Ashby Plot Analysis: Young's Modulus vs Density – A Guide to Smart Material Selection

                                                                Figure 1

    An Ashby plot is one of the most powerful visualization tools in materials engineering. By plotting Young’s Modulus (stiffness) against Density (mass per volume) on logarithmic scales, we can quickly compare how different materials perform when both rigidity and weight matter.In the plot above, we compare five specific alloys:

• Magnesium Alloy AZ91E-T6
• Semi-red Brass C84200
• Tin Bronze C91100
• Copper Alloy C19200
• JIS SUH35 (a heat-resistant stainless steel)

Key Insights:

• Density significantly affects the specific stiffness (E/ρ) of a material. Materials that sit higher and more to the left on the chart offer better stiffness per unit weight.
• Magnesium Alloy AZ91E-T6 typically shows the lowest density among the group. This makes it attractive for applications where weight saving is critical (e.g., automotive, aerospace, or portable electronics), even if its absolute stiffness is lower than steels or copper alloys.
• Copper-based alloys (Semi-red Brass, Tin Bronze, and Copper Alloy C19200) generally have higher density but also respectable Young’s Modulus. They are preferred when high corrosion resistance, thermal conductivity, or wear resistance is needed alongside moderate stiffness.
• JIS SUH35 (a stainless steel variant) likely sits in the higher stiffness and higher density region, making it suitable for high-temperature or high-strength applications where weight is less critical.

Engineering Takeaway:When designing a component, engineers rarely look for the “strongest” or “stiffest” material in absolute terms. Instead, they optimize for performance indices such as E/ρ (for tension-loaded lightweight stiff structures) or E^{1/3}/ρ (for bending stiffness with minimum weight).

• If your design is weight-sensitive → favor materials toward the top-left (like magnesium alloys).
• If your design requires high absolute stiffness and can tolerate higher weight → copper alloys or steels become more competitive.
• Trade-offs always exist: lower density materials often come with lower absolute strength, higher cost, or processing challenges.

This Ashby plot helps visualize these trade-offs at a glance, enabling faster and more informed material selection during the early design stage.

    An Ashby plot is one of the most powerful visualization tools in materials engineering. By plotting Young’s Modulus (stiffness) against Density (mass per volume) on logarithmic scales, we can quickly compare how different materials perform when both rigidity and weight matter.

In the plot above, we compare five specific alloys:

• Magnesium Alloy AZ91E-T6
• Semi-red Brass C84200
• Tin Bronze C91100
• Copper Alloy C19200
• JIS SUH35 (a heat-resistant stainless steel)

Key Insights:

• Density significantly affects the specific stiffness (E/ρ) of a material. Materials that sit higher and more to the left on the chart offer better stiffness per unit weight.
• Magnesium Alloy AZ91E-T6 typically shows the lowest density among the group. This makes it attractive for applications where weight saving is critical (e.g., automotive, aerospace, or portable electronics), even if its absolute stiffness is lower than steels or copper alloys.
• Copper-based alloys (Semi-red Brass, Tin Bronze, and Copper Alloy C19200) generally have higher density but also respectable Young’s Modulus. They are preferred when high corrosion resistance, thermal conductivity, or wear resistance is needed alongside moderate stiffness.
• JIS SUH35 (a stainless steel variant) likely sits in the higher stiffness and higher density region, making it suitable for high-temperature or high-strength applications where weight is less critical.

Engineering Takeaway:When designing a component, engineers rarely look for the “strongest” or “stiffest” material in absolute terms. Instead, they optimize for performance indices such as E/ρ (for tension-loaded lightweight stiff structures) or E^{1/3}/ρ (for bending stiffness with minimum weight).

• If your design is weight-sensitive → favor materials toward the top-left (like magnesium alloys).
• If your design requires high absolute stiffness and can tolerate higher weight → copper alloys or steels become more competitive.
• Trade-offs always exist: lower density materials often come with lower absolute strength, higher cost, or processing challenges.

This Ashby plot helps visualize these trade-offs at a glance, enabling faster and more informed material selection during the early design stage.


Python Code
import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport os
# ================== Kaggle Setup ==================os.environ['KAGGLE_USERNAME'] = "XXXXXXXXXX"os.environ['KAGGLE_KEY'] = "KGAT_xxxxxxxxxxxxxxxxxxxxxxxxxxxx"
import kaggle
# Download dataset (run once)print("Downloading dataset...")kaggle.api.dataset_download_files('purushottamnawale/materials',                                   path='./materials_data',                                   unzip=True)
# Load the datadata_dir = './materials_data'csv_files = [f for f in os.listdir(data_dir) if f.endswith('.csv')]csv_path = os.path.join(data_dir, csv_files[0])
df = pd.read_csv(csv_path)
# ================== Fix Column Names ==================df = df.rename(columns={    "Material": "Material",    "E": "E",                       # Young's Modulus    "Young's Modulus": "E",    "Ro": "Density",                # Density column in this dataset    "Density": "Density"})
# Convert to numericdf['E'] = pd.to_numeric(df['E'], errors='coerce')df['Density'] = pd.to_numeric(df['Density'], errors='coerce')
# Clean datadf_clean = df.dropna(subset=['E', 'Density']).copy()
# Convert MPa to GPa if neededif df_clean['E'].max() > 1000:    df_clean['E'] = df_clean['E'] / 1000
# ================== Select Exactly 5 Materials ==================# Change these 5 material names as you likefive_materials = [    'Magnesium Alloy AZ91E-T6',    'Semi-red Brass C84200',    'Tin Bronze C91100',    'Copper Alloy C19200',    'JIS SUH35']
# Filter the 5 materialsselected = df_clean[df_clean['Material'].isin(five_materials)].copy()
print("\nMaterials being plotted:")print(selected[['Material', 'Density', 'E']])
# ================== Plot Ashby Chart for 5 Materials ==================plt.figure(figsize=(12, 9))
# Scatter plotplt.scatter(selected['Density'], selected['E'],             s=180, alpha=0.9, edgecolors='black', linewidth=1.5, color='royalblue')
# Add labels for each pointfor i, row in selected.iterrows():    plt.annotate(row['Material'],                  xy=(row['Density'], row['E']),                 xytext=(15, 10),                 textcoords='offset points',                 fontsize=11,                 fontweight='bold')
# Log scales (standard for Ashby plots)plt.xscale('log')plt.yscale('log')
# Labels and Titleplt.xlabel('Density (kg/m³)', fontsize=13, fontweight='bold')plt.ylabel("Young's Modulus E (GPa)", fontsize=13, fontweight='bold')plt.title("Ashby Plot: Young's Modulus vs Density\n(5 Selected Materials)",           fontsize=16, fontweight='bold', pad=20)
plt.grid(True, which="both", ls="--", alpha=0.7)
plt.tight_layout()plt.show()