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Process Parameter Prediction for Fused Deposition Modeling Using Invertible Neural Networks.

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Summary

This study introduces Invertible Neural Networks (INN) to optimize additive manufacturing. The INN system objectively generates precise process parameters for Fused Deposition Modeling (FDM) parts, achieving high accuracy in desired mechanical and optical properties.

Keywords:
additive manufacturingfused deposition modelingneural networkpart qualityprocess parametersproduction management

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Artificial Intelligence

Background:

  • Additive manufacturing, particularly Fused Deposition Modeling (FDM), offers significant advantages in prototyping and customization.
  • The complex interplay and non-linear relationships between numerous process parameters in FDM make achieving desired part properties challenging.
  • Existing methods for parameter selection lack objectivity and struggle with the inherent complexities of the FDM process.

Purpose of the Study:

  • To develop an objective method for generating optimal process parameters in FDM using Artificial Intelligence.
  • To demonstrate the efficacy of Invertible Neural Networks (INN) in translating desired part properties into specific manufacturing parameters.
  • To validate the precision and accuracy of the INN-driven parameter generation for FDM.

Main Methods:

  • Implementation of Invertible Neural Networks (INN) as a core technology for process parameter generation.
  • Defining desired part characteristics across mechanical properties, optical properties, and manufacturing time as input criteria.
  • Systematic validation of the generated parameters through experimental trials and property measurements.

Main Results:

  • The INN successfully generated process parameters that closely replicate desired part properties.
  • Experimental validation demonstrated high precision, with measured properties achieving up to 99.96% of the desired targets.
  • The system achieved a mean accuracy of 85.34% in replicating the specified part characteristics.

Conclusions:

  • Invertible Neural Networks provide an effective and objective solution for optimizing FDM process parameters.
  • This AI-driven approach significantly enhances the ability to customize FDM parts with predictable and accurate properties.
  • The study highlights a promising direction for intelligent manufacturing and automated process control in additive manufacturing.