SEM Coating Thickness Analysis for Overlapping Grayscale Layers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing thresholding methods fail to accurately segment and measure the thickness of boron nitride (BN) and silicon-doped boron nitride (SiBN) layers in next-generation interfacial coatings (IFCs) due to overlapping grayscale intensities, leading to errors in mechanical property evaluation of fiber-reinforced ceramic matrix composites.
Innovation Solution
A method and system using scanning electron microscopy (SEM) with automated image processing to identify inflection points in grayscale intensity gradients for precise segmentation and thickness measurement of BN and SiBN layers, employing a maximum inscribed circle method and watershed algorithm for accurate layer identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional thresholding techniques are used for phase segmentation, then the method is simple and easy to implement, but the measurement precision of BN and SiBN thickness deteriorates due to overlapping grayscale intensities
Solution Approach 1:
The patent segments the image analysis process into multiple stages: initial thresholding to separate major phases, followed by gradient calculation, inflection point detection, and iterative refinement. This multi-stage segmentation approach allows accurate identification of layer boundaries even when grayscale intensities overlap, resolving the contradiction between simple implementation and precise measurement.
Solution Approach 2:
The patent performs preliminary gradient calculation and inflection point identification before final thickness measurement. By pre-processing the image data to highlight transitions and boundaries through gradient analysis, the method prepares optimized data for subsequent thresholding and measurement steps, improving precision without significantly increasing implementation complexity.
2Productivity
If standard thresholding methods are used, then the process is automated and efficient, but the reliability of phase segmentation deteriorates when SiBN grayscale intensity overlaps with BN or SiC
Solution Approach 1:
The patent implements an iterative feedback mechanism where initial thresholding results are used to calculate gradients, identify inflection points, and refine threshold values for subsequent segmentation passes. This feedback loop continuously improves segmentation reliability by using measured boundary positions to adjust analysis parameters, maintaining automation while achieving accurate phase separation even with overlapping intensities.
Solution Approach 2:
The patent employs dynamic threshold adjustment where threshold values are not fixed but are adapted based on local gradient characteristics and inflection point positions. This dynamic approach allows the segmentation process to automatically adjust to varying grayscale conditions in different regions of the image, improving reliability across diverse material compositions while maintaining automated processing.
3Measurement precision
If manual threshold parameter adjustment is performed for each image batch, then measurement accuracy can be optimized, but the time required for analysis increases significantly
Solution Approach 1:
The patent implements self-service automation where the system automatically determines optimal threshold parameters by analyzing gradient distributions and inflection points in each image batch. The automated parameter optimization uses the measured data characteristics to self-adjust analysis settings, eliminating manual intervention while maintaining high measurement precision across different batches and conditions.
Solution Approach 2:
The patent automatically changes analysis parameters (threshold values, gradient thresholds, inflection point criteria) based on the specific characteristics of each image batch. By dynamically adjusting parameters according to measured grayscale distributions and gradient profiles, the system maintains optimal measurement accuracy for each batch without requiring manual tuning, thus preserving precision while reducing time loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and automated measurement of BN and SiBN thickness, improving the reliability of mechanical property evaluation in fiber-reinforced ceramic matrix composites by clearly distinguishing material phases with overlapping grayscale intensities.
Implementation Method 1
Scanning electron microscopy (SEM) imaging with a backscattered electron detector (BSD) can be used to produce images of the material layers
Data Source
AI summary
A method for determining coating thicknesses of a coated fiber embedded in a matrix is presented. A scanning electron microscope (SEM) image is captured of a composite material having multiple coated fibers embedded in a matrix. This image depicts a cross-sectional portion of the composite material. A processor identifies a region of the image depicting a cross-section of one among the plurality of coated fibers, and generates a line graph of a gradient of grayscale values as a function of a line scan. The line scan traverses across the image along a radius of the coated fiber, and extends between an interior location of the coated fiber and an exterior region outside the coated fiber. The processor identifies inflection points in the line graph of the gradient of grayscale values as a function of the line scan traverse location, and determines coating thickness based on these inflection points.