# Additive Manufacturing and Chemical Engineering: Looking for Synergies from a Bibliometric Study
## 4 Review of Research Focused on Additive Manufacturing and Chemical Engineering
### Additive Manufacturing and Chemical Engineering: Future Needs
#### 4.2.2 Optimization of Additive Manufacturing  
Additive manufacturing is an advanced processing method that enables the creation of customized components with complex geometries and architectural features, taking advantage of its non-linear relationship between complexity and cost [132]. Optimizing the geometry of a structure is crucial to minimizing weight, material consumption, and manufacturing costs, leading to both economic and environmental benefits. The necessity for lightweight structures arises in numerous engineering disciplines, where they serve to enhance the energy efficiency of moving systems and improve physical performance [133]. The current design strategies allow for a multitude of possibilities to identify the optimal solution or a set of optimal solutions for additive manufacturing structure design with functional properties suitable for each application. In this context, topology optimization and generative design are highly effective tools for the creation of optimal designs [134]. These tools diverge from traditional design approaches that employ computer-aided design systems to generate geometry. In contrast to these methods, these tools allow designers to prioritize functionality over form, while the generation of design alternatives is delegated to the optimization tools [135]. On the one hand, topology optimization is a computational design methodology that employs finite element analysis to achieve an optimal distribution of material within a specific design area while adhering to established constraints, loads, and boundary conditions [136]. This process involves discretizing the design area into finite elements and then employing various optimization techniques to determine how to distribute the material most efficiently. Typically, the goal is to minimize mass while avoiding excessive deformation or to maximize stiffness while keeping mass within a predetermined limit. On the other hand, generative design tools utilize artificial intelligence algorithms to generate a range of design options that meet the predetermined criteria based on user input such as materials, design space, functional surfaces, performance requirements, and manufacturing constraints [137; 138]. This way, a variety of different design solutions that the designer may not have considered can be generated, often achieving better performance compared to components designed using traditional methods in terms of mass reduction, improved strength, and reduced number of assembly parts. As depicted in Figure 14, both approaches share the first and last stages of the design process. The first stage involves using computer-aided design systems to define the functional surfaces of the three-dimensional model, while the final stage utilizes computer-aided manufacturing systems to ensure the proper fabrication of the model. In topology optimization, the model is imported into the optimization environment where conditions and objective functions are defined. Simulation is executed, resulting in an optimized geometry. The model is then modified by removing unnecessary material and validated using finite elements analysis tools in an iterative process. Furthermore, generative design starts with functional specifications, manufacturing processes, and desired objectives. The design space is explored, generating optimized and validated conceptual forms and the best alternative can be identified through comparative and trade-off studies [135]. These tools facilitate significant enhancement across a range of application domains, including the application to chemical engineering problems, such as transport through porous media [139], design of hydraulic components [140], enhanced heat transfer [141], or development of structured packings [142] and monolithic solids [143]. It is worth noting that there have been several notable successful cases of topology optimization and generative design in these contexts.