Detection of Part Porosity in Additive Manufacturing Using Machining

Jaydeep Karandikar; Akash Tiwari; Gregory Corson; Blane Fillingim; Christopher Tyler; Scott Smith

doi:10.1115/1.4069150

Detection of Part Porosity in Additive Manufacturing Using Machining

Jaydeep Karandikar
, Akash Tiwari
, Gregory Corson
, Blane Fillingim
, Christopher Tyler
, Scott Smith

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	101004
Journal	Journal of Manufacturing Science and Engineering
Volume	147
Issue number	10
DOIs	https://doi.org/10.1115/1.4069150
State	Published - Oct 1 2025

Funding

This research was supported by the DOD Industrial Base Sustainment and Analysis (IBAS) program and the Advanced Materials and Manufacturing Technologies Office (AMMTO) at the DOE Office of Energy Efficiency and Renewable Energy (EERE), and used resources at the Manufacturing Demonstration Facility, a DOE EERE User Facility at Oak Ridge National Laboratory. The authors would like to thank Rob Caron and Derril Vezina, CARON Engineering, for their support with the data acquisition setup.

Keywords

additive manufacturing (AM)
machining
monitoring
monitoring and diagnostics
sensors

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10.1115/1.4069150

Cite this

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title = "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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AU - Tiwari, Akash

AU - Corson, Gregory

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AU - Tyler, Christopher

AU - Smith, Scott

PY - 2025/10/1

Y1 - 2025/10/1

N2 - 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.

AB - 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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KW - monitoring

KW - monitoring and diagnostics

KW - sensors

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Detection of Part Porosity in Additive Manufacturing Using Machining

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