Abstract
This article aims to highlight the development of an intermittent controller designed to compensate and rectify the lack of fusion (LoF) zones that are induced during the LPBF process. The initial step involved the usage of the self-organizing map (SOM) algorithm to identify the location of LoF defects. Subsequently, the identified defects undergo clustering through the K-means algorithm to form a matrix of cells on the build plate. The center of each cell that encompasses the defective area is then selected as the optimal position for increasing laser power during the subsequence printed layer. To identify the optimum laser power value, various artificial voids, mimicking actual defects, are embedded in the coupons. The capping layer that closes the artificial void is then fabricated with different laser powers to heal the underlying defects. Based on the optimum laser power and defect size, several controlling rules are defined to change the laser power in situ in the targeted cells located within the capping layer of defects. The change in laser power is transferred as a laser correction file (LCF) to the actuator via the Message Queuing Telemetry Transport (MQTT) broker. Finally, the performance of the controller is evaluated by designing and fabricating two new sets of experiments, including artificial and randomized defects. The results are validated by performing a micro CT scan, in which the density of defects is analyzed on parts produced with and without the controller. The results suggest that the use of the controller increased the density of the sample with randomized defects by up to 1%.
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References
Toyserkani E, Sarker D, Ibhadode OO, Liravi F, Russo P, Taherkhani K (2021) Metal additive manufacturing, 1st edn. John Wiley https://doi.org/10.1002/9781119210801
Mazzoleni L, Demir AG, Caprio L, Pacher M, Previtali B (2020) Real-time observation of melt pool in selective laser melting: spatial, temporal, and wavelength resolution criteria. IEEE Trans Instrum Meas 69(4):1179–1190. https://doi.org/10.1109/TIM.2019.2912236
Mazzoleni L, Caprio L, Pacher M, Demir AG, Previtali B (2019) External illumination strategies for melt pool geometry monitoring in SLM. JOM 71(3):928–937. https://doi.org/10.1007/s11837-018-3209-1
Vasileska E, Demir AG, Colosimo BM, Previtali B (2020) Layer-wise control of selective laser melting by means of inline melt pool area measurements. J Laser Appl 32(2):022057. https://doi.org/10.2351/7.0000108
Yakout M, Phillips I, Elbestawi MA, Fang Q (2021) In-situ monitoring and detection of spatter agglomeration and delamination during laser-based powder bed fusion of Invar 36. Opt Laser Technol 136:106741. https://doi.org/10.1016/j.optlastec.2020.106741
Krauss H, Zeugner T, Zaeh MF (2014) Layerwise monitoring of the Selective Laser Melting process by thermography. Phys Procedia 56(C):64–71. https://doi.org/10.1016/j.phpro.2014.08.097
Krauss H, Eschey C, Zaeh MF (2012) Thermography for monitoring the selective laser melting process, in 23rd Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference. SFF 2012:999–1014
Mohr G, Scheuschner N, Hilgenberg K (2020) In situ heat accumulation by geometrical features obstructing heat flux and by reduced inter layer times in laser powder bed fusion of AISI 316L stainless steel. Procedia CIRP 94:155–160. https://doi.org/10.1016/j.procir.2020.09.030
Mohr G, Nowakowski S, Altenburg SJ, Maierhofer C, Hilgenberg K (2020) Experimental determination of the emissivity of powder layers and bulk material in laser powder bed fusion using infrared thermography and thermocouples. Metals (Basel) 10(11):1–36. https://doi.org/10.3390/met10111546
Mohr G et al (2020) In-situ defect detection in laser powder bed fusion by using thermography and optical tomography—comparison to computed tomography. Metals (Basel) 10(1):103. https://doi.org/10.3390/met10010103
Moylan S, Whitenton E, Lane B, Slotwinski J (2014) Infrared thermography for laser-based powder bed fusion additive manufacturing processes. AIP Conference Proceedings 1581:1191–1196. https://doi.org/10.1063/1.4864956
Schilp J, Seidel C, Krauss H, Weirather J (2014) Investigations on temperature fields during laser beam melting by means of process monitoring and multiscale process modelling. Adv Mech Eng 6(2014):217584. https://doi.org/10.1155/2014/217584
Neef A, Seyda V, Herzog D, Emmelmann C, Schönleber M, Kogel-Hollacher M (2014) Low coherence interferometry in selective laser melting. Phys Procedia 56(C):82–89. https://doi.org/10.1016/j.phpro.2014.08.100
Kanko JA, Sibley AP, Fraser JM (2016) In situ morphology-based defect detection of selective laser melting through inline coherent imaging. J Mater Process Technol 231(n/a):488–500. https://doi.org/10.1016/j.jmatprotec.2015.12.024
Fleming TG, Nestor SGL, Allen TR, Boukhaled MA, Smith NJ, Fraser JM (2020) Tracking and controlling the morphology evolution of 3D powder-bed fusion in situ using inline coherent imaging. Addit Manuf 32:100978. https://doi.org/10.1016/j.addma.2019.100978
DePond PJ et al (2018) In situ measurements of layer roughness during laser powder bed fusion additive manufacturing using low coherence scanning interferometry. Mater Des 154:347–359. https://doi.org/10.1016/j.matdes.2018.05.050
Zhao C et al (2017) Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction. Sci Rep 7(1):1–11. https://doi.org/10.1038/s41598-017-03761-2
Martin AA et al (2019) Dynamics of pore formation during laser powder bed fusion additive manufacturing. Nat Commun 10(1):1–10. https://doi.org/10.1038/s41467-019-10009-2
Guo Q et al (2019) In-situ characterization and quantification of melt pool variation under constant input energy density in laser powder bed fusion additive manufacturing process. Addit Manuf 28:600–609. https://doi.org/10.1016/j.addma.2019.04.021
Leung CLA, Marussi S, Atwood RC, Towrie M, Withers PJ, Lee PD (2018) In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing. Nat Commun 9(1):1–9. https://doi.org/10.1038/s41467-018-03734-7
Lhuissier P et al (2020) In situ 3D X-ray microtomography of laser-based powder-bed fusion (L-PBF)—A feasibility study. Addit Manuf 34:101271. https://doi.org/10.1016/j.addma.2020.101271
Pandiyan V et al (2022) Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance. Addit Manuf 58:103007. https://doi.org/10.1016/J.ADDMA.2022.103007
Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL (2019) Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf 27:42–53. https://doi.org/10.1016/j.addma.2019.01.006
Jayasinghe S, Paoletti P, Sutcliffe C, Dardis J, Jones N, Green PL (2022) Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. Prog Addit Manuf 7(2):143–160. https://doi.org/10.1007/s40964-021-00219-w
Egan DS, Ryan CM, Parnell AC, Dowling DP (2021) Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials. J Manuf Process 64:1248–1254. https://doi.org/10.1016/j.jmapro.2021.03.002
Bisht M, Ray N, Verbist F, Coeck S (2018) Correlation of selective laser melting-melt pool events with the tensile properties of Ti-6Al-4V ELI processed by laser powder bed fusion. Addit Manuf 22:302–306. https://doi.org/10.1016/j.addma.2018.05.004
Islam M, Purtonen T, Piili H, Salminen A, Nyrhilä O (2013) Temperature profile and imaging analysis of laser additive manufacturing of stainless steel. Phys Procedia 41:835–842. https://doi.org/10.1016/j.phpro.2013.03.156
Gutknecht K, Haferkamp L, Cloots M, Wegener K (2020) Determining process stability of laser powder bed fusion using pyrometry. Procedia CIRP 95:127–132. https://doi.org/10.1016/j.procir.2020.01.147
Haines MP, Peter NJ, Babu SS, Jägle EA (2020) In-situ synthesis of oxides by reactive process atmospheres during L-PBF of stainless steel. Addit Manuf 33:101178. https://doi.org/10.1016/j.addma.2020.101178
Renken V, von Freyberg A, Schünemann K, Pastors F, Fischer A (2019) In-process closed-loop control for stabilising the melt pool temperature in selective laser melting. Prog Addit Manuf 4(4):411–421. https://doi.org/10.1007/s40964-019-00083-9
Eschner N, Weiser L, Häfner B, Lanza G (2020) Classification of specimen density in laser powder bed fusion (L-PBF) using in-process structure-borne acoustic process emissions. Addit Manuf 34:101324. https://doi.org/10.1016/j.addma.2020.101324
Eschner N, Weiser L, Häfner B, Lanza G (2020) Development of an acoustic process monitoring system for selective laser melting (SLM). In: Solid Freeform Fabrication 2018: Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference. SFF 2018, pp 13–15. https://doi.org/10.26153/tsw/17205
Everton S, Dickens P, Tuck C, Dutton B (2018) Using laser ultrasound to detect subsurface defects in metal laser powder bed fusion components. JOM 70(3):378–383. https://doi.org/10.1007/s11837-017-2661-7
Everton SK, Dickens P, Tuck C, Dutton B (2016) Identification of sub-surface defects in parts produced by additive manufacturing, using laser generated ultrasound. Mater Sci Technol Conf Exhibit 2016, MS and T 2016 1:141–148
Everton S, Dickens P, Tuck C, Dutton B (2015) Evaluation of laser ultrasonic testing for inspection of metal additive manufacturing. Laser 3D Manufacturing II 9353:145–152. https://doi.org/10.1117/12.2078768
Everton S, Dickens P, Tuck C, Dutton B, Wimpenny D (2017) The use of laser ultrasound to detect defects in laser melted parts, in TMS 2017 146th Annual Meeting & Exhibition Supplemental Proceedings, pp. 105–116. https://doi.org/10.1007/978-3-319-51493-2_11
Wasmer K, Le-Quang T, Meylan B, Shevchik SA (2019) In situ quality monitoring in AM using acoustic emission: a reinforcement learning approach. J Mater Eng Perform 28(2):666–672. https://doi.org/10.1007/s11665-018-3690-2
Wasmer K, Kenel C, Leinenbach C, SA (2017) Shevchik, In situ and real-time monitoring of powder-bed AM by combining acoustic emission and artificial intelligence, in Industrializing Additive Manufacturing - Proceedings of Additive Manufacturing in Products and Applications - AMPA2017, Springer, Cham, pp. 200–209. https://doi.org/10.1007/978-3-319-66866-6_20
Dryburgh P et al (2019) Spatially resolved acoustic spectroscopy for texture imaging in powder bed fusion nickel superalloys. AIP Confer Proceed 2102:020004. https://doi.org/10.1063/1.5099708
Slotwinski JA, Garboczi EJ, Hebenstreit KM (2014) Porosity measurements and analysis for metal additive manufacturing process control. J Res Natl Inst Stand Technol 119:494. https://doi.org/10.6028/jres.119.019
Toyserkani E, Khajepour A, Corbin S (2004) Laser cladding. CRC PRESS LLC. https://doi.org/10.1201/9781420039177
Taherkhani K, Sheydaeian E, Eischer C, Otto M, Toyserkani E (2021) Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion. Addit Manuf 46:102152. https://doi.org/10.1016/J.ADDMA.2021.102152
Abdelrahman M, Reutzel EW, Nassar AR, Starr TL (2017) Flaw detection in powder bed fusion using optical imaging. Addit Manuf 15(n/a):1–11. https://doi.org/10.1016/j.addma.2017.02.001
Grasso M, Demir AG, Previtali B, Colosimo BM (2018) In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robot Comput Integr Manuf 49(n/a):229–239. https://doi.org/10.1016/j.rcim.2017.07.001
Zenzinger G, Bamberg J, Ladewig A, Hess T, Henkel B, Satzger W (2015) Process monitoring of additive manufacturing by using optical tomography. AIP Conf Proc 1650(1):164–170. https://doi.org/10.1063/1.4914606
Bamberg J, Zenzinger G, Ladewig A (2016) In-process control of selective laser melting by quantitative optical tomography. In: 19th World Conference on Non-Destructive Testing. NDT, p 8. https://www.ndt.net/article/wcndt2016/papers/th1b1.pdf
Gögelein A, Ladewig A, Zenzinger G, Bamberg J (2018) Process monitoring of additive manufacturing by using optical tomography, https://doi.org/10.21611/qirt.2018.004
Barua S, Liou F, Newkirk J, Sparks T (2014) Vision-based defect detection in laser metal deposition process. Rapid Prototyp J. https://doi.org/10.1108/RPJ-04-2012-0036
Lane B, Whitenton EP (2015) Calibration and measurement procedures for a high magnification thermal camera. Natl Inst Stand Technol. https://doi.org/10.6028/NIST.IR.8098
Lott P, Schleifenbaum H, Meiners W, Wissenbach K, Hinke C, Bültmann J (2011) Design of an optical system for the in situ process monitoring of selective laser melting (SLM). Phys Procedia 12:683–690. https://doi.org/10.1016/j.phpro.2011.03.085
Kwon O et al (2020) A deep neural network for classification of melt-pool images in metal additive manufacturing. J Intell Manuf 31:375–386. https://doi.org/10.1007/s10845-018-1451-6
Caggiano A, Zhang J, Alfieri V, Caiazzo F, Gao R, Teti R (2019) Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann 68(1):451–454. https://doi.org/10.1016/j.cirp.2019.03.021
Shevchik SA, Kenel C, Leinenbach C, Wasmer K (2018) Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit Manuf 21:598–604. https://doi.org/10.1016/j.addma.2017.11.012
Snow Z, Diehl B, Reutzel EW, Nassar A (2021) Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. J Manuf Syst 59(October 2020):12–26. https://doi.org/10.1016/j.jmsy.2021.01.008
Zhang Y, Yang S, Dong G, Zhao YF (2021) Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model. Addit Manuf 41(June 2020):101946. https://doi.org/10.1016/j.addma.2021.101946
Gaikwad A, Imani F, Rao P, Yang H, Reutzel E (2019) Design rules and in-situ quality monitoring of thin-wall features made using laser powder bed fusion, in ASME 2019 14th International Manufacturing Science and Engineering Conference, MSEC 2019, vol. 58745, p. V001T01A039, https://doi.org/10.1115/MSEC2019-3035
Scime L, Beuth J (2019) Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 25:151–165. https://doi.org/10.1016/j.addma.2018.11.010
Ye D, Hong GS, Zhang Y, Zhu K, Fuh JYH (2018) Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int J Adv Manuf Technol 96(5–8):2791–2801. https://doi.org/10.1007/s00170-018-1728-0
Petrich J, Gobert C, Phoha S, Nassar AR, Reutzel EW (2017) Machine learning for defect detection for PBFAm using high resolution layerwise imaging coupled with post-build CT scans. In: Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference. SFF 2017, pp 1363–1381. https://hdl.handle.net/2152/89950
Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S (2018) Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit Manuf 21(n/a):517–528. https://doi.org/10.1016/j.addma.2018.04.005
Yadav P, Rigo O, Arvieu C, Le Guen E, Lacoste E (2021) Data treatment of in situ monitoring systems in selective laser melting machines. Adv Eng Mater 23(5):1–15. https://doi.org/10.1002/adem.202001327
Tapia G, Khairallah S, Matthews M, King WE, Elwany A (2018) Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. Int J Adv Manuf Technol 94(9):3591–3603. https://doi.org/10.1007/s00170-017-1045-z
Tapia G, Elwany AH, Sang H (2016) Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit Manuf 12:282–290. https://doi.org/10.1016/j.addma.2016.05.009
Meng L, Zhang J (2020) Process design of laser powder bed fusion of stainless steel using a gaussian process-based machine learning model. JOM 72(1):420–428. https://doi.org/10.1007/s11837-019-03792-2
Zhang M et al (2019) High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach. Int J Fatigue 128:105194. https://doi.org/10.1016/j.ijfatigue.2019.105194
Scime L, Beuth J (2018) Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf 19(n/a):114–126. https://doi.org/10.1016/j.addma.2017.11.009
Colosimo BM, Grasso M (2018) Spatially weighted PCA for monitoring video image data with application to additive manufacturing. J Qual Technol 50(4):391–417. https://doi.org/10.1080/00224065.2018.1507563
Taherkhani K, Eischer C, Toyserkani E (2022) An unsupervised machine learning algorithm for in-situ defect-detection in laser powder-bed fusion. Manuf Process 81:476–489. https://doi.org/10.1016/j.jmapro.2022.06.074
Fathizadan S, Ju F, Lu Y (2021) Deep representation learning for process variation management in laser powder bed fusion. Addit Manuf 42:101961. https://doi.org/10.1016/j.addma.2021.101961
Zhang Y, Hong GS, Ye D, Zhu K, Fuh JYH (2018) Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater Des 156:458–469. https://doi.org/10.1016/j.matdes.2018.07.002
Yan H, Grasso M, Paynabar K, Colosimo BM (2022) Real-time detection of clustered events in video-imaging data with applications to additive manufacturing. IISE Trans 54(5):464–480. https://doi.org/10.1080/24725854.2021.1882013
Knaak C, Masseling L, Duong E, Abels P, Gillner A (2021) Improving build quality in laser powder bed fusion using high dynamic range imaging and model-based reinforcement learning. IEEE Access 9:55214–55231. https://doi.org/10.1109/ACCESS.2021.3067302
Kruth J, Mercelis P, Van Vaerenbergh J, Craeghs T (2007) Feedback control of selective laser melting. In: Proc. 3rd Int Conf Adv Res Virtual Rapid Prototyp. Taylor & Francis Ltd., pp 521–528. https://lirias.kuleuven.be/66104?limo=0
Craeghs T, Bechmann F, Berumen S, Kruth JP (2010) Feedback control of layerwise laser melting using optical sensors. Phys Procedia 5(PART 2):505–514. https://doi.org/10.1016/j.phpro.2010.08.078
Fuchs L, Eischer C (2018) In-process monitoring systems for metal additive manufacturing. White Pap., p 20. Available: https://www.eos-apac.info/upload/2020-07/159522956575650000.pdf
Taherkhani K (2022) In-situ monitoring and quality assurance algorithms for laser powder-bed fusion. University of Waterloo. http://hdl.handle.net/10012/18142
Coppen R, Banks A, Briggs E, Borgendale K, Gupta R (2019) MQTT Version 5.0. OASIS Standard. https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html
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The authors received the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) Network for Holistic Innovation in Additive Manufacturing (HI-AM).
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The authors confirm contribution to the paper as follows:
- Study conception and design: Katayoon Taherkhani, Gerd Cantzler, Christopher Eischer, Ehsan Toyserkani
- Data collection and analysis: Katayoon Taherkhani, Gerd Cantzler
- Interpretation of results: Katayoon Taherkhani, Christopher Eischer, Ehsan Toyserkani
- Draft manuscript preparation: Katayoon Taherkhani, Ehsan Toyserkani
- Revise the manuscript: Katayoon Taherkhani, Gerd Cantzler, Christopher Eischer, Ehsan Toyserkani
- All authors reviewed the results and approved the final version of the manuscript.
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Taherkhani, K., Cantzler, G., Eischer, C. et al. Development of control systems for laser powder bed fusion. Int J Adv Manuf Technol 129, 5493–5514 (2023). https://doi.org/10.1007/s00170-023-12663-9
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DOI: https://doi.org/10.1007/s00170-023-12663-9