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GP-ME-0NND5GRMechanicalReady

Ti-6Al-4V LPBF process-property optimisation

A data-led mechanical and materials study linking Ti-6Al-4V laser powder bed fusion settings to porosity, surface roughness, yield strength, and failure elongation.

Ti-6Al-4V LPBF process-property optimisation project visual
GP-ME-0NND5GR · Mechanical
  • Python
  • NumPy
  • Pandas
  • SciPy
  • scikit-learn
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Laser power and scan speed change melt-pool energy input, but density, surface condition, strength, and ductility do not improve together across every observed setting.

The engineering problem is to compare those competing responses without hiding missing measurements, confusing model estimates with experiments, or presenting an observed optimum as a qualified manufacturing recipe.

Project objectives

  • Prepare and quality-check the published 42-setting Ti-6Al-4V process-property dataset.
  • Relate laser power, scan speed, and linear energy density to porosity, RMS roughness, yield strength, and failure elongation.
  • Compare transparent response models through leave-one-setting-out validation.
  • Rank the 35 complete observed cases and identify non-dominated process settings.
  • Propagate reported response spread through three thousand seeded decision draws.

Project structure

Project components

01

Data preparation

Loads the four published workbooks, normalises units and identifiers, and records response availability.

02

Process-property analysis

Calculates linear energy density and retains descriptive associations between settings and responses.

03

Model validation

Compares mean, linear, interaction, and quadratic response models using held-out settings.

04

Decision analysis

Normalises four observed responses, calculates desirability, and identifies the observed Pareto set.

05

Uncertainty study

Samples reported measurement spread with a fixed seed and measures rank, win, and Pareto stability.

06

Evidence pipeline

Writes complete CSV, JSON, PNG, SVG, test, document, and container evidence.

Methodology

Project workflow

  1. 01
    Load the evidence

    The released study reads the compact source workbooks and verifies their expected structure.

  2. 02
    Build the feature table

    Process settings and measured responses are joined without inventing values for missing tests.

  3. 03
    Validate response models

    Each candidate model predicts settings excluded from its own fit.

  4. 04
    Compare complete cases

    The four observed objectives are ranked and checked for Pareto dominance.

  5. 05
    Test uncertainty

    Seeded response draws quantify whether the leading observed choice remains competitive.

  6. 06
    Review the evidence

    Retained tables, figures, tests, and limitations connect every headline result to the source data.

Demonstration scenario

Among the 35 complete observed cases, parameter set 41 uses 235 W and 1200 mm/s. It records 0.339 percent porosity, 12.340 micrometre RMS roughness, 1047.8 MPa yield strength, and 15.38 percent failure elongation. It has the highest observed desirability and a 94.4 percent Pareto probability across three thousand seeded uncertainty draws.

Engineering

Tools and method

Tools
The project uses Python, NumPy, Pandas, SciPy, scikit-learn, Matplotlib, Jupyter for subject analysis, simulation, and results.
Published data
Four CC BY 4.0 Ti-6Al-4V workbooks provide the process, porosity, surface, hardness, tensile, and ductility evidence.
Numerical analysis
Python, NumPy, Pandas, SciPy, and scikit-learn implement data preparation, modelling, ranking, and uncertainty calculations.
Evidence
CSV, JSON, PNG, SVG, and Matplotlib preserve complete machine-readable and graphical results.
Verification
Leave-one-setting-out validation, 42 tests, branch-aware coverage, dependency audit, repository validation, and a pinned Docker workflow.

Testing

Evaluation

Evaluation measures

  • Coverage and missingness of the 42 observed parameter settings
  • Held-out response-model error across 714 retained predictions
  • Porosity, roughness, yield-strength, and elongation tradeoffs
  • Observed desirability and Pareto-set membership
  • Rank, win, and Pareto stability across three thousand uncertainty draws
  • Reproducibility through tests, retained outputs, source checksums, and dependency audit

Project boundaries

  • The study analyses one published Ti-6Al-4V experiment and its documented process window.
  • Associations and response surfaces are descriptive within the observed data and do not prove a universal causal law.
  • The uncertainty model uses reported summary spread and does not reconstruct raw specimen distributions or every systematic error.
  • The selected setting is an observed decision-support result, not a qualified machine parameter set, material certificate, or production approval.

Included

  1. 01Complete Python source code
  2. 02Four compact published Ti-6Al-4V source workbooks with provenance
  3. 03Eight complete analysis tables and fifteen analytical figures
  4. 0476-page project report in PDF and editable Word formats
  5. 054-page setup and usage guide in PDF and editable Word formats
  6. 0659 annotated references and four attributed literature images
  7. 0742 automated tests with 96.56 percent branch-aware coverage

Project record

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Permanent project ID
GP-ME-0NND5GR
Catalogued
21 Aug 2026
Completed
27 Aug 2026
Verified
27 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.