High-accuracy calibration for multi-laser powder bed fusion via in situ detection and parameter identification

Abstract

Multi-laser powder bed fusion (ML-PBF) adopts multiple laser-scanner systems to increase the build envelope and build speed, but its calibration is an iterative and time-consuming process. In particular, multiple large-scale scan fields have a complex distortion in the overlap area, challenging the calibration process. In this study, owing to the enormous workload and alignment problems in the calibration of multiple scan fields, a novel calibration system is designed in this study to realize in situ auto-detection of numerous laser spots in the build chamber to ensure high efficiency and accuracy. Moreover, because the detectable area could not cover the entire build area and the detection data still contained errors, a virtual laser-scanner system was established by identifying the assembly defects and galvo nonlinearities of the ML-PBF system from the detection data, which served as the system's controller to improve calibration accuracy. The multi-field alignment error was less than 0.012%, which could avoid the intersection and separation of scan paths in multi-laser scanning and therefore meet the requirements for high-precision ML-PBF. Finally, the reliability of the method was verified theoretically using principal component analysis.

Keywords

Powder bed fusion / Multi-laser technology / Galvo calibration / Assembly defects / System identification

References

[1] Keremes JJ, Haynes JD, Gao Y et al (2013) Laser configuration for additive manufacturing. In US Patent Application: US 20130112672 A1

[2] Masoomi M, Thompson SM, Shamsaei N. Laser powder bed fusion of Ti-6Al-4V parts: thermal modeling and mechanical implications. Int J Mach Tools Manuf, 2017, 118: 73-90.

[3] Keicher DM, Miller WD (2001) Multiple beams and nozzles to increase deposition rate. In US Patent: US6268584

[4] Masoomi M, Thompson SM, Shamsaei N. Quality part production via multi-laser additive manufacturing. Manuf Lett, 2017, 13: 15-20.

[5] Zhang C, et al. A comparative study on single-laser and multi-laser selective laser melting AlSi10Mg: defects, microstructure and mechanical properties. Mater Sci Eng A, 2019, 746: 416-423.

[6] Heeling T, Zimmermann L, Wegener K (2016) Multi-beam strategies for the optimization of the selective laser melting process. In: proceedings of the 27th annual international solid freeform fabrication symposium, The University of Texas at Austin, US

[7] Eberle G, Dold C, Wegener K. Building a vector model representation of a two-axis laser scanhead using numerical analysis for simulation purposes. Int J Model Identif Control, 2013, 20(3): 199-207.

[8] Godineau K, Lavernhe S, Tournier C (2017) Opto-mechanical modelling of an additive manufacturing laser scanning head including assembly defects. In: joint special interest groupmeeting between EUSPEN and ASPE: dimensional accuracy and surface finish in additive manufacturing, Leuven, Belgium, 2017

[9] Mayer JR, Parker GA. A portable instrument for 3-D dynamic robot measurements using triangulation and laser tracking. IEEE Trans Robot Autom, 1994, 10(4): 504-516.

[10] Li G, et al. Geometry algorithm of galvanometer system correction for rapid prototyping of machine parts. Mach Tool Hydraul, 2014, 42(19): 28-30.

[11] Han WP, Meng W, Li YX et al (2011) Error analysis and correction methods of dual galvanometer scanning. Electro-Opt Technol Appl 26(4):14–18

[12] William II (2014) Effective calibration and implementation of galvanometer scanners as applied to direct metal laser sintering. In: Proceedings of the ASPE 2014 spring topical meeting ASPE 2014 spring topical meeting: dimensional accuracy and surface finish in additive manufacturing. American Society for Precision Engineering, Berkeley, CA

[13] Lu Y, Badarinath R, Lehtihet EA et al (2017) Experimental sampling of the Z-axis error and laser positioning error of an EOSINT M280 DMLS machine. Addit Manuf 21(1):501–516

[14] Delgado MAO, Lasagni AF. Reducing field distortion for galvanometer scanning system using a vision system. Opt Lasers Eng, 2016, 86: 106-114.

[15] Yeung H, Lane BM, Donmez MA et al (2020) In-situ calibration of laser/galvo scanning system using dimensional reference artefacts. CIRP Ann Manuf Technol 69(1):441–444

[16] Chen G, Zhang Y, Tian P. An error compensation method based on machine vision for laser-processing systems with galvanometers. Appl Phys B, 2020, 127(1): 1-9.

[17] Manakov A, Seidel HP, Ihrke I (2011) A mathematical model and calibration procedure for galvanometric laser scanning systems. In: Eisert P, Polthier K, Hornegger J (eds) Vision, modeling, and visualization (2011), Eurographics Association, pp 207–214

[18] Mnerie C, Preitl S, Duma VF (2013) Mathematical model of a galvanometer-based scanner: simulations and experiments. In: Modeling Aspects in Optical Metrology IV, SPIE, 2013. https://doi.org/10.1117/12.2020462

[19] Chen MF, Chen YP. Compensating technique of field-distorting error for the CO₂ laser galvanometric scanning drilling machines. Int J Mach Tools Manuf, 2007, 47(7–8): 1114-1124.

[20] Xie J, et al. Correction of the image distortion for laser galvanometric scanning system. Opt Laser Technol, 2005, 37(4): 305-311.

[21] Godineau K, Lavernhe S, Tournier C. Calibration of galvanometric scan heads for additive manufacturing with machine assembly defects consideration. Addit Manuf, 2019, 26: 250-257.

[22] Matsuka D, Tanaka T, Iwasaki M. Thermal demagnetization compensation for fast and precise positioning in galvanometer scanners. IEEE Trans Ind Electron, 2016, 63(9): 5514-5522.

[23] Zaeh M, Pieczona S. Adaptive inverse control of a galvanometer scanner considering the structural dynamic behavior. CIRP Ann, 2018, 67(1): 385-388.

[24] Blais F. Control of low inertia galvanometers for high precision laser scanning systems. Opt Eng, 1988, 27(2): 272104.

[25] Seki K, Kannami H, Iwasaki M et al (2010) Application of self-sensing actuation using piezoelectric element for vibration suppression of galvanometric mirror. In: 2010 IEEE/ASME international conference on advanced intelligent mechatronics, IEEE, 2010

[26] Hu C, Wang X, Song B. High-performance position-sensitive detector based on the lateral photoelectrical effect of two-dimensional materials. Light Sci Appl, 2020, 9(1): 1-9.

[27] Zhang W, et al. An improved method for spot position detection of a laser tracking and positioning system based on a four-quadrant detector. Sensors, 2019, 19(21): 4722.

[28] Lasemi A, Xue D, Gu P. Accurate identification and compensation of geometric errors of 5-axis CNC machine tools using double ball bar. Meas Sci Technol, 2016, 27(5): 055004.

[29] Aylward RP. Advanced galvanometer-based optical scanner design. Sens Rev, 2003, 23: 216-222.

[30] Mnerie C, Preitl S, Duma V-F. Galvanometer-based scanners: mathematical model and alternative control structures for improved dynamics and immunity to disturbances. Int J Struct Stab Dyn, 2017, 17(05): 1740006.

[31] Yeung H, et al. Implementation of advanced laser control strategies for powder bed fusion systems. Procedia Manuf, 2018, 26: 871-879.

[32] Moylan S, et al. An additive manufacturing test artifact. J Res Natl Inst Stand Technol, 2014, 119: 429.

[33] Moylan S, Cooke A, Jurrens K et al (2012) A review of test artifacts for additive manufacturing. Report No. NISTIR, 7858, National Institute of Standards and Technology (NIST), Gaithersburg, MD. https://doi.org/10.6028/NIST.IR.7858

[34] Lo Y-L, Liu B-Y, Tran H-C. Optimized hatch space selection in double-scanning track selective laser melting process. Int J Adv Manuf Technol, 2019, 105(7–8): 2989-3006.

[35] Norliza A, Ahmad MH, Adnan R, Adnan N. A comparative study on some methods for handling multicollinearity problems. Mathematika, 2006, 22: 109-119.

[36] Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. Biocomputing 2000, 1999 World Scientific 455-466.

Funding

National High-tech Research and Development Program http://dx.doi.org/10.13039/501100012164(2015AA042503)

K. C. Wong Education Foundation http://dx.doi.org/10.13039/501100012692