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moonbitTolerance

Deterministic mechanical tolerance stack-up analysis for MoonBit.

tolerance
mechanical
dimension-chain
engineering
moon add gckbbrant/moonbitTolerance@0.2.0
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Author
Version
0.2.0
License
Apache-2.0
Repository
Last updated
19 hours ago
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README

#moonbitTolerance

Deterministic mechanical tolerance stack-up analysis for MoonBit. The library is a reusable calculation kernel for CAD-adjacent tools, process planning, inspection software, and manufacturing data pipelines.

#Project positioning

moonbitTolerance keeps geometry, statistical assumptions, constraint evaluation, and reporting separate from any CAD file format. It is suitable when a host application needs reproducible one-dimensional or projected two-dimensional tolerance calculations with explicit assumptions.

#Core capabilities

  • Signed one-dimensional dimension chains with worst-case, RSS, and seeded Monte Carlo analysis.
  • Two-dimensional vector dimensions and projected stack-up intervals.
  • Uniform, triangular, and bounded normal-approximation sampling policies.
  • Process capability indices (Cp, Cpu, Cpl, and Cpk).
  • Gap constraints with satisfied, violated, and inconclusive outcomes.
  • CSV result export for small downstream reporting adapters.
  • Reusable process profiles, calibration points, and deterministic regression vectors.
  • Native CLI demonstration and benchmark suite.

#Quick start

Requirements: MoonBit stable toolchain and a native-capable host for the CLI.

moon test --target wasm-gc --deny-warn
moon run cmd/main

let chain = @moonbitTolerance.Chain::new("shaft", [
  @moonbitTolerance.Dimension::new("housing", 20.0, 0.05),
  @moonbitTolerance.Dimension::new(
    "cover", 0.2, 0.02,
    direction=@moonbitTolerance.negative_direction(),
  ),
])
let result = chain.monte_carlo(10000, seed=42U)

#CLI

moon run cmd/main prints a small RSS analysis and runs the deterministic benchmark suite. The CLI is intentionally a host-integration example; applications should import the root package and keep their own I/O boundary.

#Architecture

The root package owns the public engineering types. tolerance.mbt contains the one-dimensional kernel; geometry.mbt contains vector projection; distributions.mbt contains sampling and capability calculations; constraints.mbt contains interval checks and CSV output. The catalog files contain structured, inspectable reference inputs rather than hidden runtime state. cmd/main is an executable consumer of the package.

#Benchmark

The benchmark suite uses the same public API as an application: five chains with 4, 8, 16, 32, and 64 contributors, each sampled with an explicit seed. Run it with:

moon run cmd/main

The output is deterministic for a fixed toolchain, sample count, and seed. The repository does not claim a hardware-independent throughput number; wall-clock measurements belong to the machine and backend that produced them. The checked-in reference vector corpus is a deterministic regression fixture, not a measurement claim.

#Testing

moon fmt --check
moon check --deny-warn
moon test --target wasm-gc --deny-warn
moon test --target native --deny-warn
moon info

Tests cover invalid inputs, signed dimensions, deterministic sampling, projection boundaries, process capability edges, constraints, calibration correction, and reference corpus shape.

#CI

GitHub Actions runs formatting, warning-free type checking, generated-interface drift detection, coverage analysis, wasm-gc tests, and native tests on Linux, macOS, and Windows. The workflow installs the current MoonBit stable toolchain using the official installer and keeps the generated interface under review.

#License

Apache-2.0. See LICENSE.

#Development

See CONTRIBUTING.md for the local validation loop and docs/assumptions.md for modeling assumptions.

#
AnalysisResult

source
pub struct AnalysisResult {
  nominal : Double
  lower : Double
  upper : Double
  mean : Double
  standard_deviation : Double
  yield_rate : Double
  sensitivity : Array[(String, Double)]
} derive(
Debug)

#
BenchmarkCase

source
pub struct BenchmarkCase {
  name : String
  dimensions : Int
  tolerance : Double
  seed : UInt
} derive(
Debug)

#
BenchmarkCase::chain

source
fn BenchmarkCase::chain(self : BenchmarkCase) -> Chain

#
CalibrationPoint

source
pub struct CalibrationPoint {
  instrument : String
  axis : String
  nominal : Double
  bias : Double
  repeatability : Double
  confidence : Double
} derive(
Debug)

#
Chain

source
pub struct Chain {
  name : String
  dimensions : Array[Dimension]
} derive(
Debug)

#
Chain::monte_carlo

source
fn Chain::monte_carlo(self : Chain, samples : Int, seed? : UInt) -> AnalysisResult

#
Chain::new

source
fn Chain::new(name : String, dimensions : Array[Dimension]) -> Chain

#
Chain::nominal

source
fn Chain::nominal(self : Chain) -> Double

#
Chain::rss

source
fn Chain::rss(self : Chain) -> AnalysisResult

#
Chain::worst_case

source
fn Chain::worst_case(self : Chain) -> AnalysisResult

#
ConstraintReport

source
pub struct ConstraintReport {
  passed : Int
  failed : Int
  inconclusive : Int
  statuses : Array[(String, ConstraintStatus)]
} derive(
Debug)

#
ConstraintStatus

source
pub enum ConstraintStatus {
  Satisfied
  Violated
  Inconclusive
} derive(Eq, 
Debug)

#
Dimension

source
pub struct Dimension {
  name : String
  nominal : Double
  tolerance : Double
  direction : Direction
} derive(Eq, 
Debug)

#
Dimension::new

source
fn Dimension::new(name : String, nominal : Double, tolerance : Double, direction? : Direction) -> Dimension

#
Direction

source
pub enum Direction {
  Positive
  Negative
} derive(Eq, 
Debug)

#
Distribution

source
pub enum Distribution {
  Uniform
  Triangular
  NormalApproximation
} derive(Eq, 
Debug)

#
GapConstraint

source
pub struct GapConstraint {
  name : String
  minimum : Double
  maximum : Double
} derive(
Debug)

#
GapConstraint::evaluate

source

#
GapConstraint::new

source
fn GapConstraint::new(name : String, minimum : Double, maximum : Double) -> GapConstraint

#
ProcessCapability

source
pub struct ProcessCapability {
  lower_spec : Double
  upper_spec : Double
  mean : Double
  standard_deviation : Double
} derive(
Debug)

#
ProcessCapability::cp

source
fn ProcessCapability::cp(self : ProcessCapability) -> Double

#
ProcessCapability::cpk

source
fn ProcessCapability::cpk(self : ProcessCapability) -> Double

#
ProcessCapability::cpl

source
fn ProcessCapability::cpl(self : ProcessCapability) -> Double

#
ProcessCapability::cpu

source
fn ProcessCapability::cpu(self : ProcessCapability) -> Double

#
ProcessCapability::new

source
fn ProcessCapability::new(lower_spec : Double, upper_spec : Double, mean : Double, standard_deviation : Double) -> ProcessCapability

#
ProjectedDimension

source
pub struct ProjectedDimension {
  name : String
  vector : Vector2
  tolerance : Double
} derive(
Debug)

#
ProjectedDimension::new

source
fn ProjectedDimension::new(name : String, vector : Vector2, tolerance : Double) -> ProjectedDimension

#
ProjectedResult

source
pub struct ProjectedResult {
  nominal : Vector2
  lower_x : Double
  upper_x : Double
  lower_y : Double
  upper_y : Double
  radial_tolerance : Double
  sensitivity : Array[(String, Double)]
} derive(
Debug)

#
ReferenceVector

source
pub struct ReferenceVector {
  dimensions : Int
  tolerance : Double
  nominal : Double
  worst_lower : Double
  worst_upper : Double
  rss_sigma : Double
} derive(
Debug)

#
SamplingPolicy

source
pub struct SamplingPolicy {
  distribution : Distribution
  sigma_factor : Double
  seed : UInt
} derive(
Debug)

#
SamplingPolicy::new

source
fn SamplingPolicy::new(distribution? : Distribution, sigma_factor? : Double, seed? : UInt) -> SamplingPolicy

#
StandardProfile

source
pub struct StandardProfile {
  code : String
  family : String
  nominal_mm : Double
  tolerance_mm : Double
  cpk_target : Double
  sample_count : Int
} derive(
Debug)

#
Vector2

source
pub struct Vector2 {
  x : Double
  y : Double
} derive(Eq, 
Debug)

#
Vector2::add

source
fn Vector2::add(self : Vector2, other : Vector2) -> Vector2

#
Vector2::dot

source
fn Vector2::dot(self : Vector2, other : Vector2) -> Double

#
Vector2::length

source
fn Vector2::length(self : Vector2) -> Double

#
Vector2::length_squared

source
fn Vector2::length_squared(self : Vector2) -> Double

#
Vector2::new

source
fn Vector2::new(x : Double, y : Double) -> Vector2

#
Vector2::normalize

source
fn Vector2::normalize(self : Vector2) -> Vector2

#
Vector2::scale

source
fn Vector2::scale(self : Vector2, factor : Double) -> Vector2

#
Vector2::sub

source
fn Vector2::sub(self : Vector2, other : Vector2) -> Vector2

#
Vector2::zero

source
fn Vector2::zero() -> Vector2

#
benchmark_suite

source
fn benchmark_suite() -> Array[BenchmarkCase]

#
calibration_point_count

source
fn calibration_point_count() -> Int

#
calibration_points

source
fn calibration_points() -> Array[CalibrationPoint]

#
corrected_measurement

source
fn corrected_measurement(point : CalibrationPoint, observed : Double) -> Double

#
deterministic_uniform

source
fn deterministic_uniform(seed : UInt) -> (UInt, Double)

#
evaluate_constraints

source
fn evaluate_constraints(result : AnalysisResult, constraints : Array[GapConstraint]) -> ConstraintReport

#
interval_contains

source
fn interval_contains(result : AnalysisResult, value : Double) -> Bool

#
interval_width

source
fn interval_width(result : AnalysisResult) -> Double

#
negative_direction

source
fn negative_direction() -> Direction

#
normal_approximation_policy

source
fn normal_approximation_policy(seed? : UInt) -> SamplingPolicy

#
project_chain

source
fn project_chain(dimensions : Array[ProjectedDimension]) -> ProjectedResult

#
project_direction

source
fn project_direction(angle_radians : Double) -> Vector2

#
project_signed

source
fn project_signed(value : Double, angle_radians : Double) -> Vector2

#
reference_vectors

source
fn reference_vectors() -> Array[ReferenceVector]

#
result_to_csv

source
fn result_to_csv(result : AnalysisResult) -> String

#
run_benchmark_case

source
fn run_benchmark_case(case : BenchmarkCase, samples : Int) -> AnalysisResult

#
sample_deviation

source
fn sample_deviation(policy : SamplingPolicy, unit : Double, tolerance : Double) -> Double

#
standard_profile_count

source
fn standard_profile_count() -> Int

#
standard_profiles

source
fn standard_profiles() -> Array[StandardProfile]

#
triangular_policy

source
fn triangular_policy(seed? : UInt) -> SamplingPolicy

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