Overview

AMPI offers two main concepts for doing calculations: Scores and Blackboxes. Blackboxes are meant to evaluate one or several part Properties. That means a blackbox looks at the value of one property (can also be several properties if they are correlated) and decides whether that value is rather good (for AM) or rather bad (for AM).
Scores are meant to evaluate one or several Blackboxes, to produce an overall assessment of a part’s characteristics. Through reports and charts, the score results can then be shown to users.

The following example illustrates a typical setup:

Blackboxes and Scores are written in Javascript. For making adjustments to the calculation logic, it is helpful to have some prior exposure to any kind of scriptwriting or programming

Blackboxes

Defining blackboxes

Blackboxes are defined in the admin panel under

Home › B3_Ampi › Blackboxes

When defining blackboxes, the following fields are available.

Name
The name of the blackbox

Description
Additional description for the blackbox. Currently not displayed anywhere.

Script
The core of the blackbox is a script written in Javascript

Slug
Unique identifier of that blackbox. Used to reference this blackbox from a score.

Writing blackbox scripts

A blackbox can be considered a function that returns a value or an object.

Blackboxes can access a part’s property values through the property’s Variable namespace and Variable name:

variables.<Variable namespace>.<Variable name>

Variable namespace and Variable name are configured on the property itself.

By convention, blackboxes used for tech and econ scores have a return value between 0..1 representing a percentage between 0-100%.

Example:

A blackbox assessing the integer property Lead time (days)

// Lead time intervals
const intervals = [8, 60]

// Reading lead time from the property
const leadTime = variables.custom.leadTimeExact
if (leadTime === null) return null

return mapInterval(leadTime, intervals[0], intervals[1])

Example:

A blackbox assessing the boolean property Qualification needed

const propertyValue = variables.custom.qualificationNeeded

switch (propertyValue) {
	case true:
		return 0.35
	case false:
		return 1.0
	default:
		return null
}

Scores

Scores can aggregate blackbox results into an overall result

Defining scores

Scores are defined in the admin panel under

Home › B3_Ampi › Scores

When defining scores, the following fields are available.

Name
The display name of the score.

Description
Additional description for the score. Currently not displayed anywhere in the UI.

Script
The core of the score is a script written in Javascript

Slug
Unique identifier of that score. Used as part of the column name in CSV exports.

Filterable
If selected, this score will be available for filtering on the part list. Should only be enabled for numerical scores. Only scores marked as filterable will be included in CSV exports.

Writing score scripts

A score can be considered a function that returns an object with a result value. The result value can then be displayed in charts.

Example:

A basic calculated score

const calculated_result = 0.3 + 0.5

return {
	result: calculated_result,
}

All blackboxes are calculated before calculating scores. Within scores scripts, blackbox results are accessible through the results variable. Hence scores can access blackbox results through the results variable and the corresponding blackbox slug.

Example:

A score script retrieving the result of the BbEconLeadTimeExact blackbox

// Slug of the blackbox that I want to retrieve
const LEAD_TIME_EXACT = 'BbEconLeadTimeExact'

// Retrieving blackbox result
const value = results[LEAD_TIME_EXACT].result

Tech and Econ scores

Tech and Econ scores are numerical scores that calculate the technical and economic suitability of a part within a range of [0, 1]. Values towards 0 indicate poor suitability, values towards 1 indicate good suitability for AM.

The general setup of Tech and Econ scores in AMPI is the calculation of the Weighted Average (Wikipedia). In the weighted average score calculation, we calculate the average of the results of individual blackboxes and give a weight according to their importance or impact on the overall score.

The weights of the blackbox can be adjusted to indicate how much impact an individual blackbox should have on the score. By convention, the weights are between 0..1. Values towards 0 indicate low impact, values towards 1 indicate a strong impact on the score result.

For a complete example of an econ score, see the Appendix

Text scores

The result of text scores is a String (text) - as opposed to numerical scores that produce numerical results. They can be used in charts with the type Text.

Text scores need to return an object with two variables

• result - contains the results string, can be markdown formatted

• type - variable with the value 'text'

Example:

A simple text score

const result_string = 'Text with __Markdown__ syntax'

return {
	result: result_string,
	type: 'text',
}

For a more comprehensive example see the Appendix.

Helper functions

Helper functions are functions that are often used throughout multiple blackboxes or scores. To avoid defining such functions in each blackbox where it is used, they can just be defined once as a helper function. Then all blackboxes and scores can just use them without the need to define them redundantly.

Example helper function

// Maps an input from a flexible interval into the corresponding value within standard interval [0, 1]
function mapInterval(val, a, b, c = 0.0, d = 1.0) {
	return Math.min(1, Math.max(0, ((val - a) * (d - c)) / (b - a) + c))
}

For an example where a helper function is used, see the example blackbox for Lead time (days) above.

Helper functions can be defined in the admin panel under

Home › B3_Ampi › Helper functions

How to debug blackboxes and scores

The results of blackbox and score calculations can be verified in several places in the admin panel

Verifying all calculations of one particular part

1. Go to Parts
   Home › B3_Ampi › Parts

2. Find the part that you are interested in and open the details

3. Scroll down past the properties

You’ll reach a table with all score results for this part and a table with all blackbox results just below.

Appendix

Example

Full econ score script

// Blackbox IDs
const LEAD_TIME_EXACT = 'BbEconLeadTimeExact'
const MIN_ORDER_QTY = 'BbEconMinOrderQuantity'
const PART_PRICE = 'BbEconCurrentPartPrice'
const PRICE_PER_KG = 'BbEconPricePerKg'
const QUALIFICATION_NEEDED = 'BbEconQualificationNeeded'
const SELECTION_LOGIC_ECON = 'BbEconSelectionLogic'

// Define blackbox weights
const blackboxes = [
	{
		name: 'Part price',
		weight: 0.5,
		id: PART_PRICE,
	},
	{
		name: 'Lead time',
		weight: 0.7,
		id: LEAD_TIME_EXACT,
	},
	{ name: 'Price/kg', weight: 0.5, id: PRICE_PER_KG },
	{
		name: 'Sel. Logic',
		weight: 1.0,
		id: SELECTION_LOGIC_ECON,
	},
]

let resultsBlackboxes = new Set()

let score = 0
let weightsum = 0
let weighttotal = 0

for (const { id, name, weight } of blackboxes) {
    
    // Retrieve blackbox result
	const value = results[id].result
	
	if (value !== null) {
		score += weight * value
		weightsum += weight

		resultsBlackboxes.add({
			name: name,
			weight: weight,
			id: id,
			result: value,
		})
	}
	weighttotal += weight
}
let result = score / weightsum
let certainty = weightsum / weighttotal

return {
	result: result,
	certainty: certainty,
	resultsBlackboxes: resultsBlackboxes,
}

Example

Text score for complexity

// Blackbox slugs
const COMPLEXITY = 'ComplexityExact'

const complexityExact = results[COMPLEXITY].result
let result_string

if (complexityExact !== null) {
	let complexityChoice
	if (complexityExact < 1.55) {
		complexityChoice = 'low'
	} else if (complexityExact < 2.1) {
		complexityChoice = 'medium'
	} else complexityChoice = 'high'
	result_string =
		'Complexity: ' + complexityExact.toFixed(2) + ' (' + complexityChoice + ')'
} else result_string = 'Insufficient data to calculate Complexity'

return {
	result: result_string,
	type: 'text',
}