computing device; extracting a set of features from the fused data for the first image by the computing device; providing the set of features to a trained neural network by the computing device; and generating a label for each pixel of the plurality of pixels by the trained neural network, wherein a received label for a pixel indicates whether or not the pixel is associated with a crack.

A belt examination system includes a defect candidate detecting processor that detects a candidate for a belt defect that is an abnormal portion of an intermediate transfer belt of an image forming apparatus from a belt image that is an image of the intermediate transfer belt, the defect candidate detecting processor executes a background pattern reduction step to reduce a texture-pattern like background noise present in the belt image and detects the candidate based on the belt image generated during the background pattern reduction step, and the background pattern reduction step is to replace, in the belt image, a color value within a specific range of color values not including a lowest color value of the belt defect with a specific color value within the specific range.

Inspection is efficiently performed without lowering inspection accuracy, by performing an inspection using AI processing. An inspection device has a learning unit that generates a learning model by performing learning for discriminating a type of an inspection object by using as teacher data at least a part of classification results obtained by classifying a plurality of inspected objects of a same type as an inspection object into a plurality of types, or acquires the learning model, a calculation unit that outputs numerical data obtained by quantifying a level of classification accuracy of the type of the inspection object, based on a result calculated by inputting the inspection object to the learning model, and a determination unit that determines, by comparing the numerical data with types of thresholds, whether to automatically discriminate the type of the inspection object or to manually discriminate the type of the inspection object.

A defect inspection system provides an image of a surface of a hard drive media to a machine learning model that is trained to identify predefined classifications of abnormal surface patterns on the hard drive media, each of the predefined classifications being associated in system memory with a severity indicator. The defect inspection model analyzes the image and generates and output indicating that the image includes a pattern consistent with a select classification of the predefined classifications of abnormal surface patterns. When the severity indicator for the select classification satisfies a failure condition, the defect inspection system automatically implements a corrective action.