Generative design is widely considered to be one of the most significant innovations in modern computer-aided design (CAD). It represents a paradigm shift in design, as designers no longer need to model each individual feature; instead, they define the boundary conditions and objectives, and the software uses algorithms to create countless potential designs. This changes the role of the designer, transforming them from modelers into strategists who weigh up options and analyse technical relationships.

Technological advances in cloud computing, simulation and artificial intelligence have brought generative design from the realm of research into industrial practice. It is no longer just a future topic, but is already being used in various fields, including mechanical and automotive engineering, architecture, and product design.

How generative design works
The central element is an algorithm that develops suitable structures autonomously, based on predefined criteria such as loads, materials, manufacturing techniques and costs. Rather than specifying the geometry, the designer specifies the intended outcome. Typical input data includes maximum load, desired stiffness, material properties (e.g. aluminium, titanium or plastic) and manufacturing technology (e.g. 3D printing, milling or casting), as well as any assembly or installation space restrictions.

The software can use this data to generate hundreds or even thousands of proposals. These proposals are then sorted according to criteria such as weight, strength or energy consumption. The designer then evaluates the proposed variants and selects the one that best suits the specific project.

Practical advantages
The benefits of generative design lie in its efficiency and creativity. The main advantages include:

  • Development times are reduced. Variants can be created in hours rather than weeks.
  • Algorithms can find structures that optimise the use of resources. This is an important aspect of lightweight construction.
  • Solutions are created that humans would not intuitively design as they evolve in ways that are completely unexpected.
  • Early assessment of energy consumption and production costs is an important part of integrating manufacturing and sustainability.

In mechanical engineering and vehicle construction in particular, this is leading to new methods of producing lightweight, stable and cost-effective components. With the help of appropriate CAD software, these methods are becoming increasingly accessible to small and medium-sized enterprises.

Changing role of the design engineer
Generative design shifts the focus of the work. The design engineer becomes the “director” of an algorithmic process, leading it to a more streamlined and efficient outcome. Their responsibilities include:

  • precisely formulate target values,
  • understand parameters and set realistic limits,
  • Critically interpret results.

These tasks increase the demands placed on analytical thinking and technical understanding. It is important that designers learn to deal with uncertainties and diverse data. The challenge lies in correctly interpreting AI results and linking them to empirical knowledge.

Limitations and challenges
Although generative design offers many advantages, success is not guaranteed. Current challenges that need to be addressed are as follows:

  • If the inputs are not precise, the data quality is compromised. This can result in unusable outcomes.
  • Algorithms provide suggestions, but not decisions. This is where skilful interpretation comes into play.
  • Acceptance within the company is an important factor. Changes to working methods and responsibilities are necessary.
  • Not all generated shapes can be produced economically, which affects their manufacturability.

It is precisely at this point that it becomes clear that humans continue to play a central role: they evaluate, correct and decide which solutions really make sense.

Prospects for the future
The trend towards hybrid systems, which combine simulation, machine learning and real-time data, continues. This opens up new possibilities in various areas.

In the future, for example, sensors in prototypes could provide data that is directly incorporated into new calculations. Sustainability metrics, such as carbon footprint, could be automatically considered, and AI systems could suggest design decisions based on previous projects. This integration will establish generative design as a vital tool in digital product development and Industry 4.0.

Generative design is not just a new CAD tool; it is changing the way designers think. Instead of designing entirely from scratch, designers are increasingly collaborating with algorithms to find the most effective solutions. Combining human experience with artificial intelligence paves the way for innovation, sustainability and competitiveness.

Those who adopt this approach early on will benefit doubly: from faster development processes and a deeper understanding of the relationships between function, material and manufacturing. Therefore, generative design is not just a technical tool, but a guide to the future of engineering design process.