Research-Stack/5-Applications/text-to-cad/models/material_bounded_merkle_jack.py

"""
Material-Bounded Merkle Jack - Realistic structure using Research Stack mathematics.

This model uses FAMM frustration physics, manifold-generalized Bernoulli equations,
and String-Star Manifold concepts to find OPTIMAL geometry, not impossible dynamic adaptation.

Key differences from adaptive model:
- No instantaneous geometry changes (physically impossible)
- Elastic deformation only (Hooke's Law)
- Yield strength enforcement
- Fatigue life analysis
- Manufacturing feasibility constraints
- Optimization-based design instead of adaptation

Mathematical Frameworks:
- FAMM: Minimize frustration in optimal geometry design
- Manifold-generalized Bernoulli: Optimal load distribution in design phase
- String-Star Manifold: Curvature-aware geometry optimization
- Scale Space: Multi-scale optimization for manufacturing
"""

import json
import numpy as np
import math
from typing import List, Tuple, Dict, Any, Optional
from dataclasses import dataclass

@dataclass
class MaterialProperties:
    """Real material properties with physical constraints.
    
    SLS Nylon PA12 (Selective Laser Sintering):
    - Lower modulus and strength than steel
    - Anisotropic due to layer orientation
    - Porosity affects properties
    """
    youngs_modulus: float = 1.7e9  # Pa (SLS PA12 nylon)
    yield_strength: float = 48e6   # Pa (SLS PA12)
    ultimate_strength: float = 52e6  # Pa (SLS PA12)
    shear_modulus: float = 0.6e9   # Pa (SLS PA12)
    poisson_ratio: float = 0.4
    density: float = 930  # kg/m³ (SLS PA12)
    fatigue_strength_coefficient: float = 0.3  # S-N curve coefficient (lower for polymers)
    fatigue_exponent: float = -0.12  # S-N curve exponent
    porosity: float = 0.03  # 3% porosity typical for SLS
    anisotropy_factor: float = 0.8  # Strength reduction in weak direction

@dataclass
class ManufacturingConstraints:
    """Manufacturing feasibility constraints for SLS."""
    min_tubule_radius: float = 0.8e-3  # 0.8 mm minimum (SLS powder size limit)
    max_tubule_radius: float = 10.0e-3  # 10 mm maximum (SLS build volume)
    min_branch_angle: float = 45.0  # degrees (SLS overhang limit ~45°)
    max_branch_angle: float = 60.0  # degrees
    min_feature_size: float = 0.6e-3  # 0.6 mm (SLS powder size ~60µm)
    max_aspect_ratio: float = 5.0  # length/radius ratio (SLS support limited)
    layer_thickness: float = 0.1e-3  # 0.1 mm SLS layer thickness
    surface_roughness: float = 15e-6  # 15 µm Ra (SLS typical)
    min_wall_thickness: float = 0.8e-3  # 0.8 mm (SLS minimum)
    build_direction: str = "vertical"  # SLS build orientation

@dataclass
class LoadCondition:
    """Expected load condition for design optimization."""
    name: str
    load_type: str  # 'compression', 'tension', 'shear', 'torsion'
    magnitude: float  # N or N·m
    direction: Tuple[float, float, float]
    probability: float  # Probability of occurrence (0-1)
    cycles: int  # Expected load cycles for fatigue

class MaterialBoundedMerkleJack:
    """Material-bounded Merkle Jack with realistic physics constraints."""
    
    def __init__(self, geometry_file: str):
        """Load initial geometry."""
        with open(geometry_file, 'r') as f:
            data = json.load(f)
        
        self.nodes = data['nodes']
        self.edges = data['edges']
        self.params = data['parameters']
        
        # Build node lookup
        self.node_map = {n['id']: n for n in self.nodes}
        
        # Material properties
        self.material = MaterialProperties()
        
        # Manufacturing constraints
        self.manufacturing = ManufacturingConstraints()
        
        # Convert to meters
        self.tubule_radius = self.params['tubule_radius'] / 1000.0
        self.cross_sectional_area = math.pi * self.tubule_radius**2
        
        # Elastic deformation state
        self.elastic_deformation = {}
        self.residual_stress = {}
        
        # Fatigue damage accumulation
        self.fatigue_damage = {}
    
    def calculate_elastic_deformation(self, stress: float, edge_length: float) -> float:
        """
        Calculate elastic deformation using Hooke's Law.
        
        σ = E * ε → ε = σ / E
        ΔL = ε * L = (σ / E) * L
        """
        strain = stress / self.material.youngs_modulus
        deformation = strain * edge_length
        return deformation
    
    def check_yield_criterion(self, stress: float) -> bool:
        """Check if stress exceeds yield strength."""
        return stress <= self.material.yield_strength
    
    def calculate_von_mises_stress(self, axial_stress: float, shear_stress: float = 0) -> float:
        """Calculate Von Mises stress."""
        return math.sqrt(axial_stress**2 + 3 * shear_stress**2)
    
    def calculate_fatigue_life(self, stress_amplitude: float, mean_stress: float = 0) -> int:
        """
        Calculate fatigue life using S-N curve with Goodman correction.
        
        N = (σ_e / σ_a)^(1/b) with Goodman mean stress correction
        """
        # Endurance limit (typical for steel: 0.5 * ultimate strength)
        endurance_limit = self.material.fatigue_strength_coefficient * self.material.ultimate_strength
        
        # Goodman mean stress correction
        stress_ratio = mean_stress / self.material.ultimate_strength if self.material.ultimate_strength > 0 else 0
        effective_amplitude = stress_amplitude / (1 - stress_ratio)
        
        # S-N curve: N = (σ_e / σ_a)^(1/b)
        if effective_amplitude > 0 and effective_amplitude < endurance_limit:
            cycles = int((endurance_limit / effective_amplitude) ** (1 / abs(self.material.fatigue_exponent)))
        else:
            cycles = 0  # Immediate failure
        
        return cycles
    
    def check_manufacturing_feasibility(self, radius: float, angle_deg: float) -> Tuple[bool, List[str]]:
        """Check if geometry is manufacturable."""
        issues = []
        
        # Check radius constraints
        if radius < self.manufacturing.min_tubule_radius:
            issues.append(f"Radius {radius*1000:.2f} mm below minimum {self.manufacturing.min_tubule_radius*1000:.2f} mm")
        if radius > self.manufacturing.max_tubule_radius:
            issues.append(f"Radius {radius*1000:.2f} mm above maximum {self.manufacturing.max_tubule_radius*1000:.2f} mm")
        
        # Check angle constraints
        if angle_deg < self.manufacturing.min_branch_angle:
            issues.append(f"Branch angle {angle_deg:.1f}° below minimum {self.manufacturing.min_branch_angle:.1f}°")
        if angle_deg > self.manufacturing.max_branch_angle:
            issues.append(f"Branch angle {angle_deg:.1f}° above maximum {self.manufacturing.max_branch_angle:.1f}°")
        
        # Check aspect ratio
        edge_lengths = [self.calculate_edge_length(edge) for edge in self.edges]
        for length in edge_lengths:
            aspect_ratio = length / radius if radius > 0 else float('inf')
            if aspect_ratio > self.manufacturing.max_aspect_ratio:
                issues.append(f"Aspect ratio {aspect_ratio:.1f} exceeds maximum {self.manufacturing.max_aspect_ratio}")
        
        return len(issues) == 0, issues
    
    def calculate_edge_length(self, edge: Tuple[int, int]) -> float:
        """Calculate edge length in meters."""
        p_id, c_id = edge
        parent = self.node_map[p_id]
        child = self.node_map[c_id]
        
        dx = (child['x'] - parent['x']) / 1000.0
        dy = (child['y'] - parent['y']) / 1000.0
        dz = (child['z'] - parent['z']) / 1000.0
        
        return math.sqrt(dx**2 + dy**2 + dz**2)
    
    def optimize_geometry_for_loads(self, load_conditions: List[LoadCondition]) -> Dict[str, Any]:
        """
        Optimize geometry for multiple load conditions using FAMM and manifold math.
        
        This finds the BEST INITIAL geometry, not dynamic adaptation.
        """
        print(f"\n{'='*70}")
        print(f"OPTIMIZING GEOMETRY FOR {len(load_conditions)} LOAD CONDITIONS")
        print(f"{'='*70}")
        
        # Current geometry evaluation
        current_evaluation = self.evaluate_all_loads(load_conditions)
        
        print(f"\nCurrent Geometry Performance:")
        print(f"  Max stress: {current_evaluation['max_stress']/1e6:.2f} MPa")
        print(f"  Safety factor: {current_evaluation['min_safety_factor']:.2f}")
        print(f"  Fatigue life: {min(current_evaluation['fatigue_lives']) if current_evaluation['fatigue_lives'] else 'N/A'} cycles")
        print(f"  Manufacturing feasible: {current_evaluation['manufacturing_feasible']}")
        
        if not current_evaluation['manufacturing_feasible']:
            print(f"  Manufacturing issues: {len(current_evaluation['manufacturing_issues'])}")
            for issue in current_evaluation['manufacturing_issues'][:3]:
                print(f"    - {issue}")
        
        # Optimization using FAMM frustration minimization
        # Instead of changing geometry dynamically, we find optimal static geometry
        optimal_angles = self.optimize_branch_angles_famm(load_conditions)
        optimal_radius = self.optimize_tubule_radius_manifold(load_conditions)
        
        print(f"\nOptimized Geometry Parameters:")
        print(f"  Branch angles: {[f'{a:.1f}°' for a in optimal_angles]}")
        print(f"  Tubule radius: {optimal_radius*1000:.2f} mm")
        
        # Evaluate optimized geometry
        old_params = self.params['branch_angles'][:]
        old_radius = self.tubule_radius
        
        self.params['branch_angles'] = optimal_angles
        self.tubule_radius = optimal_radius
        self.cross_sectional_area = math.pi * optimal_radius**2
        
        optimized_evaluation = self.evaluate_all_loads(load_conditions)
        
        print(f"\nOptimized Geometry Performance:")
        print(f"  Max stress: {optimized_evaluation['max_stress']/1e6:.2f} MPa")
        print(f"  Safety factor: {optimized_evaluation['min_safety_factor']:.2f}")
        print(f"  Fatigue life: {min(optimized_evaluation['fatigue_lives']) if optimized_evaluation['fatigue_lives'] else 'N/A'} cycles")
        print(f"  Manufacturing feasible: {optimized_evaluation['manufacturing_feasible']}")
        
        if not optimized_evaluation['manufacturing_feasible']:
            print(f"  Manufacturing issues: {len(optimized_evaluation['manufacturing_issues'])}")
        
        # Calculate improvement
        stress_reduction = (current_evaluation['max_stress'] - optimized_evaluation['max_stress']) / current_evaluation['max_stress']
        sf_improvement = (optimized_evaluation['min_safety_factor'] - current_evaluation['min_safety_factor']) / current_evaluation['min_safety_factor']
        
        print(f"\nOptimization Results:")
        print(f"  Stress reduction: {stress_reduction*100:.1f}%")
        print(f"  Safety factor improvement: {sf_improvement*100:.1f}%")
        
        # Restore original parameters
        self.params['branch_angles'] = old_params
        self.tubule_radius = old_radius
        self.cross_sectional_area = math.pi * old_radius**2
        
        return {
            'current': current_evaluation,
            'optimized': optimized_evaluation,
            'optimal_angles': optimal_angles,
            'optimal_radius': optimal_radius,
            'improvement': {
                'stress_reduction': stress_reduction,
                'sf_improvement': sf_improvement
            }
        }
    
    def optimize_branch_angles_famm(self, load_conditions: List[LoadCondition]) -> List[float]:
        """
        Optimize branch angles using FAMM frustration minimization.
        
        Find angles that minimize frustration across all expected loads.
        """
        # Current angles
        angles = list(self.params['branch_angles'])
        
        # Simple gradient descent on angle space
        best_angles = angles[:]
        best_frustration = float('inf')
        
        # Search space: ±15 degrees around current angles
        search_range = 15.0  # degrees
        step_size = 5.0  # degrees
        
        for i in range(len(angles)):
            test_angles = angles[:]
            
            for delta in np.arange(-search_range, search_range + step_size, step_size):
                test_angle = angles[i] + delta
                
                # Clamp to manufacturing constraints
                test_angle = max(self.manufacturing.min_branch_angle, 
                               min(self.manufacturing.max_branch_angle, test_angle))
                
                test_angles[i] = test_angle
                
                # Calculate frustration for this configuration
                frustration = self.calculate_total_frustration(test_angles, load_conditions)
                
                if frustration < best_frustration:
                    best_frustration = frustration
                    best_angles[i] = test_angle
        
        return best_angles
    
    def calculate_total_frustration(self, angles: List[float], load_conditions: List[LoadCondition]) -> float:
        """Calculate total FAMM frustration across all load conditions."""
        total_frustration = 0.0
        
        # Temporarily set angles
        old_angles = self.params['branch_angles'][:]
        self.params['branch_angles'] = angles
        
        for load in load_conditions:
            # Calculate stress distribution for this load
            stresses = self.calculate_stress_for_load(load)
            
            # Calculate frustration
            if stresses:
                mean_stress = np.mean(list(stresses.values()))
                if mean_stress > 0:
                    for stress in stresses.values():
                        total_frustration += abs(stress - mean_stress) / mean_stress
        
        # Restore angles
        self.params['branch_angles'] = old_angles
        
        return total_frustration / len(load_conditions) if load_conditions else 0
    
    def optimize_tubule_radius_manifold(self, load_conditions: List[LoadCondition]) -> float:
        """
        Optimize tubule radius using manifold-generalized Bernoulli.
        
        Find radius that balances stress across manifold curvature.
        """
        # Current radius
        radius = self.tubule_radius
        
        # Search space: ±50% around current radius
        search_min = max(self.manufacturing.min_tubule_radius, radius * 0.5)
        search_max = min(self.manufacturing.max_tubule_radius, radius * 1.5)
        
        best_radius = radius
        best_stress = float('inf')
        
        # Test different radii
        for test_radius in np.linspace(search_min, search_max, 20):
            # Temporarily set radius
            old_radius = self.tubule_radius
            old_area = self.cross_sectional_area
            self.tubule_radius = test_radius
            self.cross_sectional_area = math.pi * test_radius**2
            
            # Calculate max stress across all loads
            max_stress = 0
            for load in load_conditions:
                stresses = self.calculate_stress_for_load(load)
                if stresses:
                    max_stress = max(max_stress, max(stresses.values()))
            
            if max_stress < best_stress:
                best_stress = max_stress
                best_radius = test_radius
            
            # Restore radius
            self.tubule_radius = old_radius
            self.cross_sectional_area = old_area
        
        return best_radius
    
    def calculate_merkle_reinforcement_factor(self, edge: Tuple[int, int]) -> float:
        """
        Calculate strain reinforcement factor from merkle tree topology.
        
        The merkle tree provides strain reinforcement through frustration physics:
        - When an edge is loaded, strain propagates through the tree
        - Sibling edges share load due to frustration minimization
        - Deeper nodes benefit from more load sharing paths
        - Branching factor determines reinforcement strength
        
        Reinforcement factor R = 1 + (branching_factor - 1) * (depth / max_depth)
        """
        p_id, c_id = edge
        child = self.node_map[c_id]
        depth = child['depth']
        max_depth = self.params['depth']
        branching_factor = self.params['branching_factor']
        
        # Base reinforcement from branching
        # More branches = more load sharing = higher reinforcement
        base_reinforcement = 1 + (branching_factor - 1) * 0.5
        
        # Depth-dependent reinforcement
        # Deeper nodes have more load sharing paths through the tree
        depth_factor = depth / max_depth if max_depth > 0 else 0
        
        # Total reinforcement factor
        # R = base * depth_factor + 1 (minimum reinforcement of 1)
        reinforcement = 1 + base_reinforcement * depth_factor * 0.3
        
        return reinforcement
    
    def calculate_stress_for_load(self, load: LoadCondition) -> Dict[Tuple[int, int], float]:
        """Calculate stress distribution for a specific load condition with SLS effects and merkle reinforcement."""
        stresses = {}
        
        # Effective area accounting for porosity
        effective_area = self.cross_sectional_area * (1 - self.material.porosity)
        
        # Anisotropy factor based on build direction
        anisotropy = self.material.anisotropy_factor
        
        # Calculate stress for each edge with merkle reinforcement
        for edge in self.edges:
            base_stress = 0
            
            if load.load_type == 'tension':
                if edge[0] == 0:
                    base_stress = load.magnitude / effective_area
                else:
                    base_stress = load.magnitude * 0.5 / effective_area
                # Tension is sensitive to anisotropy
                base_stress /= anisotropy
            elif load.load_type == 'compression':
                base_stress = load.magnitude * 0.1 / effective_area
                # Compression less sensitive to anisotropy
                base_stress /= (anisotropy * 0.9 + 0.1)
            elif load.load_type == 'shear':
                base_stress = load.magnitude * 0.2 / effective_area
                # Shear highly sensitive to anisotropy
                base_stress /= anisotropy
            elif load.load_type == 'torsion':
                p_id, c_id = edge
                child = self.node_map[c_id]
                r = math.sqrt(child['x']**2 + child['y']**2) / 1000.0
                base_stress = load.magnitude * r / effective_area
                # Torsion sensitive to anisotropy
                base_stress /= anisotropy
            
            # Apply merkle topology strain reinforcement
            # The tree structure provides load sharing through frustration physics
            reinforcement = self.calculate_merkle_reinforcement_factor(edge)
            base_stress /= reinforcement
            
            stresses[tuple(edge)] = base_stress
        
        return stresses
    
    def evaluate_all_loads(self, load_conditions: List[LoadCondition]) -> Dict[str, Any]:
        """Evaluate geometry under all load conditions."""
        max_stress = 0
        min_safety_factor = float('inf')
        fatigue_lives = []
        
        for load in load_conditions:
            stresses = self.calculate_stress_for_load(load)
            
            if stresses:
                load_max_stress = max(stresses.values())
                max_stress = max(max_stress, load_max_stress)
                
                safety_factor = self.material.yield_strength / load_max_stress
                min_safety_factor = min(min_safety_factor, safety_factor)
                
                # Calculate fatigue life
                fatigue_life = self.calculate_fatigue_life(load_max_stress * 0.5, load_max_stress * 0.1)
                fatigue_lives.append(fatigue_life)
        
        # Check manufacturing feasibility
        feasible, issues = self.check_manufacturing_feasibility(self.tubule_radius, 
                                                           self.params['branch_angles'][0])
        
        return {
            'max_stress': max_stress,
            'min_safety_factor': min_safety_factor,
            'fatigue_lives': fatigue_lives,
            'manufacturing_feasible': feasible,
            'manufacturing_issues': issues
        }
    
    def generate_design_report(self, optimization_result: Dict[str, Any], output_file: str):
        """Generate design optimization report."""
        with open(output_file, 'w') as f:
            f.write("# Material-Bounded Merkle Jack Design Report\n\n")
            f.write(f"Generated: {__import__('datetime').datetime.now()}\n\n")
            
            f.write("## Material Properties\n\n")
            f.write(f"- Material: SLS Nylon PA12 (Selective Laser Sintering)\n")
            f.write(f"- Young's Modulus: {self.material.youngs_modulus/1e9:.1f} GPa\n")
            f.write(f"- Yield Strength: {self.material.yield_strength/1e6:.1f} MPa\n")
            f.write(f"- Ultimate Strength: {self.material.ultimate_strength/1e6:.1f} MPa\n")
            f.write(f"- Density: {self.material.density} kg/m³\n")
            f.write(f"- Porosity: {self.material.porosity*100:.1f}%\n")
            f.write(f"- Anisotropy Factor: {self.material.anisotropy_factor}\n\n")
            
            f.write("## Manufacturing Constraints\n\n")
            f.write(f"- Min/Max Tubule Radius: {self.manufacturing.min_tubule_radius*1000:.1f} / {self.manufacturing.max_tubule_radius*1000:.1f} mm\n")
            f.write(f"- Min/Max Branch Angle: {self.manufacturing.min_branch_angle:.1f}° / {self.manufacturing.max_branch_angle:.1f}°\n")
            f.write(f"- Max Aspect Ratio: {self.manufacturing.max_aspect_ratio}\n\n")
            
            f.write("## Optimization Results\n\n")
            f.write(f"**Stress Reduction:** {optimization_result['improvement']['stress_reduction']*100:.1f}%\n")
            f.write(f"**Safety Factor Improvement:** {optimization_result['improvement']['sf_improvement']*100:.1f}%\n\n")
            
            f.write("### Recommended Geometry\n\n")
            f.write(f"- Branch Angles: {[f'{a:.1f}°' for a in optimization_result['optimal_angles']]}\n")
            f.write(f"- Tubule Radius: {optimization_result['optimal_radius']*1000:.2f} mm\n\n")
            
            f.write("### Performance Comparison\n\n")
            f.write("| Metric | Current | Optimized |\n")
            f.write("|--------|---------|----------|\n")
            f.write(f"| Max Stress | {optimization_result['current']['max_stress']/1e6:.2f} MPa | {optimization_result['optimized']['max_stress']/1e6:.2f} MPa |\n")
            f.write(f"| Safety Factor | {optimization_result['current']['min_safety_factor']:.2f} | {optimization_result['optimized']['min_safety_factor']:.2f} |\n")
            f.write(f"| Fatigue Life | {min(optimization_result['current']['fatigue_lives']) if optimization_result['current']['fatigue_lives'] else 'N/A'} | {min(optimization_result['optimized']['fatigue_lives']) if optimization_result['optimized']['fatigue_lives'] else 'N/A'} |\n")
            f.write(f"| Manufacturing Feasible | {'Yes' if optimization_result['current']['manufacturing_feasible'] else 'No'} | {'Yes' if optimization_result['optimized']['manufacturing_feasible'] else 'No'} |\n\n")
            
            if not optimization_result['optimized']['manufacturing_feasible']:
                f.write("### Manufacturing Issues\n\n")
                for issue in optimization_result['optimized']['manufacturing_issues']:
                    f.write(f"- {issue}\n")
            
            f.write("## Key Differences from Adaptive Model\n\n")
            f.write("- **No instantaneous geometry changes** - uses optimal static geometry\n")
            f.write("- **Elastic deformation only** - obeys Hooke's Law\n")
            f.write("- **Yield strength enforcement** - prevents plastic deformation\n")
            f.write("- **Fatigue life analysis** - accounts for cyclic loading\n")
            f.write("- **Manufacturing constraints** - realistic production limits\n")
            f.write("- **Optimization-based design** - finds best initial configuration\n")
            f.write("- **Merkle topology strain reinforcement** - load sharing through frustration physics\n\n")
            
            f.write("## Merkle Topology Strain Reinforcement\n\n")
            f.write("The merkle tree structure provides strain reinforcement through FAMM frustration physics:\n")
            f.write("- When an edge is loaded, strain propagates through the tree\n")
            f.write("- Sibling edges share load due to frustration minimization\n")
            f.write("- Deeper nodes benefit from more load sharing paths\n")
            f.write("- Branching factor determines reinforcement strength\n\n")
            f.write(f"- Branching Factor: {self.params['branching_factor']}\n")
            f.write(f"- Tree Depth: {self.params['depth']}\n")
            f.write(f"- Max Reinforcement Factor: {1 + (self.params['branching_factor'] - 1) * 0.5 * 0.3:.2f}x\n\n")
            
            if optimization_result['optimized']['min_safety_factor'] >= 1.5:
                f.write("✅ **DESIGN SAFE** - Meets safety requirements\n")
            elif optimization_result['optimized']['min_safety_factor'] >= 1.0:
                f.write("⚠️ **DESIGN MARGINAL** - Low safety margin\n")
            else:
                f.write("❌ **DESIGN UNSAFE** - Exceeds yield strength\n")
        
        print(f"\nDesign report generated: {output_file}")

if __name__ == "__main__":
    geometry_file = "/home/allaun/Documents/Research Stack/5-Applications/text-to-cad/models/merkle_jack.json"
    output_report = "/home/allaun/Documents/Research Stack/5-Applications/text-to-cad/models/material_bounded_design_report.md"
    
    print("Initializing Material-Bounded Merkle Jack...")
    jack = MaterialBoundedMerkleJack(geometry_file)
    
    print(f"Loaded {len(jack.nodes)} nodes and {len(jack.edges)} edges")
    print("Using material physics constraints: Hooke's Law, yield strength, fatigue, manufacturing limits")
    
    # Define expected load conditions
    load_conditions = [
        LoadCondition("Compression - Static", "compression", 10000.0, (0, 0, -1), 0.5, 1000),
        LoadCondition("Tension - Uplift", "tension", 5000.0, (0, 0, 1), 0.3, 10000),
        LoadCondition("Shear - Wind", "shear", 3000.0, (0, 1, 0), 0.4, 5000),
        LoadCondition("Torsion - Twist", "torsion", 500.0, (0, 0, 1), 0.1, 1000),
    ]
    
    print(f"\nOptimizing for {len(load_conditions)} expected load conditions...")
    
    # Optimize geometry
    optimization_result = jack.optimize_geometry_for_loads(load_conditions)
    
    # Generate report
    jack.generate_design_report(optimization_result, output_report)
    
    print("\nMaterial-bounded design optimization complete!")
    print("This model uses realistic material physics and manufacturing constraints.")