Anto01
2b3573ec42
Major additions: - Training data export system for AtomizerField neural network training - Bracket stiffness optimization study with 50+ training samples - Intelligent NX model discovery (auto-detect solutions, expressions, mesh) - Result extractors module for displacement, stress, frequency, mass - User-generated NX journals for advanced workflows - Archive structure for legacy scripts and test outputs - Protocol documentation and dashboard launcher 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
"""
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Create circular_plate_frequency_tuning_V2 study with ALL fixes applied.
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Improvements:
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- Proper study naming
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- Reports go to 3_reports/ folder
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- Reports discuss actual goal (115 Hz target)
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- Fixed objective function
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"""
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from pathlib import Path
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import sys
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import argparse
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sys.path.insert(0, str(Path(__file__).parent))
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from optimization_engine.hybrid_study_creator import HybridStudyCreator
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def main():
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parser = argparse.ArgumentParser(description='Create circular plate frequency tuning study')
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parser.add_argument('--study-name', default='circular_plate_frequency_tuning_V2',
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help='Name of the study folder')
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args = parser.parse_args()
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study_name = args.study_name
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creator = HybridStudyCreator()
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# Create workflow JSON
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import json
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import tempfile
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workflow = {
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"study_name": study_name,
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"optimization_request": "Tune the first natural frequency mode to exactly 115 Hz (within 0.1 Hz tolerance)",
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"design_variables": [
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{"parameter": "inner_diameter", "bounds": [50, 150]},
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{"parameter": "plate_thickness", "bounds": [2, 10]}
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],
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"objectives": [{
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"name": "frequency_error",
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"goal": "minimize",
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"extraction": {
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"action": "extract_first_natural_frequency",
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"params": {"mode_number": 1, "target_frequency": 115.0}
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}
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}],
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"constraints": [{
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"name": "frequency_tolerance",
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"type": "less_than",
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"threshold": 0.1
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}]
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}
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# Write to temp file
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temp_workflow = Path(tempfile.gettempdir()) / f"{study_name}_workflow.json"
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with open(temp_workflow, 'w') as f:
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json.dump(workflow, f, indent=2)
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# Create study
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study_dir = creator.create_from_workflow(
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workflow_json_path=temp_workflow,
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model_files={
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'prt': Path("examples/Models/Circular Plate/Circular_Plate.prt"),
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'sim': Path("examples/Models/Circular Plate/Circular_Plate_sim1.sim"),
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'fem': Path("examples/Models/Circular Plate/Circular_Plate_fem1.fem"),
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'fem_i': Path("examples/Models/Circular Plate/Circular_Plate_fem1_i.prt")
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},
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study_name=study_name
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)
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print()
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print("=" * 80)
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print(f"[OK] Study created: {study_name}")
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print("=" * 80)
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print()
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print(f"Location: {study_dir}")
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print()
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print("Structure:")
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print(" - 1_setup/: Model files and configuration")
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print(" - 2_results/: Optimization history and database")
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print(" - 3_reports/: Human-readable reports with graphs")
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print()
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print("To run optimization:")
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print(f" python {study_dir}/run_optimization.py")
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print()
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if __name__ == "__main__":
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main()
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