## 6 Next-Generation Structural Optimisation and Design Guide for Lattice Structures  
The next generation of structural optimisation and design principles for lattice structures is expected to incorporate a number of breakthroughs and trends in response to changing technology, materials, and engineering requirements. Some prospective paths for the development of lattice structures are discussed in the following paragraphs. Through the advancement of metamaterials and engineered composites, lattice structures are expected to be designed with materials that have specific properties, such as variable stiffness or properties that can dynamically change based on external influences, such as stress or temperature [68; 69; 70]. Using auxetic metamaterials in lattice structures provides varying stiffnesses. Auxetic materials have a negative Poisson's ratio and expand in numerous directions when stretched. Engineers incorporate these materials to build lattice structures that dynamically vary their stiffness depending on the applied load. As an illustration, in the field of sports equipment, a tennis racket could potentially be designed with lattice structures that use auxetic materials. During a robust swing, the lattice structure customises to the amount of load applied, optimising the racket's stiffness for the maximum energy transfer. Additionally, new lattice designs could incorporate bio-inspired materials with exceptional strength-to-weight ratios, mimicking the efficiency seen in structures such as bone or spider silk [17]. Lattice parts could be manufactured using synthetic materials that mimic specific properties of spider silk, such as tensile strength and elasticity. Spider silk is recognised for its remarkable strength and elasticity. Mimicking the molecular structure of the proteins of spider silk in synthetic materials could yield lattices that are both strong and resilient. These structures potentially have uses in a variety of fields, including aerospace engineering as well as construction. Machine learning and artificial intelligence (AI) are coming into use to automate and optimise the design process, allowing for the rapid production and testing of multiple design alterations. The AI and machine learning techniques are expected to be implemented more often to optimise lattice structures by analysing huge amounts of data and iteratively improving designs, resulting in more efficient designs [71; 72]. Artificial intelligence (AI) algorithms can respond to real-time feedback during the manufacturing and testing processes, by incorporating this knowledge into subsequent design iterations. As lattice structures are built and tested, AI systems are able to gather information about the designs' actual performance and behaviour. The algorithms use this feedback to iteratively improve future designs, learning via both successes and failures. This adaptive learning strategy speeds up the optimisation of lattice designs by using real-world performance data. Furthermore, improving predictive capabilities by using digital twin technologies to simulate and analyse the functionality of lattice structures in real-world engineering conditions is anticipated as well [73]. Using contemporary 3D modelling and simulation software to generate digital replicas of actual lattice parts, digital twin technologies allow for the generation of computer-generated alternatives that mirror actual lattice designs, capturing their geometry, material properties, and behaviour under various loading conditions. Quantum computing is expected to come into play for use in highly complicated simulations and optimisations, thus allowing for the design of traditionally unattainable lattice structures [74]. Engineers must also interact with modern design software and AI to create novel and efficient lattice structure designs [72].