Manufacturing Engineering Domain Ontology

FLOW ALGEBRA
TITLE: Manufacturing Engineering Domain Ontology
DOMAIN: Domain Ontologies / Engineering Domains / Manufacturing Engineering
SOURCE: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/engineering-domains/manufacturing-engineering-domain-ontology.html
VERSION: 1.0
AUTHORS: Mark Anthony Higgins — Sky (Recursive AI)
STATUS: Companion formalisation; source-page specification not independently verified.

flow "ManufacturingEngineeringDomainOntology" v1.0 {
  ontology:
    id: "manufacturing-engineering-domain-ontology"
    parent: "engineering-domains"
    purpose: "Represent manufacturing products, processes, resources, quality controls and evidence-backed engineering dependencies."

  types:
    Product: {id: string, revision: string, specification: string?, provenance: string}
    Component: {id: string, product_id: string, material_id: string?, revision: string}
    Material: {id: string, grade: string?, batch_id: string?, provenance: string}
    Process: {id: string, kind: enum[machining, forming, casting, joining, additive, assembly, finishing, inspection, other], revision: string}
    Operation: {id: string, process_id: string, sequence: integer, parameters: map<string, Quantity>}
    Equipment: {id: string, kind: string, calibration_status: string?, availability: string?}
    Resource: {id: string, kind: enum[labour, machine, tooling, material, energy, facility, other], capacity: Quantity?}
    Requirement: {id: string, target_id: string, criterion: string, tolerance: Quantity?, standard: string?}
    Measurement: {id: string, target_id: string, quantity: Quantity, instrument_id: string?, timestamp: datetime, provenance: string}
    QualityRecord: {id: string, target_id: string, result: enum[pass, fail, inconclusive], evidence: string[], timestamp: datetime}
    Quantity: {value: number, unit: string, uncertainty: number?}
    Relationship: {id: string, subject: string, predicate: string, object: string, context: string?, confidence: number, evidence: string[]}

  predicates:
    contains(product: Product, component: Component)
    uses_material(component: Component, material: Material)
    performs(operation: Operation, process: Process)
    precedes(first: Operation, second: Operation)
    uses_equipment(operation: Operation, equipment: Equipment)
    consumes(operation: Operation, resource: Resource)
    produces(operation: Operation, product: Product)
    governed_by(target: string, requirement: Requirement)
    measured_by(target: string, measurement: Measurement)
    verified_by(target: string, record: QualityRecord)
    depends_on(target: string, prerequisite: string)

  constraints:
    - "All relationship endpoints must resolve to typed entities."
    - "Confidence values must lie within [0, 1]."
    - "Measurements and process parameters must include units and uncertainty where available."
    - "Equipment-dependent operations must verify required calibration and capability."
    - "Product, process and requirement revisions must be traceable."
    - "A passed inspection does not imply universal product compliance."
    - "Dependency edges are not automatically causal relationships."
    - "Operation precedence must not imply physical transformation without evidence."
    - "Process plans must flag circular hard prerequisites and unavailable resources."

  inference:
    rule indirect_dependency:
      if depends_on(a,b) and depends_on(b,c) and compatible_context(a,b,c)
      then depends_on(a,c)
      confidence: "propagate evidence without automatic confidence inflation"
    rule sequence_precedence:
      if precedes(a,b) and precedes(b,c) and same_process_plan(a,b,c)
      then precedes(a,c)
      confidence: "subject to plan revision and execution evidence"
    rule verified_requirement:
      if governed_by(x,r) and measured_by(x,m) and satisfies(m,r) and valid(m)
      then candidate_compliance(x,r)
      confidence: "conditional on coverage, measurement uncertainty and validation"

  SEMANTIC_EQUIVALENCE:
    - "Map process, equipment and material terms to versioned standards and controlled vocabularies."
    - "Distinguish process aliases from technically equivalent manufacturing capabilities."
    - "Preserve units, tolerances, material grades, revisions and operating conditions."

  CONFIDENCE:
    range: [0,1]
    factors: [measurement_uncertainty, calibration_status, source_reliability, process_repeatability, traceability, independent_verification]
    policy: "Do not raise confidence solely by chaining dependencies or repeating evidence."

  VALIDATION:
    - "Validate entity references, types, measurement units and revision compatibility."
    - "Check resource capacities, equipment qualifications and process prerequisites."
    - "Compare inspection evidence with explicit acceptance criteria and tolerances."
    - "Flag contradictory records, stale calibrations and missing provenance."

  FLOW_HASH:
    algorithm: "SHA-256"
    input: "canonicalised ontology version, typed assertions, rules and provenance"
    note: "Compute at execution; no hash value is claimed here."

  UPDATE_MODEL:
    strategy: "versioned, provenance-preserving incremental update"
    steps: [ingest, normalise, validate, compare, reconcile, recompute_inferences, publish]
    rollback: true

  AI_CONSUMPTION:
    representation: "typed manufacturing knowledge graph with evidence-backed relationships"
    outputs: [entity_links, process_dependencies, resource_constraints, compliance_candidates, uncertainty_annotations, provenance_records]
    safeguards: [no_unsupported_compliance_claims, no_unqualified_causal_inference, preserve_units_and_revisions, require_human_review_for_safety_critical_decisions]
}

END FLOW ALGEBRA