Design of aviation products made of composite materials rely on damage tolerance strength allowables. A common industry practice is the usage of open hole specimens to determine the allowed strain levels for the design. However, if the design introduces a case of an edge distance that is shorter than that of the specimen that was used to determine the strength allowables, a finite width correction factor should be applied to the damage tolerance strength allowables. Such factor depends not only upon the edge distance, but also upon the layup. To this end, a machine learning based methodology is proposed in this study to obtain the finite width correction factor for any given set of layup and geometrical properties. Such approach can efficiently provide an accurate prediction of the finite width correction factor with relatively small amount of test or simulation data. The methodology also includes a procedure to determine the number of required training data points. Three different machine learning algorithms were used in this study, all providing very similar predicted regressions of the entire domain studied. Finite width correction factors carpet plots were produced for IM7/8552 composite, allowing the designer to easily obtain the required correction factor based on the layup and geometrical characteristics.
ASTM International D6484, standard test method for open-hole compressive strength of polymer matrix, 2009.
4.
De JongT. Stresses around pin-loaded holes in elastically orthotropic or isotropic plates. J Composite Mater1977; 11(3): 313–331.
5.
XiongY. On the finite width correction factor in composite laminates with elliptical inclusion. Adv Composites Lett1994; 3(6): 096369359400300604.
6.
TanSC. Laminated composites containing an elliptical opening. II. Experiment and model modification. J Composite Mater1987; 21(10): 949–968.
7.
TanSC. Finite-width correction factors for anisotropic plate containing a central opening. J Compos Mater1988; 22(11): 1080–1097.
8.
PetersonREPilkeyWDPetersonJE, et al.Peterson’s stress concentration factors. Wiley-Interscience, 1997.
9.
WangQZ. Simple formulae for the stress concentration factor for two-and three-dimensional holes in finite domains. J Strain Analysis Eng Design2002; 37(3): 259–264.
10.
HallettSRGreenBGJiangWG, et al.The open hole tensile test: a challenge for virtual testing of composites. Int J Fracture2009; 158(2): 169–181.
11.
SoutisCFleckNASmithPA. Failure prediction technique for compression loaded carbon fibre-epoxy laminate with open holes. J Compos Mater1991; 25(11): 1476–1498.
12.
SoutisC. Damage tolerance of open-hole CFRP laminates loaded in compression. Composites Eng1994; 4(3): 317–327.
13.
MartinELeguillonDCarrèreN. A coupled strength and toughness criterion for the prediction of the open hole tensile strength of a composite plate. Int Journal Sol Struct2012; 49(26): 3915–3922.
14.
BakhshandehKRajabiIRahimiF. Investigation of stress concentration factor for finite-width orthotropic rectangular plates with a circular opening using three-dimensional finite element model. Strojniski Vestnik2008; 54(2): 140.
15.
Nguyen-HoangMBeckerW. Open holes in composite laminates with finite dimensions: structural assessment by analytical methods. Arch Appl Mech2022; 92: 1101–1125.
16.
KumarSARajeshRPugazhendhiS. A review of stress concentration studies on fibre composite panels with holes/cutouts. Proc Inst Mech Eng Part L: J Mater Des Appl2020; 234(11): 1461–1472.
17.
ZobeiryNReinerJVaziriR. Theory-guided machine learning for damage characterization of composites. Compos Struct2020; 246: 112407.
18.
ReinerJVaziriRZobeiryN. Machine learning assisted characterisation and simulation of compressive damage in composite laminates. Compos Struct2021; 273: 114290.
19.
SauseMGSchmittSKalafatS. Failure load prediction for fiber-reinforced composites based on acoustic emission. Composites Sci Technol2018; 164: 24–33.
20.
PatelDKParthasarathyTPrzybylaC. Predicting the effects of microstructure on matrix crack initiation in fiber reinforced ceramic matrix composites via machine learning. Compos Struct2020; 236: 111702.
21.
CalifanoAChandaranaNGrassiaL, et al.Damage detection in composites by artificial neural networks trained by using in situ distributed strains. Appl Compos Mater2020; 27(5): 657–671.
22.
ManoharKHoganTButtrickJ, et al.Predicting shim gaps in aircraft assembly with machine learning and sparse sensing. J Manufacturing Syst2018; 48: 87–95.
23.
ZobeiryNHumfeldKD. A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Eng Appl Artif Intelligence2021; 101: 104232.
24.
SaccoCRadwanABAndersonA, et al.Machine learning in composites manufacturing: A case study of automated fiber placement inspection. Compos Struct2020; 250: 112514.
25.
GuGXChenCTRichmondDJ, et al.Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horizons2018; 5(5): 939–945.
26.
YangZYabansuYCAl-BahraniR, et al.Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Comput Mater Sci2018; 151: 278–287.
27.
GuGXChenCTBuehlerMJ. De novo composite design based on machine learning algorithm. Extreme Mech Lett2018; 18: 19–28.
28.
FreedYZobeiryNSalviatoM. Development of aviation industry-oriented methodology for failure predictions of brittle bonded joints using probabilistic machine learning. Composite Structures. 2022; 297: 115979.
29.
FreedYSalviatoMZobeiryN. Implementation of a probabilistic machine learning strategy for failure predictions of adhesively bonded joints using cohesive zone modeling. International Journal of Adhesion and Adhesives2022; 118: 103226.
30.
MarlettKNgYTomblinJ. Hexcel 8552 IM7 unidirectional prepreg 190 gsm & 35% RC qualification material property data report. Wichita, KS: National Center for Advanced Materials Performance, 2011.
31.
RasmussenCEWilliamsCKI. Gaussian processes for machine learning (adaptive computation and machine learning). Cambridge, MA: The MIT Press, 2005.
32.
CamanhoPPErçinGHCatalanottiG, et al.A finite fracture mechanics model for the prediction of the open-hole strength of composite laminates. Composites Part A: Appl Sci Manufacturing2012; 43(8): 1219–1225.
33.
GreenBGWisnomMRHallettSR. An experimental investigation into the tensile strength scaling of notched composites. Composites Part A: Appl Sci Manufacturing2007; 38(3): 867–878.
34.
CamanhoPPMaim´ıPD´avilaCG. Prediction of size effects in notched laminates using continuum damage mechanics. Composites Sci Technol2007; 67: 2715–2727.
