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Defect Characterization Through Automated Laser Track Trace Identification in SLM Processes Using Laser Profilometer Data

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Abstract

This paper presents recent developments in statistical image processing to demonstrate feasibility of layer-by-layer nondestructive inspection for selective laser melting (SLM) of metallic parts. A matrix of 10 mm × 10 mm stainless 316 squares was deposited with the first row consisting of just the sintered surface, Row 2 a single layer, Row 3 with 2 layers and so on. These layers were scanned to emulate the layer-by-layer collection of top surface geometry using a laser sensor. The resultant data were utilized to generate ISO 25718 roughness parameters and subsequently to perform track identification. This calculation consisted of two salient steps: (1) computing the gradients of surface roughness, which vanish at the peaks and pits, and (2) extracting the scanning direction by taking Hough transform of the geometry. This algorithm was repeated for all layers, and the alignment of the roughness with the rotating scan was observed to be persistent for all layers. Correlation between surface properties obtained along various scan directions and defect probability is explored.

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Authors and Affiliations

  1. Applied Optimization, Fairborn, OH, USA

    Brandon Baucher & Anil B. Chaudhary

  2. University of Tennessee, Knoxville, TN, USA

    Sudarsanam S. Babu & Subhadeep Chakraborty

Authors
  1. Brandon Baucher
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  2. Anil B. Chaudhary
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  3. Sudarsanam S. Babu
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  4. Subhadeep Chakraborty
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Corresponding author

Correspondence to Subhadeep Chakraborty.

Additional information

This article is an invited paper selected from presentations at the symposium ‘Additive Manufacturing: In-situ Process Monitoring and Control,’ held during MS&T’17, October 8-12, 2017, in Pittsburgh, Pa., and has been expanded from the original presentation.

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Baucher, B., Chaudhary, A.B., Babu, S.S. et al. Defect Characterization Through Automated Laser Track Trace Identification in SLM Processes Using Laser Profilometer Data. J. of Materi Eng and Perform 28, 717–727 (2019). https://doi.org/10.1007/s11665-018-3842-4

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  • DOI: https://doi.org/10.1007/s11665-018-3842-4

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