4.6 Article

Uncertainties Induced by Processing Parameter Variation in Selective Laser Melting of Ti6Al4V Revealed by In-Situ X-ray Imaging

期刊

MATERIALS
卷 15, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/ma15020530

关键词

selective laser melting; laser powder bed fusion; additive manufacturing; spatter; melt pool dynamics; quality uncertainty

资金

  1. U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory [DE-AC02-06CH11357]
  2. Center for Aerospace Manufacturing Technology (CAMT) at Missouri University of Science and Technology
  3. US National Science Foundation
  4. Graduate Assistance in Areas of National Need (GAANN)

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This study identifies the sources of uncertainty in the selective laser melting (SLM) process and reveals that variations in processing parameters can result in significant changes in the depression zone, melt pool, and spatter behavior. The responses of SLM dynamics to small variations in processing parameters provide valuable insights for understanding the uncertainties in the SLM process.
Selective laser melting (SLM) additive manufacturing (AM) exhibits uncertainties, where variations in build quality are present despite utilizing the same optimized processing parameters. In this work, we identify the sources of uncertainty in SLM process by in-situ characterization of SLM dynamics induced by small variations in processing parameters. We show that variations in the laser beam size, laser power, laser scan speed, and powder layer thickness result in significant variations in the depression zone, melt pool, and spatter behavior. On average, a small deviation of only ~5% from the optimized/reference laser processing parameter resulted in a ~10% or greater change in the depression zone and melt pool geometries. For spatter dynamics, small variation (10 mu m, 11%) of the laser beam size could lead to over 40% change in the overall volume of the spatter generated. The responses of the SLM dynamics to small variations of processing parameters revealed in this work are useful for understanding the process uncertainties in the SLM process.

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