## 1 Introduction
### Machine Learning Applications in Mechanics  
Artificial intelligence (AI), specifically ML, has become an important area of study in the modern era. ML has a huge variety of applications that cover various fields of knowledge, and mechanical engineering is not an exception; applications of ML allow researchers to predict and/or classify specific mechanical properties. ML can also aid the manufacturing process by assisting in the selection of parameters for specific processes [30], allowing the automation of the design of some actuators [31]. The application of hybrid ML to select design parameters in modern mechanical applications, such as in mechanical metamaterials, has been reported [31; 32; 33; 34; 35]. Other applications of ML in the structural sector are found in the works of [PERSON] et al. [36; 37], where the weighted averaging of non-fine-tuned ML models was applied to the concrete construction sector, with special emphasis on corroded beams.  
ML has been tested in the field of mechanical metamaterials to forecast specific mechanical properties and reduce iterations in the design processes. In this form, an ML algorithm can be used to predict errors in compliant auxetic designs based on a mathematically optimal deformation [32]. Another manner of using ML in the field of mechanical metamaterials is to aid in the design process to obtain smart structures with programmable mechanical responses [34]. In the work of [PERSON] et al., this approach was presented, and it included the generation of a new metamaterial that can be designed and adapted to different target properties by the implementation of Bayesian ML [35]. In addition, ML has been used with trained datasets to automate specific design processes concerning mechanical metamaterials. That was the case for [PERSON] et al., who developed a method to design hierarchical materials using machine learning using a trained dataset with thousands of structures from finite element simulations [33].  
With the aim of enhancing the displacement range in additively manufactured CJs and aiding their design process via ML, in this work, we study compliant cross-axis joints (CCAJs) where flexures have been replaced with lattice flexures. The stiffness of these FHs is modified using mechanical metamaterials as honeycomb lattices (hexagonal, re-entrant, and square). The resulting lattice-flexure hinges (LFHs) with different lattice topologies are studied both computationally and experimentally on additively manufactured samples. Finally, the use of ML is explored to aid in the selection of design and topology parameters to achieve specific elastic performance.  
The remainder of the work is structured as follows: Section 2 includes all the details of the computational design process, parameter definitions, finite element modeling, additive manufacturing, and ML; Section 3 reports on the simulations and experimental results, along with a comparison with ML predictions; Section 4 presents discussions of how the stiffness generated in each LFH affected the displacement of the CCAJ, as well as the importance of identifying the deformation mechanisms in specific LFHs and understanding how they affect the total range of motion of CCAJs; finally, in Section 5, some important concluding remarks and an overview of future work are presented.