Abstract
The improvements of surface quality and dimensional accuracy are critical for Wire and Arc Additive Manufacturing (WAAM). This paper highlights a multi-objective optimization process for Bainite steel additive manufacturing. The welding design matrix for conducting the experiments was made by using the Box-Behnken design of response surface methodology (RSM). The input process parameters were varied at three levels which result in 46 experimental trials. The responses were measured during or after conducting the experiments. A second-order response surface model was developed and then multi-objective optimization was performed to obtain the desired surface appearance. The acceleration and staggered deposition processes were used to decrease the head dimension of single weld bead. The results show that the optimized sample surface appearance is smooth which has little spatters and no visible defects. Compared with the traditional processes which rely on overlapping rate adjustment but weaken the single weld bead morphology optimization, the process of this paper has comprehensive considerations of droplet transfer, heat input, and shaping coefficient. It enables the capacity of fabricating metal parts with high accuracy and lays a good foundation for Bainite steel additive manufacturing.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Huang Y, Leu MC, Mazumder J, Donmez A (2015) Additive manufacturing: current state, future potential, gaps and needs, and recommendations. J Manuf Sci Eng 137:1–10
Song YA, Park S (2006) Experimental investigations into rapid prototyping of composites by novel hybrid deposition process. J Mater Process Technol 171(1):35–40
Doumanidis C, Kwak YM (2002) Multivariable adaptive control of the bead profile geometry in gas metal arc welding with thermal scanning. Int J Press Vessel Pip 79(4):251–262
Zhang YM, Chen Y, Li P, Male AT (2003) Weld deposition-based rapid prototyping: a preliminary study. J Mater Process Technol 135(2):347–357
Almeida PMS, Williams S (2010) Innovative process model of Ti–6Al–4V additive layer manufacturing using cold metal transfer (CMT). Proceedings of the 21st Annual International Solid Freeform Fabrication Symposium 25-36
Zhang HO, Wang XP, Wang GL, Zhang Y (2013) Hybrid direct manufacturing method of metallic parts using deposition and micro continuous rolling. Rapid Prototyp J 19(6):387–394
Gomes JHF, Costa SC, Paiva AP, Balestrassi PP (2012) Mathematical modeling of weld bead geometry, quality, and productivity for stainless steel claddings deposited by FCAW. J Mater Eng Perform 21(9):1862–1872
Murugan N, Gunaraj V (2005) Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes. J Mater Process Technol 168(3):478–487
Palani PK, Murugan N (2007) Optimization of weld bead geometry for stainless steel claddings deposited by Fcaw. J Mater Process Technol 190:291–299
Palani PK, Murugan N (2006) Sensitivity analysis for process parameters in cladding of stainless steel by flux cored arc welding. J Manuf Process 8(2):90–100
Kumar VV, Murugan N (2011) Effect of FCAW process parameters on weld bead geometry in stainless steel cladding. Int J Miner Metall Mater 10(9):827–842
Xiong J, Zhang G, Gao H, Wu L (2013) Modeling of bead section profile and overlapping beads with experimental validation for robotic GMAW-based rapid manufacturing. Robot Comput Integr Manuf 29(2):417–423
Ding D, Pan Z, Cuiuri D, Li H (2015) A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM). Robot Comput Integr Manuf 31:101–110
Yang JR, Huang CY, Wang SC (1992) The development of ultra-low-carbon bainitic steels. Mater Des 13(6):335–338
Correia DS, Goncalves CV, Cunha SSD, Ferraresi VA (2005) Comparison between genetic algorithms and response surface methodology in GMAW welding optimization. J Mater Process Technol 160(1):70–76
Benyounis KY, Olabi AG (2008) Optimization of different welding processes using statistical and numerical approaches—a reference guide. Adv Eng Softw 39(6):483–496
Box GEP, Hunter JS, Hunter WG (2005) Statistics for experimenters: design, innovation, and discovery. J Am Stat Assoc 10:12
Ferreira SLC, Bruns RE, Ferreira HS, Matos GD, David JM et al (2007) Box-Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta 597(2):179–186
Sharma A, Arora N, Mishra BK (2015) Mathematical model of bead profile in high deposition welds. J Mater Process Technol 220:65–75
Korra NN, Vasudevan M, Balasubramanian KR (2014) Multi-objective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology. Int J Adv Manuf Technol 77(1–4):67–81
Prasad KS, Chalamalasetti SR, Damera NR (2015) Application of grey relational analysis for optimizing weld bead geometry parameters of pulsed current micro plasma arc welded inconel 625 sheets. Int J Adv Manuf Technol 78(1–4):625–632
Yang T, Xiong J, Chen H, Chen Y (2015) Modeling of weld bead geometry for rapid manufacturing by robotic GMAW. Int J Mod Phys B 29(10n11):1–7
Suryakumar S, Karunakaran KP, Bernard A, Chandrasekhar U, Raghavender N, Sharma D (2011) Weld bead modeling and process optimization in hybrid layered manufacturing. Comput Aided Des 43(4):331–344
Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J R Stat Soc 13(1):1–45
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Youheng, F., Guilan, W., Haiou, Z. et al. Optimization of surface appearance for wire and arc additive manufacturing of Bainite steel. Int J Adv Manuf Technol 91, 301–313 (2017). https://doi.org/10.1007/s00170-016-9621-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-016-9621-1