Optimized hatch space selection in double-scanning track selective laser melting process

  • Yu-Lung LoEmail author
  • Bung-Yo Liu
  • Hong-Chuong Tran


Additive manufacturing (AM) techniques such as selective laser melting (SLM) have many advantages over traditional manufacturing methods. However, the quality of SLM products is critically dependent on the process parameters, e.g., the laser power, scanning speed, powder layer thickness, hatch space, and scan length. Determining the parameter settings which optimize the product quality is a challenging, but extremely important problem for manufacturers. In a previous study, the present group determined the optimal values of the laser power and scanning speed for 316L stainless steel powder beds. The present study extends this work to investigate the effects of the hatch space and scan length on the melting pool characteristics in a double-scanning track SLM process. A three-dimensional finite element model is constructed to predict the features of the scan track melt pool for various values of the hatch space and scan length. A circle packing design method is then used to select a representative set of hatch space and scan length parameters to train artificial neural networks (ANNs) to predict the melt pool temperature, melt pool depth, and overlap rate between adjacent tracks. Finally, the trained ANNs are used to create process maps relating the scan track features to the hatch space and scan length. The optimal hatch space and scan length region of the temperature process map is then determined based on a joint consideration of the peak temperature (less than 3300 K), the difference in depth of adjacent melt pools (less than 10 μm), and the overlap rate of adjacent scan tracks (25~35%). The results indicate that the optimal hatch space is equal to 61% of the laser spot size given an SLM system with a laser power of 180 W, a scanning speed of 680 mm/s, a laser spot size of 120 μm, and a 316L SS powder layer thickness of 50 μm.


Selective laser melting Surrogate modeling Artificial neural network Parameter optimization Hatch space 



The authors gratefully acknowledge the financial support provided to this study by the Ministry of Science and Technology of Taiwan under Grant Nos. MOST 105-2218-E-006-015, 107-2218-E-006-051, and 108-2218-E-006-026. The research was also supported in part by the Ministry of Education, Taiwan, Headquarter of University Advancement through funding to the Intelligent Manufacturing Research Center (iMRC), National Cheng Kung University (NCKU).


  1. 1.
    Tran H-C, Lo Y-L (2019) Systematic approach for determing optimal processing parameters to produce parts with high density in selective laser melting process, major revision to The International Journal of Advanced Manufacturing TechnologyGoogle Scholar
  2. 2.
    Shi X, Ma S, Liu C, Wu Q (2017) Parameter optimization for Ti-47Al-2Cr-2Nb in selective laser melting based on geometric characteristics of single scan tracks. Opt Laser Technol 90:71–79CrossRefGoogle Scholar
  3. 3.
    Yadroitsev I, Gusarov A, Yadroitsava I, Smurov I (2010) Single track formation in selective laser melting of metal powders. J Mater Process Technol 210:1624–1631CrossRefGoogle Scholar
  4. 4.
    Han Q, Setchi R, Lacan F, Gu D, Evans SL (2017) Selective laser melting of advanced Al-Al2O3 nanocomposites: simulation, microstructure and mechanical properties. Mater Sci Eng A 698:162–173CrossRefGoogle Scholar
  5. 5.
    Han X, Zhu H, Nie X, Wang G, Zeng X (2018) Investigation on selective laser melting AlSi10Mg cellular lattice strut: molten pool morphology, surface roughness and dimensional accuracy. Materials (Basel) 11:392CrossRefGoogle Scholar
  6. 6.
    Di W, Yang L, Yongqiang Y, Dongming X (2016) Theoretical and experimental study on surface roughness of 316L stainless steel metal parts obtained through selective laser melting. Rapid Prototyp J 22:706–716CrossRefGoogle Scholar
  7. 7.
    Kruth JP, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M, Lauwers B (2004) Selective laser melting of iron-based powder. J Mater Process Technol 149:616–622CrossRefGoogle Scholar
  8. 8.
    Liu Y, Yang Y, Wang D (2016) A study on the residual stress during selective laser melting (SLM) of metallic powder. Int J Adv Manuf Technol 87:647–656CrossRefGoogle Scholar
  9. 9.
    Mercelis P, Kruth JP (2006) Residual stresses in selective laser sintering and selective laser melting. Rapid Prototyp J 12:254–265CrossRefGoogle Scholar
  10. 10.
    Kamath C, El-dasher B, Gallegos GF, King WE, Sisto A (2014) Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W. Int J Adv Manuf Technol 74:65–78CrossRefGoogle Scholar
  11. 11.
    Gusarov AV, Yadroitsev I, Bertrand P, Smurov I (2009) Model of radiation and heat transfer in laser-powder interaction zone at selective laser melting. J Heat Transf 131:072101–072101-10CrossRefGoogle Scholar
  12. 12.
    Hodge NE, Ferencz RM, Solberg JM (2014) Implementation of a thermomechanical model for the simulation of selective laser melting. Comput Mech 54:33–51MathSciNetCrossRefGoogle Scholar
  13. 13.
    Tran H-C, Lo Y-L (2018) Heat transfer simulations of selective laser melting process based on volumetric heat source with powder size consideration. J Mater Process Technol 255:411–425CrossRefGoogle Scholar
  14. 14.
    Tran H-C, Lo Y-L, Huang M-H (2017) Analysis of scattering and absorption characteristics of metal powder layer for selective laser sintering. IEEE/ASME Trans Mechatron 22:1807–1817CrossRefGoogle Scholar
  15. 15.
    Verhaeghe F, Craeghs T, Heulens J, Pandelaers L (2009) A pragmatic model for selective laser melting with evaporation. Acta Mater 57:6006–6012CrossRefGoogle Scholar
  16. 16.
    King WE, Barth HD, Castillo VM, Gallegos GF, Gibbs JW, Hahn DE et al (2014) Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J Mater Process Technol 214:2915–2925CrossRefGoogle Scholar
  17. 17.
    Roberts I, Wang C, Esterlein R, Stanford M, Mynors D (2009) A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing. Int J Mach Tools Manuf 49:916–923CrossRefGoogle Scholar
  18. 18.
    Foroozmehr A, Badrossamay M, Foroozmehr E (2016) Finite element simulation of selective laser melting process considering optical penetration depth of laser in powder bed. Mater Des 89:255–263CrossRefGoogle Scholar
  19. 19.
    Gusarov A, Laoui T, Froyen L, Titov V (2003) Contact thermal conductivity of a powder bed in selective laser sintering. Int J Heat Mass Transf 46:1103–1109CrossRefGoogle Scholar
  20. 20.
    Çengel YA, Ghajar AJ (2015) Heat and mass transfer: fundamentals & applications, 5th edn. McGraw-Hill Education, New YorkGoogle Scholar
  21. 21.
    Han L, Phatak K, Liou F (2004) Modeling of laser cladding with powder injection. Metall Mater Trans B 35:1139–1150CrossRefGoogle Scholar
  22. 22.
    Kamath C (2016) Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 86:1659–1677CrossRefGoogle Scholar
  23. 23.
    Fang K-T, Li R, Sudjianto A (2005) Design and modeling for computer experiments. CRC Press, Boca RatonCrossRefGoogle Scholar
  24. 24.
    Loh L-E, Chua C-K, Yeong W-Y, Song J, Mapar M, Sing S-L (2015) Z.-H. Liu and D-Q Zhang, Numerical investigation and an effective modelling on the selective laser melting (SLM) process with aluminium alloy 6061. Int J Heat Mass Transf 80:288–300CrossRefGoogle Scholar
  25. 25.
    Simson T, Emmel A, Dwars A, Böhm J (2017) Residual stress measurements on AISI 316L samples manufactured by selective laser melting. Addit Manuf 17:183–189CrossRefGoogle Scholar
  26. 26.
    Kruth J-P, Deckers J, Yasa E, Wauthlé R (2012) Assessing and comparing influencing factors of residual stresses in selective laser melting using a novel analysis method. Proc IME B J Eng Manufact 226:980–991CrossRefGoogle Scholar
  27. 27.
    Xia M, Gu D, Yu G, Dai D, Chen H, Shi Q (2016) Influence of hatch spacing on heat and mass transfer, thermodynamics and laser processability during additive manufacturing of Inconel 718 alloy. Int J Mach Tools Manuf 109:147–157CrossRefGoogle Scholar
  28. 28.
    Di W, Yongqiang Y, Xubin S, Yonghua C (2012) Study on energy input and its influences on single-track, multi-track, and multi-layer in SLM. Int J Adv Manuf Technol 58:1189–1199CrossRefGoogle Scholar
  29. 29.
    Wang D, Wu S, Fu F, Mai S, Yang Y, Liu Y, Song C (2017) Mechanisms and characteristics of spatter generation in SLM processing and its effect on the properties. Mater Des 117:121–130CrossRefGoogle Scholar
  30. 30.
    Taheri Andani M, Dehghani R, Karamooz-Ravari MR, Mirzaeifar R, Ni J (2018) A study on the effect of energy input on spatter particles creation during selective laser melting process. Additive Manufacturing 20:33–43CrossRefGoogle Scholar
  31. 31.
    Shi X, Ma S, Liu C, Chen C, Wu Q, Chen X, Lu J (2016) Performance of high layer thickness in selective laser melting of Ti6Al4V. Materials (Basel) 9:12Google Scholar
  32. 32.
    Wang S, Liu Y, Shi W, Qi B, Yang J, Zhang F, Han D, Ma Y (2017) Research on high layer thickness fabricated of 316L by selective laser melting. Materials (Basel) 10:9Google Scholar
  33. 33.
    Gong H, Rafi H, Starr T, Stucker B (2013) The effects of processing parameters on defect regularity in Ti-6Al-4V parts fabricated by selective laser melting and electron beam melting, presented at the 24th, Annual international solid freeform fabrication symposium; an additive manufacturing conference, proceedingsGoogle Scholar
  34. 34.
    Yasa E, Kruth JP (2011) Microstructural investigation of selective laser melting 316L stainless steel parts exposed to laser re-melting. Procedia Eng 19:389–395CrossRefGoogle Scholar
  35. 35.
    Chuang C-H, Sung T-W, Huang C-L, Lo Y-L (2012) Relative two-dimensional nanoparticle concentration measurement based on scanned laser pico-projection. Sensors Actuators B Chem 173:281–287CrossRefGoogle Scholar
  36. 36.
    Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Cheng Kung UniversityTainanTaiwan

Personalised recommendations