Abstract: In one embodiment, an automatic high-speed X-ray system may generate a high-resolution X-ray image of an inspected sample at a direction substantially orthogonal to a plane of the inspected sample. The system may determine a first cross-sectional shape of a first portion of a first element of interest in the inspected sample based on grayscale values of the X-ray image associated with the first element of interest. The system may determine a second cross-sectional shape of a second portion of the first element of interest in the inspected sample. The second cross-sectional shape may be determined based on the grayscale values of the X-ray image associated with the first element of interest. The system may determine one or more first metrological parameters associated with the first element of interest in the inspected sample based a comparison of the first cross-sectional shape and the second cross-sectional shape.

Abstract: A method and system for inspecting a honeycomb body. The method includes capturing a first image. Instances of at least one feature in the first image that correlates to a structural characteristic of the honeycomb body are detected. One or more detected instances of the at least one feature identified in the first image are abstracted by creating a graphical representation of each of the one or more detected instances of the at least one feature. A second image is generated by augmenting the first image with the graphical representation in place of or in addition to each of the one or more detected instances of the at least one feature identified in the first image. The second image is analyzed using a machine learning algorithm to classify the honeycomb body with respect to the structural characteristic of the honeycomb body.

Abstract: In one embodiment, an automatic high-speed X-ray system may generate a high-resolution X-ray image of an inspected sample at a direction substantially orthogonal to a plane of the inspected sample. The system may determine a first cross-sectional shape of a first portion of a first element of interest in the inspected sample based on grayscale values of the X-ray image associated with the first element of interest. The system may determine a second cross-sectional shape of a second portion of the first element of interest in the inspected sample. The second cross-sectional shape may be determined based on the grayscale values of the X-ray image associated with the first element of interest. The system may determine one or more first metrological parameters associated with the first element of interest in the inspected sample based a comparison of the first cross-sectional shape and the second cross-sectional shape.

Abstract: Imaging-based methods for detecting defects in an extruded cellular ceramic article having a web array are disclosed. The methods may include capturing a digital image of the web array to establish an intensity-based initial web array representation. The methods also may include performing an intensity threshold process on the initial web array representation to define an intensity-based high-resolution web array representation having sub-pixel resolution, and performing piecewise cubic spline fits to define corresponding web skeletons. The method may also include comparing intensities of the high-resolution web array representation along the web skeletons to a threshold intensity to determine the defect locations and sizes. The methods can be applied to determining the size and location of skin defects as well.

Abstract: Imaging-based methods for detecting defects in an extruded cellular ceramic article having a web array are disclosed. The methods may include capturing a digital image of the web array to establish an intensity-based initial web array representation. The methods also may include performing an intensity threshold process on the initial web array representation to define an intensity-based high-resolution web array representation having sub-pixel resolution, and performing piecewise cubic spline fits to define corresponding web skeletons. The method may also include comparing intensities of the high-resolution web array representation along the web skeletons to a threshold intensity to determine the defect locations and sizes. The methods can be applied to determining the size and location of skin defects as well.

Abstract: Optical reader systems and methods with rapid microplate position detection capability are disclosed. The optical reader system includes a scanning optical system configured to scan select position-detecting features to accurately determine the microplate position within the optical reader system. The method includes measuring positions of the position-detecting features in order of their respective amounts of position tolerance, from smallest to largest. The position measurement is carried out for a number of position-detecting features sufficient to determine the microplate position with the optical reader system to within a select microplate position tolerance.

Abstract: Optical reader systems and methods with rapid microplate position detection capability are disclosed. The optical reader system includes a scanning optical system configured to scan select position-detecting features to accurately determine the microplate position within the optical reader system. The method includes measuring positions of the position-detecting features in order of their respective amounts of position tolerance, from smallest to largest. The position measurement is carried out for a number of position-detecting features sufficient to determine the microplate position with the optical reader system to within a select microplate position tolerance.

Abstract: An edge inspection method for detecting defects on a wafer edge normal surface includes acquiring a set of digital images which captures a circumference of the wafer. An edge of the wafer about the circumference is determined. Each digital image is segmented into a plurality of horizontal bands. Adjacent edge clusters about the circumference of the wafer are combined into edge pixel bins. The edge pixel bins are analyzed via edge clusters analysis to identify defects. The edge pixel bins are also analyzed via blob analysis to determine defects.

Abstract: An edge inspection method for detecting defects on a wafer edge normal surface includes acquiring a set of digital images which captures a circumference of the wafer. An edge of the wafer about the circumference is determined. Each digital image is segmented into a plurality of horizontal bands. Adjacent edge clusters about the circumference of the wafer are combined into edge pixel bins. The edge pixel bins are analyzed via edge clusters analysis to identify defects. The edge pixel bins are also analyzed via blob analysis to determine defects.

Abstract: An edge inspection method for detecting defects on a wafer edge normal surface includes acquiring a set of digital images which captures a circumference of the wafer. An edge of the wafer about the circumference is determined. Each digital image is segmented into a plurality of horizontal bands. Adjacent edge clusters about the circumference of the wafer are combined into edge pixel bins. The edge pixel bins are analyzed via edge clusters analysis to identify defects. The edge pixel bins are also analyzed via blob analysis to determine defects.

Abstract: A system for inspecting components is provided. The system includes an axial lighting system that illuminates the component with axial lighting to allow one or more features of the component to be located, such as by causing protruding features to be brighter than the background and recessed features to be darker than the background. An off-axis lighting system illuminates the component with off-axis lighting in the absence of the axial lighting to allow the component to be inspected to locate one or more features, such as a bump contact.

Abstract: An edge inspection method for detecting defects on a wafer edge normal surface includes acquiring a set of digital images which captures a circumference of the wafer. An edge of the wafer about the circumference is determined. Each digital image is segmented into a plurality of horizontal bands. Adjacent edge clusters about the circumference of the wafer are combined into edge pixel bins. The edge pixel bins are analyzed via edge clusters analysis to identify defects. The edge pixel bins are also analyzed via blob analysis to determine defects.