Finite Element Analysis Fagan Pdf 25
Virginie Fayad
A major prerequisite of the study of the evolution of the primate craniofacial complex is to develop an understanding of how mechanical factors influence facial growth and adult morphology (e.g. Moss, 1973; Oyen et al. 1979; Dechow & Carlson, 1990). This issue can be approached in several ways, but that adopted in this paper is to develop FEMs of macaque faces at different stages during growth and to explore how stresses and strains experienced during static loading are related to bone modelling. Finite element analysis (FEA) is increasingly being used to test hypotheses pertaining to the functional morphology of the primate craniodental system (Spears & Crompton, 1996; Daegling & Hylander, 1997, 2000; Chen & Chen, 1998; Spears & Macho, 1998; Macho & Spears, 1999; McConnell & Crompton, 2001; Witzel & Preuschoft, 2002; Preuschoft & Witzel, 2004; Witzel et al. 2004; Macho et al. 2005; Marinescu et al. 2005; Richmond et al. 2005; Ross et al. 2005; Strait et al. 2005). The reliability of models ought to be tested against real-world data before they can be employed confidently (Richmond et al. 2005). Several authors have recognized this need; thus, Fagan et al. (2002) carried out sensitivity studies of FEMs of intervertebral discs, and Sellers & Crompton (2004) and Wang et al. (2004) undertook both validation and sensitivity studies to dynamic models of the masticatory and locomotor systems. Those cranial FEA studies that do take the need for verification and sensitivity into account have done so, for example, by comparing the FEM with known muscle physiological data (Ross et al. 2005) or by referring to material properties of bone obtained by mechanical testing (Strait et al. 2005). Additionally, in vitro experimental strain analyses have enabled validation of FEMs (e.g. Marinescu et al. 2005). The latter approach offers the particular advantage of a better control of the loads and boundary conditions, model geometry and elastic properties of the materials defined in the model. Moreover, the strain gauge locations in the experimental specimens can be precisely recorded, allowing an accurate comparison between experimental and simulated strain results.
Calvarial reconstruction in craniosynostosis can be optimized using various computational tools. The finite element method (FEM) is a well-established tool that has been widely used to design, develop, and optimize various mechanical structures such as aeroplanes and bridges [e.g., Fagan, 1992]. In brief, FEM works by dividing the geometry of the problem under investigation into a finite number of sub-regions, called elements. The elements are connected together at their corners and sometimes along their mid-side points, called nodes. For mechanical stress analysis, a variation in displacement (e.g., linear or quadratic) is then assumed through each element, and equations describing the behaviour of each element are derived in terms of the (initially unknown) nodal displacements. These element equations are then combined to generate a set of system equations that describe the behaviour of the whole problem. After modifying the equations to account for the boundary conditions applied to the problem, these system equations are solved. The output is a list of all the nodal displacements. The element strains can then be calculated from the displacements and the stresses from the strains. This method can be then performed iteratively to optimize a particular design to achieve a certain displacement or level of strain and stress considering the loading applied to the system and its requirements.
FEM was introduced to the field of orthopaedic trauma in the 1950s [Huiskes and Chao, 1983] and is nowadays widely used in design and development of various implantable devices. Perhaps the earliest finite element (FE) analysis of the craniofacial system dates back to the 1970s [e.g., Hardy and Marcal, 1973; Tanne et al., 1988; Lestrel, 1989]. For example, Hardy and Marcal [1973] developed a simplified model of the skull and concluded that it is well designed for resistance to anterior loads. There are a large number of studies that have used FEM in a wide range of application on the craniofacial system. Many studies have used FEM for example in the field of craniofacial injury and trauma with a number of studies focusing on adult as well as infant-related trauma [e.g., Horgan and Gilchrist, 2003; Roth et al., 2010; Wang et al., 2016; Dixit and Liu, 2017; Ghajari et al., 2017]. At the same time in the past 20 years, evolutionary biologists and functional morphologists have widely used this technique to understand the form and function of craniofacial systems in an evolutionary context [e.g., Rayfield, 2007; Moazen et al., 2009; Wang et al., 2010; O'Higgins et al., 2011; Prado et al., 2016]. More recently, this technique has been used to understand the biomechanics of craniofacial development and its associated congenital diseases such as cleft lip/palate and craniosynostosis [e.g., Remmler et al., 1998; Pan et al., 2007; Khonsari et al., 2013; Jin et al., 2014; Lee et al., 2017; Marghoub et al., 2018].
A summary of model development from computed tomography (A) to a 3D reconstructed model of the skull preoperatively (B), to a 3D virtual reconstruction postoperatively (C), and then to finite element predictions (D), here due to constant pressure applied to the inner surface of the skull (modified with permission from Wolański et al., 2013).
Finite element analysis should be based on correct modeling. Consequently, the complete modeling process is the most recommended. However, in practice, it is permissible for certain modeling steps to be removed owing to computational efficiency considerations. According to the use distribution of each modeling process as shown in Figure 2A, the 3D geometry model and finite element analysis seem to be the two most essential procedures in the FEA of ICs. In addition, scientific software selection is equally critical for accurate modeling. Therefore, the frequency of regularly used software in each modeling process is also provided in Figure 2B. According to the statistics, Mimics (Materialise, Inc., Leuven, Belgium) is the most extensively used software for 3D geometric models. The following software: Geomagic (Geomagic, Inc., North Carolina, United States), Solidworks (Solidworks, Inc., Massachusetts, United States), and Hypermesh (Altair Technologies, Inc., California, United States) are the most commonly used software for the smoothing process, solid modeling, and meshing process, accounting for 8, 10, and 10 applications, respectively. Accounting 29 and 17, respectively, show that Abaqus (Simulia, Inc., Rhode Island, United States) and Ansys (Ansys, Inc., Michigan, United States) are the two most used finite element software, proving their relevance in related research. Only a few researchers employ programming software, with Matlab software tools being the most popular. Abaqus and Ansys, on the other hand, have their own programmable language components, Abaqus subroutine UMAT and Ansys APDL, which may be used to program simulations. These popular software programs assist in guaranteeing the relative accuracy of study outcomes and are appropriate for researchers undertaking relevant investigations.
The Apatite-Wollastonite (A/W) bioceramic composite is a bioactive and compatible material used for hard tissue repair; whether it can withstand enough physiological loading to be used in ICs is of great interest. The biomechanical behavior of a novel apatite-wollastonite interbody cage was evaluated in a finite element model of the lumbar spine (Bozkurt et al., 2018). The findings provide favorable support for the A/W bioceramic composite as an effective material used for interbody fusion.
Porous cage optimization typically employs computational algorithms to optimize the design of cage morphology for cage subsidence. A finite element study used optimization algorithms to design a porous cage with optimal anti-subsidence morphology and simulated the PLIF surgery to evaluate the biomechanical properties of the optimal cage at various porosities (69%, 80%, and 85%) (Tsai et al., 2016). The results revealed that porosity of 69% and 80% resulted in better biomechanical performance, and the subsidence resistance of the optimum design was superior to conventional cage designs. In 2018, a porous cage optimized scheme combining multiscale mechanics and density-based topology optimization was proposed, and the simulation results suggested that the optimized cage had a lower risk of subsidence (Moussa et al., 2018). A more recent study designed a novel porous cage using a novel global-local topology optimization method to reduce the risk of cage subsidence and stress shielding (Wang et al., 2020). The ideal biomechanical effect of the optimized cage was validated by evaluating the biomechanical properties of the optimized cage in TLIF surgery simulation.
PLIF was one of the initial approaches to lumbar interbody fusion, first tried in 1940. In 1982, the TLIF was developed to reduce the risks and limitations associated with the PLIF procedure while maintaining spine stability (Fan et al., 2021b). The following fusion techniques were frequently compared to these two techniques. According to a biomechanical comparison of the PLIF, TLIF, Extreme lumbar interbody fusion (XLIF), and OLIF in a single segment lumbar fusion model, the PLIF had less stability than the other three fusion procedures due to greater ROM and stress peaks in the posterior instrument (Lu and Lu, 2019). Furthermore, the OLIF and XLIF procedures resulted in fewer stress peaks in the cortical endplate and cancellous bone than the TLIF procedure, which was beneficial for subsidence resistance, disc height, and segmental angle maintenance. Another study used a lumbar-spine finite element model to compare the biomechanical differences between PLIF, TLIF, ALIF, and circumferential lumbar interbody fusion (CLIF/360) (Umale et al., 2021). The model revealed that ALIF is the most flexible and CLIF/360 is the stiffest, which corresponded to the findings of the in vitro experimental study. A numerical study compared two different minimally invasive techniques, LLIF and TLIF (Areias et al., 2020). However, FEA results revealed that neither is significantly better than the other in terms of spinal stability.