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GP-ME-0NND5GRMechanicalOpen for request

Additive-manufacturing parameter optimisation

A design-of-experiments workbench for modelling and optimising additive-manufacturing parameters against strength, surface, accuracy, time, and material objectives.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • Pandas
  • scikit-learn
  • Docker

Project definition

Problem statement

Layer height, speed, temperature, orientation, infill, material, and machine condition interact to affect part quality, time, and material use. Changing one factor at a time can miss interactions and produce conclusions that do not generalise.

The engineering problem is to design efficient experiments, build valid response models, optimise competing objectives, and confirm selected settings with held-out or physical tests.

Project objectives

  • Define a bounded process, material, specimen, factors, levels, responses, and measurement methods.
  • Generate a factorial, response-surface, Taguchi, or selected space-filling experiment design.
  • Model parameter effects and interactions with diagnostics and uncertainty.
  • Find Pareto-optimal settings for selected quality, time, and material objectives.
  • Verify recommended settings through held-out or confirmation experiments.

System design

System modules

01

Experiment designer

Defines factors, ranges, constraints, replicates, randomisation, blocks, and the selected design method.

02

Measurement manager

Records specimen, machine, process, strength, roughness, dimensional, time, mass, and failure data.

03

Response modeller

Fits selected statistical or machine-learning models and displays residual and validation checks.

04

Optimisation engine

Searches feasible settings and constructs Pareto tradeoffs across weighted or constrained objectives.

05

Confirmation dashboard

Compares predicted and measured confirmation results with uncertainty and experiment traceability.

Methodology

System workflow

  1. 01
    Define study

    The student fixes the process, material, specimen, factors, responses, and equipment limits.

  2. 02
    Create design

    The system produces a randomised experiment table with repeats and constraints.

  3. 03
    Enter results

    Measured outcomes and failed builds are recorded with specimen and process traceability.

  4. 04
    Model and optimise

    Validated response models generate feasible settings and Pareto choices.

  5. 05
    Confirm

    Selected settings are tested separately and prediction error is added to the final comparison.

Demonstration scenario

An FDM study varies layer height, print speed, nozzle temperature, and infill for strength, dimensional error, time, and mass. The workbench fits response models, displays significant interactions, creates a Pareto front, and compares one selected setting with a separate confirmation build.

Engineering

Technical architecture

Web application
Nuxt and Vue for study setup, experiment tables, effect plots, Pareto views, and confirmation results.
Experiment API
FastAPI for designs, observations, models, optimisation jobs, diagnostics, and exports.
Data layer
PostgreSQL for studies, factors, runs, specimens, measurements, models, candidates, and confirmations.
Analysis engine
Python, Pandas, statistical tools, and scikit-learn for design generation, response fitting, validation, and optimisation.
Reproducibility
Randomisation seeds, measurement units, machine and material records, versioned datasets, and saved model settings.

Testing

Evaluation

Evaluation measures

  • Design balance, factor coverage, randomisation, and repeatability
  • Cross-validated response error and residual diagnostics
  • Accuracy of predicted parameter effects and interactions on prepared data
  • Pareto feasibility and objective improvement against baseline settings
  • Prediction error on held-out or physical confirmation runs
  • Sensitivity to measurement noise, model choice, and factor-range changes

System boundaries

  • Conclusions apply only to the documented process, machine, material batch, specimen, and parameter ranges.
  • Prepared data can demonstrate the software, but a full engineering study requires controlled physical experiments.
  • Model optimisation cannot recommend settings outside tested or explicitly bounded ranges with confidence.
  • Machine safety, material handling, and test procedures remain governed by laboratory rules and qualified supervision.

Included

  1. 01Experiment design and measurement-entry interface
  2. 02Response modelling, optimisation, and confirmation modules
  3. 03Parameter effects, Pareto tradeoffs, and uncertainty dashboards
  4. 04Prepared or experimentally collected build data and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, specimen and process diagrams, experiment results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-ME-0NND5GR
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

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.