This study presents a systematic approach to screening, optimizing, and analyzing synergistic interactions among critical abrasive wear parameters in AA8011 aluminum alloy reinforced with boron carbide (B4C). Using a D-optimal design, the effects of load, sliding velocity, sliding distance, abrasive grit size, and B4C content (0–10 wt.%) were investigated to minimize wear rate and coefficient of friction (COF). The composites, fabricated via ultrasonic-assisted stir casting, exhibited a 38% hardness improvement with 6 wt.% B4C due to uniform dispersion and strong interfacial bonding. Statistical screening identified load, grit size, and B4C content as dominant factors, while optimization revealed that 6 wt.% B4C, 162 µm grit, and 10–15 N load reduced wear by 74% and COF by 50% compared to unreinforced AA8011. Synergistic analysis uncovered interactions where B4C reinforcement promoted protective tribolayer formation, mitigating microcutting wear mechanisms. Predictive models (R² > 0.96) were validated through ANOVA, offering a robust framework for designing wear-resistant composites. This study connects process parameters with performance, offering industries a data-driven approach to customize AA8011/B4C composites for automotive and aerospace uses. The results give engineers practical guidance to optimize material choices and operating conditions in demanding tribological applications, such as high-wear components, like brake systems in vehicles and actuators in aircraft.
Integrated Experimental Design and Optimization of Abrasive Wear Parameters of B4C-Reinforced AA8011 Matrix Composites Using D-Optimal DOE Approach.
RamanathanAKrishnanPKMuralirajaR. A review on the production of metal matrix composites through stir casting – furnace design, properties, challenges, and research opportunities. J Manuf Process2019; 42: 213–245.
2.
KhalidMYUmerRKhanKA. Review of recent trends and developments in aluminium 7075 alloy and its metal matrix composites (MMCs) for aircraft applications. Results Eng2023; 20: 101372.
3.
SakthiveluSSethusundaramPPRavichandranM, et al.Experimental investigation and analysis of properties and dry sliding wear behavior of Al -Fe-Si alloy matrix composites. Silicon2021; 13: 1285–1294.
4.
KarabulutŞKarakoçHÇıtakR. Influence of B4C particle reinforcement on mechanical and machining properties of Al6061/B4C composites. Compos Part B Eng2016; 101: 87–98.
5.
ChenHSWangWXLiYL, et al.The design, microstructure and tensile properties of B4C particulate reinforced 6061Al neutron absorber composites. J Alloys Compd2015; 632: 23–29.
6.
TalluriBKNarasimha RaoRÖzcanM, et al. Evaluating mechanical and tribological performance of B4C reinforced aluminum composites with predictive machine learning models. Results in Engineering2025; 27: 106503.
7.
IdrisiAHMouradAHI. Conventional stir casting versus ultrasonic assisted stir casting process: mechanical and physical characteristics of AMCs. J Alloys Compd2019; 805: 502–508.
8.
JayappaKAnilKCKhanZA. Enhancing wear resistance in Al-7075 composites through conventional mixing and casting techniques. J Mater Res Technol2023; 27: 7935–7945.
9.
ZhouXGaoYWangY, et al.Effects of ZrC particles, load and sliding speed on the wear behavior of the ZrC/2024Al composites. Wear2022; 506–507: 204465.
10.
KalifaMStarrAKhanM. Current research and challenges in modelling wear, friction, and noise in mechanical contacts. Proc Inst Mech Eng Part J J Eng Tribol.2025; 239: 1487–1511.
11.
SvenningssonI. On the mechanism of two-body abrasive wear in turning ‘the spin-split theory,’. Int J Adv Manuf Technol2017; 92: 3337–3348.
12.
NietoAYangHJiangL, et al.Reinforcement size effects on the abrasive wear of boron carbide reinforced aluminum composites. Wear2017; 390–391: 228–235.
KhanMMNisarM. Effect of in situ TiC reinforcement and applied load on the high-stress abrasive wear behaviour of zinc–aluminum alloy. Wear2022; 488–489: 204082.
16.
KaushikNCRaoRN. Effect of applied load and grit size on wear coefficients of Al 6082–SiC–Gr hybrid composites under two body abrasion. Tribol Int2016; 103: 298–308.
17.
KaushikNCRaoRN. Effect of grit size on two body abrasive wear of Al 6082 hybrid composites produced by stir casting method. Tribol Int2016; 102: 52–60.
18.
KaushikNSri ChaitanyaCRaoRN. Abrasive grit size effect on wear depth of stir cast hybrid Al–Mg–Si composites at high stress condition. Proc Inst Mech Eng Part J J Eng Tribol2018; 232: 672–684.
19.
SahinY. Optimization of testing parameters on the wear behaviour of metal matrix composites based on the Taguchi method. Mater Sci Eng A2005; 408: –8.
20.
DharmalingamSSubramanianRKökM. Optimization of abrasive wear performance in aluminium hybrid metal matrix composites using taguchi-grey relational analysis. Proc Inst Mech Eng Part J J Eng Tribol2013; 227: 749–760.
21.
KumarKRSreebalajiVS. Desirability based multiobjective optimisation of abrasive wear and frictional behaviour of aluminium (Al/3.25Cu/8.5Si)/fly ash composites. Tribol - Mater Surfaces Interfaces2015; 9: 128–136.
22.
SinghBKGhoshDMandalN, et al.Modeling and predicting abrasive wear behavior of Al–SiCp composite using multi-response optimization-based RSM. J Inst Eng Ser D2019; 100: 263–273.
23.
KökM. Prediction and optimisation of abrasive wear model for particle reinforced MMCs using statistical analysis. Mater Res Innov2011; 15: 366–372.
24.
VermaPKSinghA. Mechanical and dry sliding tribological characteristics of aluminium matrix composite reinforced with high entropy alloy particles. Tribol Int2024; 191: 109055.
25.
Rodríguez-RojasFZamoraVGuiberteauF, et al.Effect of the degree of densification on the unlubricated sliding-wear behaviour of monolithic B4C ceramics. Tribol Int2023; 186: 108656.
26.
KiranMDBrLYBabbarA, et al.Tribological properties of CNT-filled epoxy-carbon fabric composites: optimization and modelling by machine learning. J Mater Res Technol2024; 28: 2582–2601.
27.
ÖzdemirATurkozM. Development of a D-optimal design-based 0–1 mixed-integer nonlinear robust parameter design optimization model for finding optimum design factor level settings. Comput Ind Eng2020; 149: 1065D4.
28.
BaoQ, et al.Using mixture design approach for modeling and optimizing tribology properties of PPESK composites. Compos Part A Appl Sci Manuf2022; 163: 107256.
29.
WangJXieWRyzhovIO. Algorithms for budget-constrained D-optimal design. Math Oper Res2024; 49: 1–61.
30.
LeeC-PHuangM-NL. D-Optimal designs for second-order response surface models with qualitative factors. J Data Sci2021; 9: 139–153.
31.
ChandrasekarPNatarajanSRamkumarKR. Influence of carbide reinforcements on accumulative roll bonded Al 8011 composites. Mater Manuf Process2019; 34: 889–897.
32.
PoovazhaganLKalaichelvanKBalajiVR, et al.Development of aa6061/SiCp metal matrix composites by conventional stir casting and ultrasonic assisted casting routes - A comparative study. Adv Mater Res2014; 984–985: 384–389.
33.
GollaCBBabar PashaMRaoRN, et al.Influence of TiC particles on mechanical and tribological characteristics of advanced aluminium matrix composites fabricated through ultrasonic-assisted stir casting. Crystals (Basel)2023; 13: 1360.
34.
ChenCCChiangKTChouCC, et al.The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. Int J Adv Manuf Technol2011; 54: 465–478.
35.
Saravana KumarMBegumSRVasumathiM. Influence of stir casting parameters on particle distribution in metal matrix composites using stir casting process. Mater Res Express2019; 6. doi: 10.1088/2053-1591/ab4045
36.
GollaCBRaoRNIsmailS. Triboinformatics approach for prediction of high-stress abrasive wear and coefficient of friction in Al / TiC nanocomposites using machine learning techniques. ASME J Tribol2025; 147: 021401-1–021401-17.
37.
GudipudiSNagamuthuSSubbianKS, et al.Enhanced mechanical properties of AA6061-B4C composites developed by a novel ultra-sonic assisted stir casting. Eng Sci Technol an Int J2020; 23: 1233–1243.
38.
SharmaSC. The sliding wear behavior of A16061-garnet particulate composites. Wear2001; 249: 1036–1045.
39.
AdesinaOSAdediranAAEdoziunoFO, et al.Statistical modeling of Si-based refractory compounds of bamboo leaf and alumina reinforced Al–Si–Mg alloy hybrid composites. Sci Rep2023; 13: 1–14.
40.
Al-rubaieKSYoshimuraHNDe MelloDB. Two-body abrasive wear of Al – SiC composites. Wear.1999; 233-235: 444–454.
41.
TalluriBKRaoRN. Multi-response optimisation of wear parameters of B4C reinforced Al-Fe-Si composites: Using Taguchi-grey relational analysis. Proc Inst Mech Eng Part E J Process Mech Eng2024; 238: 4349–4359.
42.
RameshBNSureshaB. Optimization of tribological parameters in abrasive wear mode of carbon-epoxy hybrid composites. Mater Des2014; 59: 38–49.
43.
LagisettiVKReddyAPKrishnaPV. Experimental investigation on the two body abrasive wear of AA6061-SiC-Gr nano and hybrid nanocomposites. Proc Inst Mech Eng Part J J Eng Tribol2023; 237: 1027–1041.
44.
ManikandanRArjunanTVAkhilAR. Studies on micro structural characteristics, mechanical and tribological behaviours of boron carbide and cow dung ash reinforced aluminium (Al 7075) hybrid metal matrix composite. Compos Part B Eng2020; 183: 107668.
45.
BalamuruganK, et al.Effect of TiC/RHA on solid particle erosion of Al6061 hybrid composites fabricated through a 2-step ultrasonic-assisted stir casting process. J Mater Res Technol2023; 25: 4888–4900.
46.
RajendhranNBaetsPDHuangS, et al.Single-point scratch testing for understanding particle engagement in abrasion of multiphase materials. Wear2021; 476: 203689.
