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In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

International Journal of Advanced Manufacturing Technology, ISSN: 1433-3015, Vol: 132, Issue: 9-10, Page: 5087-5101
2024
  • 4
    Citations
  • 0
    Usage
  • 22
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
  • Captures
    22
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Study Findings from Georgia Southern University Broaden Understanding of Machine Learning [In Situ Process Monitoring of Multi-layer Deposition In Wire Arc Additive Manufacturing (Waam) Process With Acoustic Data Analysis and Machine Learning]

2024 MAY 16 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- A new study on Machine Learning is now available.

Article Description

Additive manufacturing (AM) of metal components is expanding as a developing technique for fabricating high-value and large-scale metal parts. Among various metal AM procedures, wire arc additive manufacturing (WAAM) received special attention in recent years due to its unique potential for fabricating complex geometries in industrial scale and applications. In this study, a step forward for developing a continuous, multi-layer in-situ monitoring technique based on acoustic signatures recorded by acoustic emission nondestructive method over the deposition process is presented. The major goal of this research is to investigate if previously proven single-layer monitoring procedures based on acoustic signatures can be expanded toward a robust multi-layer and continuous monitoring method. Two different types of materials have been used in a WAAM process equipped with acoustic emission sensors, and recorded signals were analyzed by traditional statistical assessment as well as a K-mean clustering machine learning algorithm. The findings affirm the effectiveness of acoustic signals in monitoring processes during the continuous deposition of material and indicate that acoustic signals can reliably identify distinct process states across all layers. This underscores the reliability of acoustic signals as a multi-layer process monitoring method.

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