## 2 Design Methods Based on TO and GD Tools
### Generative Design  
GD tools use artificial-intelligence-based algorithms [53; 54; 55] to generate an array of design options that meet predetermined criteria, based upon initial user preferences, such as materials, design space and functional surfaces, performance requirements, and manufacturing constraints [56]. In particular, they can generate a number of different design solutions, that the designer would never have thought of, and with better performances compared to a component designed with traditional methods in terms of mass reduction, strength improvement, and reduction in the number of assembly parts.  
Research on Generative Design started in the early 70s when the first design algorithms that mimicked nature were born [57]. Then, with the advent of parametric CAD tools, GD design was studied with a more practical approach, but only in the first decade of the century, when the first attempts of using GD in academic research for applications in the architectural fields were made [58; 59; 60]. Recently, thanks to the significant increase in computing power available, generative methods have begun to be adopted to the design of products [61; 62; 63]. This is due to the fact that, in recent years, generative software hasbeen put into the market as plug-in modules for CAD systems that support designers to create and generate more efficient designs that are lighter, stronger, and in some cases, more artistic. In fact, nowadays, more and more industrial manufacturing companies are starting to experiment with the adoption of such tools to redesign products or generate new concepts that are reworked by designers [22; 64]. To this end, a prime example is [PERSON]'s A.I. chair for Kartell, revealed during the Salone del Mobile in Milan in 2019, in which the GD technology was adopted to serve as a co-creator by providing design alternatives generated according to parameters such as materials, production methods, and cost constraints, supplied by the designer.  
As depicted in Figure 1, the design process starts by using a CAD system in order to create a starting shape and obstacle geometry. Then, the user provides the functional specification including the material, functional surfaces related to mechanical interferences, loads and constraints, and the desired manufacturing processes, such as molding, machining, and Additive Manufacturing. The user also defines the desired target, such as a weight reduction or maximizing stiffness. From there, the tool cycles through the prescribed design space to generate a plethora of optimized and validated concept shapes. This can be completed using genetic algorithms and neural networks. These algorithms are based on machine learning techniques for training artificial intelligence, due to which the neural network simultaneously serves as an assistant both in the construction of a mathematical model of the optimized product and in the process of its interpretation, taking into account, for example, manufacturing parameters. An advantage in this phase is that simulations are made on a cloud-based system so, despite the performances of the designer's computer, demanding GD studies can be undertaken. After that, the results obtained are shown to the designer. Here, the GD tool supports designers in identifying the best concept for their study by allowing them to perform comparison and trade-off studies on the basis of the desired key performance indicators. The designer can then select the most appropriate solution and validate the geometry through a CAM tool before proceeding with the manufacturing. The result of the optimization process can be directly prototyped by means of AM technologies. Nevertheless, the geometry can be modified by means of built-in modeling tools that allow to manipulate control points or define cross-section profiles and edit the geometry to generate more relevant solutions [65; 66].