3D Print Quality Optimization

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(777)

# 3D printing parameters
n_prints = 120

layer_height = np.random.uniform(0.1, 0.4, n_prints)
print_speed = np.random.uniform(30, 100, n_prints)
temperature = np.random.uniform(190, 230, n_prints)

# Quality score
quality = 100 - layer_height * 50 - (print_speed - 50)**2 / 100 - np.abs(temperature - 210) * 0.5
quality += np.random.normal(0, 5, n_prints)
quality = np.clip(quality, 40, 100)

# Grade classification
grade = np.where(quality > 85, 'Excellent', np.where(quality > 70, 'Good', 'Poor'))
grade_colors = {'Excellent': '#6CF527', 'Good': '#27D3F5', 'Poor': '#F5276C'}
colors = [grade_colors[g] for g in grade]

fig = plt.figure(figsize=(10, 8), facecolor='#ffffff')
ax = fig.add_subplot(111, projection='3d', facecolor='#ffffff')

ax.scatter(layer_height, print_speed, temperature, c=colors, s=quality*0.8, 
           alpha=0.7, edgecolors='#374151', linewidths=0.3)

ax.set_xlabel('Layer Height (mm)', color='#1f2937', fontsize=10)
ax.set_ylabel('Print Speed (mm/s)', color='#1f2937', fontsize=10)
ax.set_zlabel('Temperature (°C)', color='#1f2937', fontsize=10)
ax.set_title('3D Print Quality Optimization', color='#1f2937', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#6b7280', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#e5e7eb')
ax.yaxis.pane.set_edgecolor('#e5e7eb')
ax.zaxis.pane.set_edgecolor('#e5e7eb')

ax.view_init(elev=20, azim=45)
plt.tight_layout()
plt.show()