--- name: metallurgical-analysis-d708f0cb2f6b description: Specialized skill for metallic materials analysis and metallography including grain size measurement, phase quantification, and inclusion rating allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: materials-science domain: science category: materials-characterization priority: high phase: 6 tools-libraries: - ImageJ - Clemex - ASTM E112 - ASTM E45 graph: domains: [domain:materials-science] skillAreas: [skill-area:data-analysis, skill-area:statistical-analysis, skill-area:mathematical-reasoning] workflows: [workflow:experiment-design] roles: [role:research-engineer] --- # Metallurgical Analysis Skill ## Purpose The Metallurgical Analysis skill provides specialized capabilities for characterizing metallic materials through metallographic techniques, enabling systematic evaluation of microstructure, grain size, phase distribution, and inclusion content per industry standards. ## Capabilities - Metallographic preparation protocol selection - Etching reagent selection for different alloys - Grain size measurement (ASTM E112, intercept method) - Phase fraction quantification (point count, image analysis) - Inclusion rating (ASTM E45) - Banding and segregation assessment - Prior austenite grain boundary revelation - Weld microstructure evaluation (HAZ mapping) ## Usage Guidelines ### Sample Preparation 1. **Sectioning** - Select appropriate cutting method (abrasive, EDM, precision saw) - Minimize heat input to prevent microstructural changes - Identify orientation relative to processing direction 2. **Mounting** - Choose mount type (hot compression, cold setting) - Add conductive filler for SEM/EBSD samples - Consider edge retention requirements 3. **Grinding and Polishing** - Follow systematic grit progression (120 to 1200 to diamond) - Use appropriate lubricant and rotation direction - Verify scratch-free surface before etching ### Etching Selection | Alloy System | Etchant | Purpose | |--------------|---------|---------| | Carbon steels | 2% Nital | General microstructure | | Stainless steel | Vilella's | Martensitic structures | | Aluminum | Keller's | Grain boundaries, precipitates | | Titanium | Kroll's | Alpha-beta microstructure | | Copper | FeCl3/HCl | Grain boundaries | | Nickel superalloys | Glyceregia | Gamma prime, carbides | ### Grain Size Measurement 1. **ASTM E112 Methods** - Comparison method: Match to standard charts - Planimetric method: Count grains in known area - Intercept method: Count grain boundary intersections 2. **Calculation** - Apply magnification correction - Use minimum 5 fields for statistical validity - Report ASTM grain size number with standard deviation 3. **Special Cases** - Duplex structures: Report both phases separately - Elongated grains: Measure aspect ratio - Prior austenite: Use specific etchants (picric acid based) ### Inclusion Rating (ASTM E45) 1. **Method Selection** - Method A: Worst field rating - Method D: Quantitative measurement - Specify inclusion type (A, B, C, D sulfides, oxides) 2. **Reporting** - Include severity (thin, heavy) and length - Note distribution (random, stringer, cluster) - Compare to specification limits ## Process Integration - MS-002: Electron Microscopy Characterization - MS-017: Root Cause Failure Analysis ## Input Schema ```json { "sample_id": "string", "alloy_system": "steel|aluminum|titanium|copper|nickel", "alloy_grade": "string", "analysis_type": "grain_size|phase_fraction|inclusion|weld_eval", "magnification": "number", "etchant_used": "string" } ``` ## Output Schema ```json { "sample_id": "string", "grain_size": { "astm_number": "number", "average_diameter": "number (microns)", "standard_deviation": "number", "method": "string" }, "phase_fractions": [ { "phase": "string", "fraction": "number (percent)", "morphology": "string" } ], "inclusion_rating": { "method": "string", "type_a": "number", "type_b": "number", "type_c": "number", "type_d": "number" }, "observations": "string" } ``` ## Best Practices 1. Document complete preparation procedure for reproducibility 2. Use consistent magnification within a comparative study 3. Apply appropriate etching time - under-etched is better than over-etched 4. Verify grain boundaries are fully revealed before measurement 5. Use image analysis software for quantitative phase measurements 6. Include representative micrographs in reports ## Integration Points - Connects with Electron Microscopy for high-resolution analysis - Feeds into Failure Analysis for metallographic investigation - Supports Mechanical Testing for structure-property correlation - Integrates with Heat Treatment Optimization for process development --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/science/materials-science/skills/metallurgical-analysis/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice # MIT License Copyright (c) 2025 A5C AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/science/materials-science/skills/metallurgical-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Product Management Roles: Data Analyst, Product Manager, Researcher Specialties: Specialty not specified Tools: Clemex, Imagej, Astm E45, Astm E112 Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 99e4f5a6650fae69d287871d5b0c92a273f7b6b16a6e1f1eead087027e76cff4