Selective Laser Melting: in-process monitoring techniques

The need for rapid qualification and verification of low-volume production or specialised components built using AM drives the development of novel sensing and control technique for SLM processes. They need to address specific challenges, such as multi-layer deposition or complex fine features. Here, we review in-process sensing and control strategies for SLM of metal components.

Within a complex system such as SLM machine and its build chamber, various sub-systems need to be monitored: laser, beam delivery system, motion controls, powder recoating and recycling and build environment,…

Using sensing and active feedback control within each of these subsystems can compound the process repeatability and reliability. Ideally, the metal components built using SLM should be fully dense, with isotropic (or tailored) mechanical properties, tailored surface roughness and high dimension accuracy.

Many of these characteristics such as material composition, density and microstructure cannot be directly measured. But indirectly by monitoring certain characteristics of the beam-material interaction, as well as solidified regions of the deposit. For instance, literature shows that characteristics of the melt pool geometry can be used to predict deposited microstructures in Ti-6Al-4V [2] and Inconel 718 [3].

The table below [1] summarises the main sensing and controls currently available to monitor SLM machines.

Laser system delivery

Potential issues

• thermal load on optics (Lenses refraction index variation with temperature and mechanical distortion), 
• damage from from gaseous process emissions and melt spatter, cracking

• beam power variation  

Impact

• beam front variation, changes in focus length and spot size/shape, distortion and beam attenuation
• distortion and beam attenuation

• Melting homogeneity

Sensors and remedial actions

• active cooling

• cleaning

• non-intrusive, real-time measurement and assessment of both internal and external laser beam power fluctuations: Pump power active feedback loop control [4], monitoring reflections [5],[6] 

Build chamber environment 

Potential issues

• O2 concentration
  

• Pressure 
  

Impact

• Oxidation
  

• Powder flow and recoating, Vaporisation of water of low melting compounds
  

Sensors and Remedial actions

• Electrochemical Trace oxygen sensor

• Pressure sensor
  

Processing

Potential issues

• Melt pool geometry and evolution
  

• Melt pool temperature,

• composition of the melt pool

• Lack of fusion defects

• Optical path
  
  

Impacts

• Microstructure, mechanical properties

• Microstructure, mechanical properties
  

• Microstructure, mechanical properties
  

• density

• Dimension accuracy
  

Sensors and remedial actions

• coaxial thermal melt pool imaging, light pyrometry technique to determine melt pool characteristics [7], coaxial single-color IR imaging [8], 50 fps, coaxial CMOS camera. [9] [10], melt pool width controller [11]

 

• photodiode [12],[13],[14], dual-color pyrometer [15], 

• Optical emission spectra [16] [17] [18]

• Optical emission spectra [18]
  

• [19]
  

References
[1] Edward W Reutzel Abdalla R Nassar, (2015),"A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing", Rapid Prototyping Journal, Vol. 21 Issue 2
[2] Bontha, S., Klingbeil, N.W., Kobryn, P.A., Fraser, H.L., 2006. Thermal process maps for predicting solidification microstructure in laser fabrication of thin-wall structures. J. Mater. Process. Technol. 178, 135–142.
[3] Thompson, J.R., 2014. Relating Microstructure to Process Variables In Beam-Based Additive Manufacturing of Inconel 718 (Master Of Engineering). Wright State University.
[4] Paschotta, R., 2008. Laser Noise, in: Field Guide to Lasers. Bellingham, Washington, pp. 116– 120.
[5] Johnstone, I., 2000. Beam sampling & process monitoring in laser material processing applications. Ind. Laser Solut. 20, 34–35.
[6] Ophir Photonics, 2014. Ophir Photonics’BeamWatchTM, Industry’s First Non-Contact Industrial Beam Monitoring System for Very High PowerYAG and FiberLasers.
[7] Hofmeister, W.H., MacCallum, D.O., Knorovsky, G.A., 1999. VIDEO MONITORING AND CONTROL OF THE LENS PROCESS. Presented at the American Welding Society 9th International Conference of Computer Technology in Welding, Detroit, Mi.
[8] Hu, D., Kovacevic, R., 2003. Sensing, modeling and control for laser-based additive manufacturing. Int. J. Mach. Tools Manuf. 43, 51–60.
[9] Colodron, P., Fariña, J., Rodríguez-Andina, J.J., Vidal, F., Mato, J.L., Montealegre, M.A., 2011. Performance improvement of a laser cladding system through FPGA-based control, in: IECON 2011-37th Annual Conference on IEEE Industrial Electronics Society. pp. 2814–2819.
[10] Araujo, J.R., Rodriguez-Andina, J.J., Farina, J., Vidal, F., Mato, J.L., Montealegre, M.A., 2012. FPGA-based laser cladding system with increased robustness to optical defects, in: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society. Montreal, QC, pp. 4688–4693.
[11] Hofman, J.T., Pathiraj, B., van Dijk, J., de Lange, D.F., Meijer, J., 2012. A camera based feedback control strategy for the laser cladding process. J. Mater. Process. Technol. 212, 2455–2462. 
[12] Bi, G., Gasser, A., Wissenbach, K., Drenker, A., Poprawe, R., 2006. Identification and qualification of temperature signal for monitoring and control in laser cladding. Opt. Lasers Eng. 44, 1348–1359. 
[13] Bi, G., Schürmann, B., Gasser, A., Wissenbach, K., Poprawe, R., 2007. Development and qualification of a novel laser-cladding head with integrated sensors. Int. J. Mach. Tools Manuf. 47, 555–561.
[14] Bi, G., Sun, C.N., Gasser, A., 2013. Study on influential factors for process monitoring and control in laser aided additive manufacturing. J. Mater. Process. Technol. 213, 463–468.
[15] Song, L., Bagavath-Singh, V., Dutta, B., Mazumder, J., 2011. Control of melt pool temperature and deposition height during direct metal deposition process. Int. J. Adv. Manuf. Technol. 58, 247–256.
[16] Bartkowiak, K., 2010. Direct laser deposition process within spectrographic analysis in situ. Phys. Procedia 5, 623–629.
[17] Song, L., Mazumder, J., 2011. Feedback control of melt pool temperature during laser cladding process. Control Syst. Technol. IEEE Trans. On 19, 1349–1356.
[18] Nassar, A.R., Spurgeon, T.J., Reutzel, E.W., 2014a. Sensing defects during directed-energy additive manufacturing of metal parts using optical emissions spectroscopy, in: Solid Freeform Fabrication Symposium Proceedings, University of Texas, Austin, TX.
[19] Nassar, A.R., Spurgeon, T.J., Reutzel, E.W., 2014b. Intra-Layer Control via Alteration of Path Plan in Directed Energy Deposition