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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Data Science

Background:

  • Modern manufacturing trends favor small-batch production, driving adoption of additive manufacturing (AM) technologies like 3D printing.
  • While AM offers design flexibility and material efficiency, challenges remain in achieving product quality comparable to conventional methods, particularly concerning surface roughness.
  • Surface roughness is a critical quality indicator affecting product lifespan and structural integrity in AM components.

Purpose of the Study:

  • To develop and validate a data analysis methodology for predicting and controlling surface roughness in AM.
  • To enhance the quality of products manufactured using additive manufacturing processes.

Main Methods:

  • Utilized data pre-processing techniques and deep neural networks (DNNs).
  • Integrated sensor data with DNNs for predictive modeling of surface roughness.
  • Applied the methodology to field data from wire + arc additive manufacturing (WAAM) processes.

Main Results:

  • The proposed methodology effectively predicts surface roughness in AM.
  • Achieved a low mean absolute percentage error (MAPE) of 1.93% in surface roughness prediction.
  • Demonstrated the practical applicability and effectiveness of the data-driven approach in a real-world WAAM setting.

Conclusions:

  • The data analysis methodology significantly improves the prediction accuracy of surface roughness in AM.
  • This approach offers a viable solution for enhancing the quality control of AM products.
  • The findings support the integration of advanced data analytics for optimizing additive manufacturing processes.