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Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Bader Alwomi Aljabali1, Joseph Shelton2, Salil Desai1,3

  • 1Department of Industrial & Systems Engineering, College of Engineering, North Carolina A & T State University, Greensboro, NC 27411, USA.

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Summary
This summary is machine-generated.

This study introduces an automated system for selecting optimal additive manufacturing (AM) processes. It uses a data-driven approach to improve the design for additive manufacturing (DFAM) framework, enhancing 3D printing efficiency.

Keywords:
Industry 4.0design for additive manufacturingexpert systemgenetic algorithm

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Area of Science:

  • Manufacturing Engineering
  • Computer Science
  • Materials Science

Background:

  • Additive Manufacturing (AM) enables complex 3D object creation but lacks automated design rules.
  • Selecting appropriate AM processes requires specialized expertise, hindering widespread adoption.
  • Industry 4.0 demands data-driven solutions for Design for Additive Manufacturing (DFAM).

Purpose of the Study:

  • To develop an automated, data-driven system for AM process selection within the DFAM framework.
  • To capture and apply expert knowledge for optimizing AM process selection.
  • To address manufacturability challenges in 3D-printed parts.

Main Methods:

  • Utilized a Genetic and Evolutionary Feature Weighting technique with 3D CAD data.
  • Developed a two-stage predictive model.
  • Benchmarked Steady-State Genetic Algorithm (SSGA) against Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimization (PSO).

Main Results:

  • The two-stage model achieved average accuracies of 70% (Stage 1) and up to 97.33% (Stage 2).
  • SSGA demonstrated superior performance compared to EDA and PSO for AM process selection.
  • The system successfully identifies optimal AM processes for specific 3D object geometries.

Conclusions:

  • The developed automated system accurately identifies optimal AM processes, supporting DFAM.
  • The data-driven approach effectively captures and applies expert knowledge.
  • This system offers a scalable solution for improving AM process selection and manufacturability.