Detection of Part Porosity in Additive Manufacturing Using Machining

Abstract Monitoring of part porosity in additive manufacturing is critical for part qualification and process parameter development. This article describes the monitoring of porosity in additive manufacturing using machining. Grade 91 steel samples with different levels of porosity were additively manufactured using a laser blown-powder process. The samples were machined, and the tool position and cutting power were monitored using the machine controller and an external power transducer, respectively. Results show that the power transducer is sensitive to machining porosities. A novel approach using the standard deviation of cutting power was developed to determine the location of porosities along the length of the machining pass. Test cuts were completed at different machining process parameters. Results showed that the probability of detection was one for pore volume greater than 0.3 mm3 at different machining parameters. Although the detection of porosity using machining is a destructive method for evaluation, it offers a low-cost and fast approach for monitoring porosity in additive manufacturing.

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Publication Details

Journal
Journal of Manufacturing Science and Engineering
Published
2025-07-14
DOI
https://doi.org/10.1115/1.4069150
Citations
1
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
0.68
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article

Detection of Part Porosity in Additive Manufacturing Using Machining

Akash Tiwari, Jaydeep Karandikar, Blane Fillingim, Gregory Corson et al.
1 citations
Journal of Manufacturing Science and Engineering
Additive Manufacturing and 3D Printing Technologies
0.68
article

Detection of Part Porosity in Additive Manufacturing Using Machining

Akash Tiwari, Jaydeep Karandikar, Blane Fillingim, Gregory Corson, Christopher W. Tyler, Scott Smith
article en
1 citations

Abstract

Abstract Monitoring of part porosity in additive manufacturing is critical for part qualification and process parameter development. This article describes the monitoring of porosity in additive manufacturing using machining. Grade 91 steel samples with different levels of porosity were additively manufactured using a laser blown-powder process. The samples were machined, and the tool position and cutting power were monitored using the machine controller and an external power transducer, respectively. Results show that the power transducer is sensitive to machining porosities. A novel approach using the standard deviation of cutting power was developed to determine the location of porosities along the length of the machining pass. Test cuts were completed at different machining process parameters. Results showed that the probability of detection was one for pore volume greater than 0.3 mm3 at different machining parameters. Although the detection of porosity using machining is a destructive method for evaluation, it offers a low-cost and fast approach for monitoring porosity in additive manufacturing.

Journal of Manufacturing Science and EngineeringVol. 147(10)
Oak Ridge National Laboratory (US), National Transportation Research Center (US)
Openalex Percentile: Top 28%
Additive Manufacturing and 3D Printing Technologies
0.68
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Detection of Part Porosity in Additive Manufacturing Using Machining — Akash Tiwari, Jaydeep Karandikar, et al. · Journal of Manufacturing Science and Engineering (2025) | TGRS Research Map | TGRS