AI Optimization of Processes in AM Environments
Posted on January 12th, 2023
There are several barriers to the widespread adoption of desktop 3D printers. One of the main barriers is the high cost of the technology. 3D printers can be expensive, especially high-quality ones, and this can make them out of reach for many people. Additionally, 3D printing technology is still less reliable than tried-and-true manufacturing methods and materials. This can make it hard for people to get started with 3D printing, even if they are interested in the technology. Finally, there are also concerns about the safety and reliability of 3D printing technology.
AI can be used in additive manufacturing in a number of ways. For example, it can be used to optimize the design of a part to be produced using additive manufacturing, to predict the mechanical properties of a part based on its design, or to control the manufacturing process itself to ensure that the final product meets certain specifications. Additionally, AI can be used to monitor the manufacturing process and identify potential issues before they become problems, as well as to automate certain aspects of the process, such as setting up the equipment or loading and unloading parts.
3D modeling can be challenging. One of the main challenges is that it requires a high level of skill and proficiency with specialized software. Creating realistic and detailed 3D models can also be time-consuming and requires a great deal of patience and attention to detail. Additionally, 3D modeling can be challenging because it involves a lot of complex processes, including sculpting, texturing, lighting, and rendering, which can be difficult to master. Finally, because 3D modeling involves creating virtual objects, it can be difficult to visualize and manipulate the models in a way that accurately reflects the real world.
NVIDIA is working to solve this barrier with a new AI tool called Magic3D:
This type of text based AI modeling method removes some of the challenges in creating the 3D digital designs needed to run 3D printers. ChatGPT could even be used to reduce the skills needed in slicing the 3D models (2nd step in AM workflow). Specific knowledge of machines, firmware, and differing slicing utilities could be a thing of the past once AI G-code generation is perfected. Additionally, it is possible to ask ChatGPT to generate OpenSCAD models based on text input. Currently the results are sub-optimal and need human refinement but still impressive considering that ChatGPT was not trained on G-code or OpenSCAD.
Overall, AI has the potential to improve the efficiency and accuracy of additive manufacturing processes, resulting in better-quality parts and more cost-effective production. This is under the assumption that the AI optimization models have not been subverted to produce less robust designs. As an example of this is a topology optimization that removes too much material to induce a failure. BISON can help secure AI optimization by preventing unauthorized changes, ensuring the integrity of the design.
This blog post was written by ChatGPT (~60% of total word count)
If you are interested in learning more about securing Additive Manufacturing or a demonstration of BreakPoint Lab’s BISON AM solution capability, please contact us at email@example.com