Overview

To understand the main metrics enabling to sort, filter and prioritize parts within the platform, it is important to understand what information is considered and how it is processed to get to a result.

• The potential of the part is related to its economic parameters. We evaluate the current part lead time, cost, potential savings to conclude if an economic opportunity arises.

• The feasibility is related to the technical parameters. We evaluate the part size, material, presence of holes, thin walls, aspect ratio and support structure for additive manufacturing suitability.

• The priority of the part represents a concatenation of the two above metrics. Depending on the result, it is classified as the following:

  • Highest - The part shows a high business opportunity and technical feasibility. The part should be realised.

  • High - The part shows either a high business opportunity or is technical feasible. The part should be considered for realisation but should be further assessed.

  • Medium - The part shows medium business opportunities and feasibly. The part should be further assessed as data might be missing.

  • Low - The part shows either a low business opportunity or is technical not feasible. The part should be put on-hold.

  • Very Low - The part shows a low business opportunity and is technical not feasibility. The part should be rejected.

Calculating the Potential

Overview

This script calculates an economic score for additive manufacturing technologies, considering yearly cost, lead time savings, and cost-saving potential. The final score integrates weighted factors and adjusts based on the number of applications or assemblies the part can be found on.

Methodology

The script evaluates available technologies, filtering out those deemed non-viable. It then calculates economic factors such as yearly cost impact, lead time advantages, and savings potential. These values are weighted, summed, and adjusted to derive a final score.

Economic Factors

1. Yearly Cost Impact – Assesses the total cost based on part price and demand quantity. yearly_cost = current cost of the part in conventional manufacturing x yearly demand

2. Lead Time Savings – Compares manufacturing lead times to determine efficiency gains. lead_time_savings = current lead time of part - AM lead time of part

3. Cost-Saving Potential – Evaluates the financial benefits of switching technologies. cost_savings = current part price in conventional manufacturing - AM part cost

4. Usage Factor – Adjusts the final score based on the number of applications or assemblies a part is found on.

Scoring Process

Each economic factor is weighted and combined into an overall score. An increase is applied based on the breadth of application, ensuring relevance without exceeding a normalised scale.

Application

This scoring system supports decision-making by ranking manufacturing technologies based on economic feasibility. It enables informed choices regarding cost-effectiveness, efficiency, and potential benefits of additive manufacturing solutions.

Calculating the Feasibility

Purpose

The Feasibility Scoring System evaluates multiple manufacturing technologies based on their compatibility with specific design requirements. By applying weighted criteria, the system determines a normalized feasibility score, helping users identify the most suitable technology for their part.

Scoring Methodology and Parameter Weights

Each parameter is assigned a weight that reflects its impact on manufacturability. Higher weights indicate critical factors, while lower weights represent secondary considerations. The final normalized score considers all relevant parameters and adjusts based on part complexity.

Key Parameter Weights

• Material Class (High Impact, 40%)
  Material compatibility is the most critical factor, influencing manufacturability and final part performance.

• Size Limitations (Moderate Impact, 12%)
  Evaluates whether a technology can accommodate the part’s dimensions.

• Structural Needs (Moderate Impact, 12%)
  Considers support structure requirements and their influence on feasibility.

• Design-Specific Factors (Moderate Impact, 12% each)

  • Wall Thickness: Ensures manufacturability within a technology’s constraints.

  • Holes: Evaluates the feasibility of producing internal geometries.

  • Aspect Ratio: Assesses manufacturability of thin or elongated sections.

• Additional Considerations (Lower Impact, 10% each)

  • Accessibility: Determines if post-processing or internal features can be accessed.

  • Trapped Material: Evaluates the likelihood of residual material affecting part functionality.

  • Gaps: Checks for manufacturability of small clearances.

  • Sharp Edges: Considers limitations in resolving fine edge details.

Complexity Adjustment

A complexity factor is applied to adjust scores for highly intricate designs:

• If part complexity is high (>0.5 index) → Score is adjusted down to 90%

• If part complexity is low (≤0.5 index) → Score remains unchanged

Technology Scoring and Selection

• Each technology receives a weighted feasibility score based on its performance across parameters.

• Technologies with critical failures (e.g., material incompatibility or major printability issues) are excluded.

• The normalized feasibility score is calculated across all viable technologies.

• The highest-scoring technology is identified as the most suitable for manufacturing.

Final Output

The system provides a normalized feasibility score (0-100%), helping users make data-driven decisions for selecting the best manufacturing technology.

Calculating the Priority

The priority assesses and classifies the combined results of Potential and Feasibility scores, producing a final status based on predefined conditions. Each score reflects a key aspect of a technology's applicability, and the classification indicates the overall suitability.

Key Steps and Considerations

1. Score Calculation:

   • Both Potential and Feasibility scores are derived from respective evaluation functions, providing values between 0 and 1.

   • Averages are calculated, with missing scores treated as 0 in this specific context, ensuring a fair total even if one score is unavailable.

2. Result Classification:

   • Scores are classified into status icons based on thresholds:

     • Highest: Both scores are 66% or higher.

     • High: One score is 66% or above, and the other is mid-range (33%-66%) or missing.

     • Medium: Both scores fall in the mid-range (33%-66%), or one score is mid-range while the other is missing.

     • Low: One score is below 33% and the other is mid-range or missing.

     • Lowest: Both scores fall below 33%.

   • The classification captures the overall feasibility and potential, even if only partial information is available, guiding decision-making through intuitive status icons.

Final Output

The results are found in the KPI cards found below.