Generative design (generative design) is a computer and AI-driven design process that automatically creates products based on user-defined requirements and constraints. Or, in short, the software designs a part or product based on product attributes and design goals suggested by the user. 3D printing has enough advantages to manufacture generatively designed parts.
The more variables that are entered into generative design software, such as intended use, manufacturing method, available materials, etc., the better the computer design will produce. Products designed with software generally have a curvilinear appearance and a structure that resembles the shape of a tree branch or a skeleton. Generative design has been likened to the evolutionary trial-and-error process that nature goes through to arrive at optimal structures, and at a much faster rate. Using generative design has many advantages, such as saving materials while ensuring high strength.
For example, the bracket below, the bracket on the right is the original design. The brace on the left is the part optimized to reduce material usage while maintaining the same strength and conforming to the same dimensions. The final scaffold can be injection molded, machined or 3D printed. AI-generated designs are often so complex that they can only be manufactured through 3D printing methods.
Shape optimization algorithms, or topology optimization, have long tackled one of the most fundamental engineering problems: how to make sufficiently strong parts with as little material as possible. Today's AI generative design goes a step further, offering a plethora of options. Importantly, engineers are no longer limited by their skill level, experience, or creativity. At the end of a generative design analysis, engineers have a concept that can be further explored and refined or a ready-to-manufacture design solution that fully meets all project requirements for weight, loads, materials, etc.
Generative design is a creative tool, and like many other computer-assisted processes, it needs a human to drive it. Therefore, success is related to the quality of the data entered by the user. Here we can borrow the concept of GIGO from computer science ("garbage in, garbage out"), wrong input data will produce wrong results. While generative design tools allow almost anyone to master complex designs, don't underestimate the skill required to correctly define the initial conditions for the desired part. In general, the information needed for generative design software to solve a problem includes:
●Achievability of the manufacturing process, this is the advantage of 3D printing manufacturing. 3D printing itself has extremely high flexibility, which is very suitable for the part manufacturing of generative design.
● Reduce analysis. Built-in test and calculation simulations reduce any further costly virtual computer-aided engineering (CAE) analysis.
●Reduce workload. It frees professionals from tedious trial-and-error tasks and increases productivity by providing hundreds of possible design solutions.
● Reduce costs. Save money by delivering high-performance designs with less material usage, and by reducing development time and time-to-market.
As the Internet of Things and artificial intelligence become more prevalent in our daily lives, generative design may become the norm in product design. This technology provides designs that will not only have a positive impact on the industry but also on the environment Because fewer resources are needed to produce everyday products. At present, the development of generative software is also very fast, including Fusion 360, Creo Generative Design Extension, Ansys Discovery, nTopology, Dassault Systèmes CATIA Generative Design Engineering, etc. You can use these this software in combination with the 3D printing process to create your own generative design parts.