This research investigates the surface treatment of AZ31B magnesium alloy using submerged waterjet peening in order to improve its mechanical properties and biocompatibility for biomedical applications. The experimental parameter conditions were set by varying jet pressure (P) from 100 MPa to 150 MPa and traverse speed (v) from 60 mm/min to 240 mm/min, with a constant standoff distance (h) of 1.5 mm at a single pass. Their effects on surface hardness and roughness were investigated. Results showed that surface roughness increased significantly at lower traverse speeds due to prolonged jet interaction, while hardness improved by up to 26.06% owing to dislocation density and grain refinement. Multi-objective optimisation using the TOPSIS method identified an optimal parameter combination of 150 MPa pressure, 120 mm/min traverse speed, and 1.5 mm standoff distance, balancing roughness and hardness. Validation by Random Forest Regression with Leave One Out Cross Validation ensured model accuracy. Surface morphology exhibited erosion-induced craters and pits because of cavitation-driven plasticity, and XRD analysis confirmed no phase transformation but an increase in dislocation density. Topographical and wettability measurements indicated increased surface energy and decreased contact angle, favoured by greater asperity density. Biocompatibility tests proved enhanced MG-63 cell viability and adhesion on peened surfaces, which verified the bio-functionality of the alloy surface modification. The results establish submerged waterjet peening as a promising, non-thermal surface treatment for AZ31B alloy surface modification for load-bearing implant applications.
RadhaRSreekanthD. Insight of magnesium alloys and composites for orthopedic implant applications–a review. J Magnes Alloy2017; 5: 286–312.
2.
NiranjanCARaghavendraTRaoMP, et al.Magnesium alloys as extremely promising alternatives for temporary orthopedic implants–A review. J Magnes Alloy2023; 11: 2688–2718.
3.
NegahbanAShamsiMSedighiM. Advances in the modification of magnesium-based biomaterials to address corrosion and corrosion-fatigue: a review of developments and prospects. J Mater Res Technol2024; 30: 4064–4108.
4.
AliMHusseinMAAl-AqeeliN. Magnesium-based composites and alloys for medical applications: a review of mechanical and corrosion properties. J Alloys Compd2019; 792: 1162–1190.
5.
FanLChenSYangM, et al.Metallic materials for bone repair. Adv Healthc Mater2024; 13: 2302132.
6.
RadhakrishnanMMohanMNatarajanY. Effect of process parameters involved in SWJP on surface integrity and biocompatibility characteristics of AZ31B Mg alloy. J Manuf Process2024; 114: 213–231.
7.
NatarajanYMurugesanPKMohanM, et al.Abrasive water jet machining process: a state of art of review. J Manuf Process2020; 49: 271–322.
8.
NarayanappaCCNagappanBVigneshP, et al.Assessment of biodegradability and biocompatibility: an experimental and comparative analysis of magnesium and magnesium-(zinc-tin)/hydroxyapatite composites. Proc Inst Mech Eng Part L J Mater Des Appl2025; 239: 1000–1012.
9.
SumanNRoySC. Cyclic behavior of material after single loading of cavitation peening. Mater Today Proc2022; 62: 7480–7486.
10.
Sekyi-AnsahJWangYQuaisieJK, et al.Surface characteristics and cavitation damage in 8090Al–Li alloy by using cavitation water jet peening processing. Iran J Sci Technol Trans Mech Eng2021; 45: 299–309.
11.
ZagarSSoyamaHGrumJ, et al.Surface integrity of heat-treatable magnesium alloy AZ80A after cavitation peening. J Mater Res Technol2022; 17: 2098–2107.
12.
SoyamaHKorsunskyAM. A critical comparative review of cavitation peening and other surface peening methods. J Mater Process Technol2022; 305: 117586.
13.
SoyamaHKujiCLiaoY. Comparison of the effects of submerged laser peening, cavitation peening and shot peening on the improvement of the fatigue strength of magnesium alloy AZ31. J Magnes Alloy2023; 11: 1592–1607.
14.
WangGYaoSChiY, et al.Improvement of titanium alloy TA19 fatigue life by submerged abrasive waterjet peening: correlation of its process parameters with surface integrity and fatigue performance. Chin J Aeronaut2024; 37: 377–390.
15.
YuvarajNPavithraEShamliCS. Investigation of surface morphology and topography features on abrasive water jet milled surface pattern of SS 304. J Test Eval2020; 48: 2981–2997.
16.
SinghBSaxenaKKDagwaIM, et al.Optimization of machining characteristics of titanium-based biomaterials: approach to optimize surface integrity for implants applications. Surf Rev Lett2025; 32: 2340008.
17.
ZouYSangZWangQ, et al.Improving the mechanical properties of 304 stainless steel using waterjet peening. Mater Sci2020; 26: 161–167.
18.
SrivastavaMHlochSGubeljakN, et al.Surface integrity and residual stress analysis of pulsed water jet peened stainless steel surfaces. Measurement ( Mahwah N J)2019; 143: 81–92.
19.
StolárikGKlichováDPoloprudskýJ, et al.Submerged surface texturing of AISI 304L using the pulsating water jet method. Arch Civ Mech Eng2024; 24: 207.
20.
YuvarajNKumarMP. Surface integrity studies on abrasive water jet cutting of AISI D2 steel. Mater Manuf Process2017; 32: 162–170.
21.
HutliENedeljkovicMBonyárA. Controlled modification of the surface morphology and roughness of stainless steel 316 by a high speed submerged cavitating water jet. Appl Surf Sci2018; 458: 293–304.
22.
RammohanSKumaranSTUthayakumarM, et al.Application of TOPSIS optimization in abrasive water jet machining of military grade armor steel. Hum Factors Mech Eng Def Saf2021; 5: 3.
23.
ChakrabortySRautRDRofinTM, et al.A narrative literature review on optimization of manufacturing processes using weighted aggregated sum product assessment (WASPAS) method. OPSEARCH2024; 62: 1–25.
24.
SinghSAgrawalVSaxenaKK, et al.Optimization on manufacturing processes at Indian industries using TOPSIS. Indian J Eng Mater Sci2023; 30: 32–44.
25.
KumarAKaurK. A novel MCDM-based framework to recommend machine learning techniques for diabetes prediction. Int J Eng Technol Innov2024; 14: 29–43.
26.
AbdullaABaryannisG. A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection. Supply Chain Anal2024; 7: 100074.
27.
JamesGWittenDHastieT, et al.An Introduction to Statistical Learning: With Applications in R. New York: Springer, 2013.
28.
KuhnM. Applied Predictive Modeling. New York: Springer, 2013.
29.
KohaviR. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc Int Joint Conf Artif Intell (IJCAI)1995; 14: 1137–1145.
30.
HastieTTibshiraniRFriedmanJ. The Elements of Statistical Learning. New York: Springer, 2009.
31.
YaoSChiYZhuX, et al.Gradient nanostructure enabled exceptional fretting fatigue properties of Inconel 718 superalloy through submerged abrasive waterjet peening. Chin J Aeronaut2025; 38: 103297.
32.
HlochSSvobodováJSrivastavaAK, et al.Submerged pulsating water jet erosion of ductile material. Wear2024; 538: 205243.
33.
AmithSCLakshmananPKumarKB, et al.Harnessing high-loading CNT in Al7075 nanocomposites by hybrid processing route: tribomechanical performance and machine learning-driven optimization. Int J Metalcast2025: 1–20.
34.
BerrarD. Cross-validation. In: Encyclopedia of bioinformatics and computational biology. Amsterdam: Elsevier, 2019, pp.542–545.
35.
BarriusoSLieblichMMultignerM, et al.Roughening of metallic biomaterials by abrasiveless waterjet peening: characterization and viability. Wear2011; 270: 634–639.
36.
KumarRRDevasenaTAbeensM. Improved mechanical properties of nanostructured 316 L stainless steel by statistically optimized low pulsed laser shock peening process for aeronautical application. Optik (Stuttg)2023; 276: 170654.
37.
AbeensMMuruganandhanRThirumavalavanK. Effect of low energy laser shock peening on plastic deformation, wettability and corrosion resistance of aluminum alloy 7075 T651. Optik (Stuttg)2020; 219: 165045.
MatosGRM. Surface roughness of dental implant and osseointegration. J Maxillofac Oral Surg2021; 20: 1–4.
41.
VishnuJAnsheedARHameedP, et al.Insights into the surface and biocompatibility aspects of laser shock peened Ti-22Nb alloy for orthopedic implant applications. Appl Surf Sci2022; 586: 152816.
42.
MingTPengQHanY, et al.Effect of water jet cavitation peening on electrochemical corrosion behavior of nickel-based alloy 600 in NaCl solution. Mater Chem Phys2023; 295: 127122.
43.
WangQTanLYangK. Cytocompatibility and hemolysis of AZ31B magnesium alloy with Si-containing coating. J Mater Sci Technol2015; 31: 845–851.
44.
KimYKParkISLeeKB, et al.Characterization and biocompatibility of a calcium-containing AZ31B alloy as a biodegradable material. J Mater Sci2015; 50: 4672–4682.
45.
ErisenDEZhangYZhangB, et al.Biosafety and biodegradation studies of AZ31B magnesium alloy carotid artery stent in vitro and in vivo. J Biomed Mater Res B Appl Biomater2022; 110: 239–248.
46.
ZhangRZhouXGaoH, et al.The effects of laser shock peening on the mechanical properties and biomedical behavior of AZ31B magnesium alloy. Surf Coat Technol2018; 339: 48–56.
47.
RahmanZUPompaLHaiderW. Electrochemical characterization and in vitro bio-assessment of AZ31B and AZ91E alloys as biodegradable implant materials. J Mater Sci Mater Med2015; 26: 217.
48.
UddinMHallCSantosV, et al.Synergistic effect of deep ball burnishing and HA coating on surface integrity, corrosion and immune response of biodegradable AZ31B Mg alloys. Mater Sci Eng C2021; 118: 111459.
49.
HouXQinHGaoH, et al.A systematic study of mechanical properties, corrosion behavior and biocompatibility of AZ31B Mg alloy after ultrasonic nanocrystal surface modification. Mater Sci Eng C2017; 78: 1061–1071.
50.
MohanMRadhakrishnanMNatarajanY. Investigation of process parameters influence in waterjet peening of AZ31B-Mg alloy for bioimplant applications. Surf Eng2024; 40: 320–335.
51.
de OliveiraLAda SilvaRMPRodasACD, et al.Surface chemistry, film morphology, local electrochemical behavior and cytotoxic response of anodized AZ31B magnesium alloy. J Mater Res Technol2020; 9: 14754–14770.
52.
BagherifardSHickeyDJFintováS, et al.Effects of nanofeatures induced by severe shot peening (SSP) on mechanical, corrosion and cytocompatibility properties of magnesium alloy AZ31. Acta Biomater2018; 66: 93–108.
