Overview

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Overview

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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.

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The graph to the right describes exemplary the relation among a part’s properties, blackboxes, and scores.

• Each part is made up of a number of properties and corresponding values.

• Blackboxes are small functions that evaluate one or several part properties by reading their values and calculating a result.

• The blackbox results are fed into scores where they are aggregated and produce a score result.

Calculating an Econ Score

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• 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

By convention, the evaluation of the potential results lie between 0 and 1, whereby 0 indicates poor suitability and 1 perfect suitability. The results in the [0, 1] interval are displayed as percentage values from 0% to 100% in the user interface.

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In the above example, the blackbox lead time savings has the lowest impact on the econ score.

The results of the score calculation are displayed for each part and each score in an overview:

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• Factor
  The blackbox - and thus the corresponding properties - that are considered for the score

• Score
  The individual result which is calculated by the corresponding blackbox - without applying weights.

• Impact
  The weight, or impact, this blackbox should have in the overall calculation

• Subscore
  The weighted result of each blackbox how it is considered for the overall score calculation

Calculating a Tech Score

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The tech score uses the following parameters:

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Blackbox

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Properties incl. Type

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Weight

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Max Dimension

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• Size (size)

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1.0

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Average Wall Thickness

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• Avg wall thickness (float)

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0.4

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Max Dimension vs. Avg. Wall Thickness

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• Size (size)

• Avg wall thickness (float)

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0.6

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Surface Area vs. Bounding Box Area

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• Surface Area (float)

• Bounding box area (float)

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0.4

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Aspect Ratio

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• size (size)

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1.0

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Waste Ratio

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• Volume (float)

• Bounding box volume (float)

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0.75

The functions seen in the tech score are calculated as follows:

// 1. Max Dimension:

function fMaxDim(maxDim) {
const center = 200; // Center of the Gaussian curve
const deviation = Math.abs(maxDim - center); // Only consider positive deviations
const standardDeviation = 200; // Adjust this value based on desired sensitivity

// Gaussian function to calculate the score
const gaussian = (x) => Math.exp(-Math.pow(deviation,2) / (2 * Math.pow(standardDeviation,2)));

const score = gaussian(maxDim)

return score;
}


//2. Average Wall Thickness
function fAvgWallThickness(avgWallThickness) {
const printableThreshold = 0.7; // Threshold for printable average wall thickness
const sigmoidFactor = 2; // Adjust this factor based on desired sensitivity

const score = 1 / (1 + Math.exp(-(avgWallThickness - printableThreshold) * sigmoidFactor));
return score;
}

//3. average wall thickness & maxDim
function fMaxDimAvgWallThickness(maxDim, avgWallThickness) {
const desiredRatio = 10; // Adjust the desired ratio based on specific requirements

// Calculate the ratio between max dimension and average wall thickness
const ratio = maxDim / avgWallThickness;

// Calculate the score based on the ratio and desired ratio
const score = 1 - Math.abs(ratio - desiredRatio) / desiredRatio;

// Ensure the score is within the range of 0 to 1
return Math.max(0, Math.min(score, 1));
}


// 4. Model Area / Bounding Box Area:
function fSurfaceAreaBoundingBoxArea(x, y, z, surfaceArea, volume) {
const boundingBoxArea = 2 * (x * y + y * z + z * x);
const desiredRatio = 0.9; // Adjust the desired ratio (closer to 1 for higher score)
const sigmoidFactor = 1; // Adjust the sigmoid factor based on desired sensitivity

const ratio = surfaceArea / boundingBoxArea;

// Apply sigmoid function to map the ratio between 0 and 1
const sigmoid = (x) => 1 / (1 + Math.exp(-x * sigmoidFactor));

const score = sigmoid(ratio - desiredRatio);
return ratio;
}

//5. Aspect Ratio
function fAspectRatio(x, y, z) {
const desiredAspectRatio = 1; // Desired aspect ratio
const aspectRatio = Math.max(x, y, z) / Math.min(x, y, z);
const sigma = 2; // Adjust this parameter based on desired sensitivity

// Calculate the bell curve value using the Gaussian distribution formula
const exponent = -(Math.pow(aspectRatio - desiredAspectRatio, 2) / (2 * Math.pow(sigma, 2)));
const bellCurveValue = Math.exp(exponent);

return bellCurveValue;
}

//6. Waste Ratio

function fWasteRatio(volume, boundingBoxVolume) {
const desiredWasteRatio = 0.00; // Desired waste ratio (close to 0)
const sigmoidFactor = 5; // Adjust this factor based on desired sensitivity

// Sigmoid function to map the ratio between 0 and 1
const sigmoid = (x) =>1 - 1 / (1 + Math.exp(-(x - 0.5) * sigmoidFactor));

const ratio = volume / boundingBoxVolume;
const score = sigmoid(ratio);

return score;
}

What do these functions mean?

1. Max Dimension (MaxDim):

   • Summary: Evaluates the printability and structural integrity of a 3D printed part based on its maximum dimension.

   • Explanation: The function calculates a score that reflects how the maximum dimension of a part compares to a desired center value. The score is determined using a Gaussian curve, with a higher score indicating that the part's size is closer to the desired center value. This center value is determined through empirical evaluation that parts that are too small should be penalized and parts that are close to the printer’s maximum are also slightly less viable than those with a reasonable dimension and where multiple of the same part can fit in the printer.

2. Average Wall Thickness (AvgWallThickness):

   • Summary: Assesses the printability of a 3D printed part by considering the average thickness of its walls.

   • Explanation: The function calculates a score based on the average wall thickness of a part. A sigmoid function is used to map the score, with a higher score indicating that the average wall thickness is closer to a printable threshold. This score helps determine whether the part's walls are within a suitable range for successful printing. A lower threshold is riskier in AM while being over-thick isn’t problematic, unless special printing processes take place, such as printing large full metal blocks that can cause issues with thermal stresses.

3. Max Dimension vs. Avg. Wall Thickness (MaxDimAvgWallThickness):

   • Summary: Provides a combined assessment of a part's size and wall thickness to determine its printability and structural stability.

   • Explanation: A higher ratio indicates that the part is relatively larger compared to the thickness of its walls, which can result in thin or unsupported areas. This may pose challenges during printing, as thin walls can be prone to deformation, warping, or structural instability. On the other hand, a lower ratio suggests that the part has a more balanced relationship between its size and wall thickness, which can contribute to better printability and structural integrity. It indicates that the walls are relatively thicker compared to the overall size of the part, providing better stability and strength.

4. Surface Area vs. Bounding Box Area (SurfaceAreaBoundingBoxArea):

   • Summary: Evaluates how efficiently a 3D printed part utilizes its space within the bounding box.

   • Explanation: A higher ratio indicates that the part's surface area is relatively larger compared to its bounding box, suggesting a more complex and intricate geometry. This can be desirable for detailed and intricate 3D prints. On the other hand, a lower ratio indicates that the part's surface area is relatively smaller compared to its bounding box, suggesting a simpler and more solid geometry. This can be advantageous for printing sturdy and robust objects with less intricate details.

5. Aspect Ratio (AspectRatio):

   • Summary: Assesses the balance between the dimensions of a 3D printed part to ensure optimal printability.

   • Explanation: A higher ratio indicates that the part's surface area is relatively larger compared to its bounding box, suggesting a more complex and intricate geometry. This can be desirable for detailed and intricate 3D prints. On the other hand, a lower ratio indicates that the part's surface area is relatively smaller compared to its bounding box, suggesting a simpler and more solid geometry. This can be advantageous for printing sturdy and robust objects with less intricate details.

6. Waste Ratio (WasteRatio):

   • Summary: Evaluates the efficiency of material usage in a 3D printed part.

   • Explanation: Minimizing the waste ratio is desirable in 3D printing as it helps optimize material usage, reduce costs, and reduce environmental impact. Design considerations, such as optimizing support structures, using lattice or infill patterns, and incorporating lightweight designs, can help reduce the waste ratio and improve the overall efficiency of the printing process. Additionally, a high waste ratio for classical manufacture does point towards AM as a solution.

Profiles with Different Weights

It is possible to look at the same part from different angles. Two typical cases for optimization are cost reduction and lead time improvement. It is possible to set up different Econ scores by including different blackboxes or adjusting the weights by which blackboxes impact the overall results. Thus different Profiles can be created that score parts according to different measurements.

It is possible to configure different profiles by which to assess parts:

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Focusing on cost reduction

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part’s potential.

Calculating a Feasibility

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Purpose

The Feasibility Scoring System evaluates multiple technologies for compatibility with specific design requirements, providing a score that represents the best technology choice based on weighted criteria. This allows users to assess which technology most effectively meets their part’s requirements.

Parameter Weights in the Feasibility Scoring System

Each parameter in the Feasibility Scoring System is assigned a specific weight that reflects its importance to the overall manufacturing success. Higher weights indicate parameters that are more critical to determining the feasibility of a technology for a particular design, while lower weights suggest factors that are influential but less decisive.

Key Parameter Weights

1. Material Class (High Weight, 40%):
   Material compatibility is a crucial factor, as it impacts the overall manufacturability and end-use performance of the part. A high weight here means that a technology’s suitability heavily depends on its ability to work with the required material.

2. Size Limitations (Maximum Size) (Moderate Weight, 12%):
   Size constraints are important but secondary, impacting the feasibility based on whether a technology can accommodate the dimensions of the part.

3. Structural Needs (Support Structure) (Moderate Weight, 12%):
   This considers the ability to incorporate or avoid support structures. A 12% weight reflects its moderate impact on determining feasibility.

4. Design-Specific Requirements (Wall Thickness, Holes, Aspect Ratio) (Moderate Weight, 12% each):
   Design-specific factors such as wall thickness, holes, and aspect ratio affect the manufacturability in unique ways based on each technology's limitations. Each of these factors receives an equal weight, collectively contributing significantly to the total score.

Combined Impact

The weighting system ensures that more critical factors like material class contribute substantially to the final score, while also accounting for important but secondary factors like size and specific design features. This balanced approach allows for a comprehensive assessment, focusing on essential requirements without disregarding secondary aspects.

Best Technology Selection

After calculating the feasibility scores for all available technologies, the system identifies and selects the highest-scoring technology as the optimal choice for the given requirements.

Result

The system outputs a final score, reflecting the best technology for the specified parameters, supporting informed decision-making for technology selection.

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