## 1 Introduction
The research executed a convergence test to set the best mesh size that could generate the best quality of calculations, yet this increased the time and cost required for calculation. [PERSON] et al. [21] carried out modeling and FE simulations to obtain an overview of unit cells while reducing the time and experimental tests needed to validate the same outcome. Other researchers tried using COMSOL Multiphysics simulation programs for finite element analysis to validate numerical models [22]. Recent studies have shown that the more accurate FEM results are, the greater the computational effort will be. As a result, surrogate model use is increasing as an approach to overcome this problem. [PERSON] et al. [23] compared mesh-free surrogate models with the help of machine learning (ML) and deep learning (DL) to see how to find a suitable surrogate model for a specific case. [PERSON] et al. [24] showed that FE simulations are applicable in all cases of lattices, yet they might be time-consuming when working with complicated geometry. Consequently, the study presented a surrogate model to address such problems. Many researchers followed suit in building surrogate models to either help with lattice structure design [25], predict mechanical properties using artificial neural networks (ANNs) [26], or predict the performance of lattice structures with transfer learning TL [27]. However, developing a computational model using machine learning or deep learning techniques brings extra time and costs due to all the data gathering, processing, and model development. Thus, the question arises as to what a helpful solution might be when the experimented data are not sufficient to build a surrogate model, yet an easier-to-handle substitute model for lattice structures is still needed. In response to this, [PERSON] et al. [28] simplified the FE simulation by allowing the lattice structure to be modeled as a solid body but using the lattice-structured body's equivalent elastic properties, rather than modeling it with its actual complex geometry. [PERSON] et al. used simplified FE analysis to investigate the mechanical properties of lattice structures under compressive loading and showed the effect of lattice structure orientation on the stiffness-weight ratio [29]. [PERSON] et al. [30] used finite element models that only worked with a simple representation of the structural geometry of bone and the inserted prosthesis. The research stated that optimizing lattice structures generated reliable results. Researchers have tried various techniques and developed many formulas to facilitate the prediction of the mechanical properties of lattice structures using FEM but with more simple-to-model geometry under many loading conditions [31; 32; 33; 34]. According to the medical guidance and professional literature, correctly chosen lattice-shaped implants promote bone ingrowth. Based on our previous research [35; 36; 37], the 3D lattice infill type built into ANSYS has proven to be a suitable and usable lattice structure for hip implants and can also be printed with sufficient accuracy using additive manufacturing from Ti6 Al4V material. DMLS technology was used to produce specimens according to the ISO 13314:2011 standard [38]. The effective Young's modulus of the structure built from 3D lattice infill was determined at the level of the test specimens. Using these results, a numerical model surrogating the lattice structure was developed. The time-consuming finite element calculations could therefore be replaced with this surrogate model. In addition to reducing computational time by a factor of 100, the accuracy of the numerical model also satisfied the engineering practice. The novel method can significantly optimize the design process of patient-tailored hip implants that aim to approximate the stiffness of bone.