Although the exploitation of AM technologies continues to accelerate, a key barrier to adoption of AM is the need for in-process monitoring and process control [1, 2].
Equivalence-based and model-based certifications require reliable data set to validate complex multi-physics models. To move towards the certify-as-you-build scheme, industries call for sound in-situ process monitoring and quality control.
Equivalence-based and model-based certifications require reliable data set to validate complex multi-physics models. To move towards the certify-as-you-build scheme, industries call for sound in-situ process monitoring and quality control.
AM, with its rapid melting/cooling cycles high intensity local glare and small heat source/feedstock interaction volume, doesn’t lend itself to easy and precise measurements of melt pool temperatures.
Process monitoring is typically used to identify the formation of defects as it occurs with the view to remediate it or induce the decision of terminate a build and limit the production costs of a flawed component. In general, the goal is to determine the presence of porosity, contaminant inclusions, swelling, warping and other defects. Temperature evolution in the material is also measured.
Process monitoring is typically used to identify the formation of defects as it occurs with the view to remediate it or induce the decision of terminate a build and limit the production costs of a flawed component. In general, the goal is to determine the presence of porosity, contaminant inclusions, swelling, warping and other defects. Temperature evolution in the material is also measured.
In-situ monitoring
Infra-red thermography, standard cameras, high speed video and pyrometry have all been used for in situ monitoring and the detection of:
- Layer solidification homogeneity
- Temperature measurements and thermal build-up/history
- Powder layer uniformity
- Warping, curling, and other part deformations
- Melt pool characteristics (shape, temperature, dynamics, emission wavelenth)
Warping/curling effect picked up by high resolution in-line optical imaging system [12]
Standard images, and high speed videos, pick up the difference in light emission that can give information on local temperature variations and melt pool dynamics [3].
If optical monitoring can pick up defect formation IR or near-IR imaging contribute to full layer thermal analysis . This data help better understand solidification and thermal history and general AM metallurgy [4]. It is particularly useful for EBM, where it measures the elevated surface or powder-bed temperatures [5].
A feedback system using average temperature data acquired by in near-IR has already been used for on-the-fly process parameters correction [6].
If optical monitoring can pick up defect formation IR or near-IR imaging contribute to full layer thermal analysis . This data help better understand solidification and thermal history and general AM metallurgy [4]. It is particularly useful for EBM, where it measures the elevated surface or powder-bed temperatures [5].
A feedback system using average temperature data acquired by in near-IR has already been used for on-the-fly process parameters correction [6].
near IR imaging of Ti64 samples during build [11]
Thermocouples are used to effectively measure substrate temperature and local time-averaged temperature variation during build [].
Pyrometers have been used in Direct Energy Deposition [7] Electron Beam Melting [5] and Selective Laser Melting [8] The details of the sample location of the pyrometer are important to note, as the heat source may or may not pass in the measured area, depending on part geometry.
Pyrometers have been used in Direct Energy Deposition [7] Electron Beam Melting [5] and Selective Laser Melting [8] The details of the sample location of the pyrometer are important to note, as the heat source may or may not pass in the measured area, depending on part geometry.
Post-build Non Destructive Evaluation
Ultrasonic imaging, the Archimedes principle, X-ray computed tomography (XRCT) and neutron tomography have all been used as non-destructive evaluation (NDE) of AM components. Non-destructive techniques are commonly used to detect internal defects [9].
The Archimedes principle gives a qualitative indication of porosity in materials. It is a fast and economical technique for bulk measurements and seems to show congruent results with XRCT [10].
XRCT and neutron tomography are used to map out pores and determine the location of defects in the components. Ultrasonic transducers are capable of detecting smaller amounts of porosity (~0·5%) and should also be capable of porosity mapping.
The Archimedes principle gives a qualitative indication of porosity in materials. It is a fast and economical technique for bulk measurements and seems to show congruent results with XRCT [10].
XRCT and neutron tomography are used to map out pores and determine the location of defects in the components. Ultrasonic transducers are capable of detecting smaller amounts of porosity (~0·5%) and should also be capable of porosity mapping.
Current implementation in commercial systems
With the rise of R&D activities focused on process monitoring solutions, many manufacturers now offer additional proprietary modules, some of which can be retrofitted on previous generation machines. In most cases though, the data generated is stored and only analysed post-build.
[13]
Pyrometry is implemented with some DED processes to affect process control. Layer imaging using standard cameras has been implemented commercially on some EBM systems. Effective IR imaging and process feedback has yet to be implemented in any commercial system, but is a good option for users demanding better quality assurance.
Future trends
Vision based and thermal metrology solutions go some way to addressing quality control issues in AM.
The need remains to characterise 3D structures over relatively large areas to very high spatial resolution. Theses metrology tasks must be fast, compatible with the production environment and implemented in real time for closed-loop feedback.
The need remains to characterise 3D structures over relatively large areas to very high spatial resolution. Theses metrology tasks must be fast, compatible with the production environment and implemented in real time for closed-loop feedback.
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References
[1] W. J. Sames, F. A. List, S. Pannala, R. R. Dehoff & S. S. Babu (2016): The metallurgy and processing science of metal additive manufacturing, International Materials Reviews, http://dx.doi.org/10.1080/09506608.2015.1116649
[2] AM steering group UK, steering group, 2015 6th October, http://www.amnationalstrategy.uk/streering-group/
[3] T. Scharowsky, F. Osmanlic, R. F. Singer and C. Körner: ‘Melt pool dynamics during selective electron beam melting’, Appl.
Phys. A, 2014, 114, 1303–1307.
[4] S. Moylan, E.Whitenton, B. Lane and J. Slotwinski: ‘Infrared thermography for laser-based powder bed fusion additive manufacturing processes’, AIP Conf. Proc., 2014, 1581, 1191–1196.
[5] E. Rodriguez: ‘Development of a thermal imaging feedback control system in electron beam melting’, ETD Collection for
University of Texas, El Paso, 2013.
[6] J. Mireles, C. Terrazas, F. Medina and R. Wicker: ‘Automatic feedback control in electron beam melting using infrared tomography’, in ‘Solid freeform fabrication symposium’, Austin, TX, 2013.
[7] D. D. Gu, W. Meiners, K. Wissenbach and R. Poprawe: ‘Laser additive manufacturing of metallic components: materials, processes and mechanisms’, Int. Mater. Rev., 2012, 57, 133–164.
[8] M. Pavlov, M. Doubenskaia and I. Smurov: ‘Pyrometric analysis of thermal processes in SLM technology’, Phys. Proc., 2010, 5 Part B, 523–531.
[9] J. A. Slotwinski and E. J. Garboczi: ‘Porosity of additive manufacturing parts for process monitoring’, AIP Conf. Proc., 2014, 1581, 1197–1204.
[10] A. B. Spierings,M. Schneider and R. Eggenberger: ‘Comparison of density measurement techniques for additive manufactured metallic parts’, Rapid Prototyping J., 2011, 17, 380–386.
[11] E. Rodriguez, F. Medina, D. Espalin, C. Terrazas, D. Muse, C. Henry, E. MacDonald and R. Wicker: ‘Integration of a
thermal imaging feedback control system in electron beam melting’, in ‘Solid freeform fabrication symposium’, Austin, TX, 2012.
[12] S. Klezczynski, J. zur Jacobsmuhlen, J. Serht, G. Witt (Eds), Error detection in laser beam melting systems by high resolution imaging, 23rd International freeform fabrication, Austin texas, 2012
[13] S.K. Everton et al., Materials and design 95 (2016), 431-435
[1] W. J. Sames, F. A. List, S. Pannala, R. R. Dehoff & S. S. Babu (2016): The metallurgy and processing science of metal additive manufacturing, International Materials Reviews, http://dx.doi.org/10.1080/09506608.2015.1116649
[2] AM steering group UK, steering group, 2015 6th October, http://www.amnationalstrategy.uk/streering-group/
[3] T. Scharowsky, F. Osmanlic, R. F. Singer and C. Körner: ‘Melt pool dynamics during selective electron beam melting’, Appl.
Phys. A, 2014, 114, 1303–1307.
[4] S. Moylan, E.Whitenton, B. Lane and J. Slotwinski: ‘Infrared thermography for laser-based powder bed fusion additive manufacturing processes’, AIP Conf. Proc., 2014, 1581, 1191–1196.
[5] E. Rodriguez: ‘Development of a thermal imaging feedback control system in electron beam melting’, ETD Collection for
University of Texas, El Paso, 2013.
[6] J. Mireles, C. Terrazas, F. Medina and R. Wicker: ‘Automatic feedback control in electron beam melting using infrared tomography’, in ‘Solid freeform fabrication symposium’, Austin, TX, 2013.
[7] D. D. Gu, W. Meiners, K. Wissenbach and R. Poprawe: ‘Laser additive manufacturing of metallic components: materials, processes and mechanisms’, Int. Mater. Rev., 2012, 57, 133–164.
[8] M. Pavlov, M. Doubenskaia and I. Smurov: ‘Pyrometric analysis of thermal processes in SLM technology’, Phys. Proc., 2010, 5 Part B, 523–531.
[9] J. A. Slotwinski and E. J. Garboczi: ‘Porosity of additive manufacturing parts for process monitoring’, AIP Conf. Proc., 2014, 1581, 1197–1204.
[10] A. B. Spierings,M. Schneider and R. Eggenberger: ‘Comparison of density measurement techniques for additive manufactured metallic parts’, Rapid Prototyping J., 2011, 17, 380–386.
[11] E. Rodriguez, F. Medina, D. Espalin, C. Terrazas, D. Muse, C. Henry, E. MacDonald and R. Wicker: ‘Integration of a
thermal imaging feedback control system in electron beam melting’, in ‘Solid freeform fabrication symposium’, Austin, TX, 2012.
[12] S. Klezczynski, J. zur Jacobsmuhlen, J. Serht, G. Witt (Eds), Error detection in laser beam melting systems by high resolution imaging, 23rd International freeform fabrication, Austin texas, 2012
[13] S.K. Everton et al., Materials and design 95 (2016), 431-435