During the industrial revolutions, production moved from craft manufacturing to a more current mass customization. This was obtained through intermediate stages where the production was conditioned by continually growing and more revolutionary models, more adaptable to new needs, and has been subject to the choice of preferring a pull strategy rather than a push one [1]. The adoption of the pull strategy foresees a series of changes in the way a company approaches the market: shorter development times, identification of demand in quantitative and qualitative terms, flexibility, decentralization of production and resource efficiency [2]. With the advent of the Fourth Industrial Revolution and the digitalisation of production (Industry 4.0), the manufacturing sector has to face further new challenges, which require the development of new skills and the use of innovative tools. This scenario includes smart factories, and particularly the use of innovative production systems such as Additive Manufacturing (AM).
In agreement with various authors, AM is a technology capable of producing a component layer-by-layer directly from its three-dimensional model without the need for specific tools, and in a single production step [3, 4]. The characteristic layer wise production of AM claims a number of advantages, such as the ability to produce complex and highly lightened shapes, thus minimizing the buy-to-fly ratio [5]. The aerospace sector can be considered as the one which makes the most of AM techniques potential. The primary objective of the aerospace industry is to reduce the overall mass of the aircraft, while improving the part performance, even at the risk of higher production costs [6]. Reducing the overall mass of the aircraft can make it fly farther, faster and consume less fuel. In addition, the aerospace industry can take advantage of AM to reduce environmental impact, cutting down greenhouse gas emissions [7]. In the attempt to make this possible, the various parts of the aircraft should be lighter, but care should be taken not to affect other structural requirements such as stiffness, fatigue strength and resistance to high temperatures [8]. For these reasons, the aerospace industry has always been very attentive to the search for new materials, such as superalloys or composite materials, and to the use of innovative production techniques, such as AM [4]. In this sector, each structural component adopted on the aircraft must pass the two most critical phases: qualification and product certification. Thanks to the constant increase in the reliability of AM techniques, it is now possible to standardize the qualification and certification process of the components produced by AM following current regulations. GE estimates that AM will be exploited to build future ships that can safely transport humans to the Moon and Mars, and in has already started the fuel nozzle tip mass production parts for jet engines [9, 10].
With the aim of taking advantage of the adoption of AM technologies, the design of the components plays a key role. Nowadays AM metal techniques produce high-performance final components with complex shapes, freeing the design from the process limits of standard techniques (e.g., milling, turning, foundry, bending, etc.). Since the cost of a component made for AM does not depend on its complexity but rather on the amount of material used and its size, it can be said that the products made for AM are complexity for free [11]. As a result, geometric complexity is no longer a constraint for component design, and design optimization tools can be exploited efficiently. Topological Optimization (TO) and Generative Design (GD) are two effective tools available to the designer for component optimization. As a matter of fact, the very complex shapes generated by TO and GD, often not producible by conventional processing techniques, now have a production potential [12]. These tools can both be used to define an initial concept from which to develop an increasingly detailed design in the later stages of the design. Also, TO plays a significant role in optimizing existing components that are obsolete in terms of performance and can therefore be optimized by adopting a material or a production process other than the original one. TO is used in a wide range of fields, from the automotive, aerospace and medical sectors to those of design and architecture. On the other hand, GD has found its main place in the design of buildings, and therefore in the field of architecture and design [13]. In more recent years, the GD has also been used in the engineering field, particularly the structural one, thanks to the development and implementation of commercial software [14, 15].
As concerns GD, it is a conceptual project support to the designer based on a parametric computer-aided design (CAD), capable of generating multiple output proposals, which can be achieved by more than one production techniques [16]. The core of GD is to apply an evolutionary algorithm to populate alternative solutions in the design space [17]. Such algorithms optimise solutions taking into account overall structural characteristics, material properties and process constraints. In addition, GD algorithms may use both Evolutionary Design (EV) and TO but are not limited to the use of only one of these [18]. Although there is no precise and consistent definition of what GD is, it can be said that "generative design systems are aimed at creating new design processes that produce spatially novel yet efficient and buildable designs through the exploitation of current computing and manufacturing capabilities" [19], so it can help engineers think like designers and vice versa as assessed by Kazi et al. [20]. However, it should be noted that in some cases, the distinction between the two approaches is unclear as TO algorithms are also used in GD. On the other hand, GD offers a range of outputs that meet the designer's requirements, thus allowing him to use multiple approaches to solve a problem. Typically, the choice of the right solution in the design process is based on the personal judgment of the designer.
GD allows taking into account some of the aspects of production already in the conceptual phase, positively impacting the design process, since potential issues are anticipated, and risk is reduced. This design approach for product development allows designers to evaluate performance in advance, partly avoiding the costly testing phase [21]. The methodology embraces the concurrent engineering approach, where the development of a project is integrated into its manufacturing, and the duration of the product development cycle could be reduced up to 70% [22].
This work adopts a simulation-driven method based on GD software, aiming to define a methodology to quickly select the most promising solution from a portfolio of solutions, cutting down the time spent in the design phase. The proposed approach is intended for conceptual design and allows a variety of geometries to be analysed and ranked, and the best one to be quickly identified from the perspective of product functionality, costs and production aspects. It opens the way to the next steps of preliminary and final designs. More in details, the methodology is applied to AM as manufacturing technology and aerospace as application sector.