# US 10,371,650 B2 · Macrotexture map visualizing texture heterogeneity in polycrystalline parts

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,371,650 B2
- Document ID: US10371650B2
- Status: Granted
- Kind: B2
- Application number: 15/273,128
- Filed: 2016-09-22
- Published: 2018-03-22
- Granted: 2019-08-06
- Earliest claimed: 2016-09-22
- Assignees: Advanced Manufacturing LLC
- Applicants: Dongsheng Li
- Inventors: Dongsheng Li
- Web page: https://patentgrep.com/patent/US10371650B2

## CPC

- G01N 23/203: Measuring back scattering
- G01N 23/2206: Combination of two or more measurements, at least one measurement being that of secondary emission, e.g. combination of secondary electron [SE] measurement and back-scattered electron [BSE] measurement
- G06F 18/23: Clustering techniques
- G06T 11/10: Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77)
- G06T 11/26: Drawing of charts or graphs
- G01N 2223/401: image processing
- G01N 2223/606: texture
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

This invention provides a method, system, and computer program to visualize texture (crystal orientation distribution) heterogeneity in polycrystalline aggregate part in large length scale. This is a critical representation step for microstructure characterization, useful in effective behavior simulation, risk analysis and hotspot identification. In contrast to orientation image map where each color component represents a crystal orientation, each color in this macrotexture map represents a set of texture. Different color represent different texture and similar texture shall have similar color. This method will provide a critical tool in evaluating texture heterogeneity of components, leading to a first-hand understanding of property heterogeneity and anisotropy. For an experienced user, these maps serve the same purpose in identifying high risk locations in the investigated component as medical imaging maps do for diagnosis purpose. This method will also serve as a starting point in mesoscale simulation with meshing sensitivity based on the texture heterogeneity. It will provide a bridge between texture characterization and behavior simulation of component with texture heterogeneity. This method will also offer a linkage between crystal plasticity simulation in small length scale and finite element/difference simulation in large length scale.

## Claims

### Claim 1

A method for generating an image of a polycrystalline component in the form of a macrotexture map comprising:
obtaining a component;
evaluating the polycrystalline structure of the component preliminarily;
creating a sampling strategy to measure texture heterogeneity of at least one full side of the component;
selecting a plurality of sampling locations on the at least one full side of the component for texture measurement;
selecting a texture measurement method;
using the selected texture measurement method to obtain data representing texture, wherein texture is based on crystal orientation distribution of aggregated crystals;
processing the data representing texture to obtain a texture parameter set, wherein the texture parameter set comprises one of:
weights of texture components used to express local texture, and random texture; and
visually representing texture at different locations on the at least one full side of the component on a macrotexture map using the texture parameter set by
creating a uniform color map based on the texture parameter set as a local function;
creating the macrotexture map based the color map; and
rendering the macrotexture map.

### Claim 2 (depends on 1)

The method as recited in claim 1, wherein the parameter set is determined by weights of the features constructed by principal component analysis (PCA) of texture.

### Claim 3 (depends on 1)

The method as recited in claim 1, wherein the parameter set is determined by weights of the features constructed by cluster analysis of texture.

### Claim 4 (depends on 1)

The method as recited in claim 1, wherein the parameter set is determined by weights of the features constructed by spectral analysis of texture.

### Claim 5 (depends on 1)

The method as recited in claim 1, wherein the parameter set is determined by coefficients of spherical harmonics used in expansion expression of texture.

### Claim 6 (depends on 1)

The method as recited in claim 1, wherein the parameter set comprises coefficients of Fourier expansion of texture.

### Claim 7 (depends on 1)

The method as recited in claim 1, wherein a color code utilizes 1 or more numbers determined from the parameter set to create a color system.

### Claim 8 (depends on 7)

The method as recited in claim 7, wherein the color code utilizes one number of parameter sets to create the color system, including a gray bar and a jet color map.

### Claim 9 (depends on 7)

The method as recited in claim 7, wherein the color code utilizes 2 or more number of parameter sets to create the color system.

### Claim 10 (depends on 7)

The method as recited in claim 7, wherein the color code utilizes reduced dimension of parameter sets to create the color system.

### Claim 11 (depends on 1)

The method as recited in claim 1, wherein the macrotexture map is a colored map with a first texture represented by a first color and a second texture represented by a second color.

### Claim 12 (depends on 1)

The method of claim 1, wherein the polycrystalline component is a metal sheet.