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An Explorative Study of AI Applications in Composite Material Extrusion Additive Manufacturing

  • Austin Harper,
  • Thorsten Wuest

摘要

Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and Machine Learning (ML) to improve processing capabilities. We conducted a literature review to survey the current state of the art for AI/ML applications within Material Extrusion AM (MEX-AM). Furthermore, this study explored the intersection of AI applications and use of Carbon Fiber-Reinforced Polymers (CFRP) as a MEX-AM material. We found that while discontinuous CFRPs are covered in several experimental studies, there was a noticeable lack of research on continuous CFRPs among the collected papers. We found that the most common ML Solution for quality issues in MEX-AM was the artificial neural network feed forward supervised learning back propagation (ANN-FFNN-SL-BPN) Solution.