DfAI - Design for Artificial Intelligence
CDFAM Speaker Chris McComb – Carnegie Mellon University
As we embark on the journey of incorporating artificial intelligence into design and engineering, it is crucial to develop a strategic approach towards Design for Artificial Intelligence (DfAI).
While numerous scholars focus on deciphering the complexities of integrating AI into engineering, addressing the myriad data-related challenges, Chris McComb and his team at Carnegie Mellon University delve into not only the mechanical aspects and individual approaches to DfAI, but also the dynamics of group-based methodologies. Their research encompasses the subtleties of engineers' interaction with AI-powered engineering software and, of equal importance, their interpersonal communication in such contexts.
At the forthcoming CDFAM Computational Design (+DfAM) Symposium, Chris will discuss his research in depth. Consequently, we inquired about his work, the hurdles he identifies in adopting DfAI, and his insights on seamlessly assimilating AI into design and manufacturing organizations to optimize value generation.
Following are excerpts from our conversation with Chris, the full interview can be found via CDFAM Design for Artificial Intelligence: A Force Multiplier for Additive Manufacturing
Given the quality of any AI generated material is based on the quality of the data training the machine learning algorithms, how can we best seed the content with metadata to allow AI to create and compare for multiple requirements from the same data.
Metadata is the lifeblood of AI engineering, and this is one of the things that differentiates our niche from our colleagues in computer science – not only do we crave the metadata, but it is absolutely essential for what we need to do!
For example, a simple voxelized file format or an STL retains geometry information about a part. However, for many useful engineering AI applications we need much more than just geometry – we need surface finish information, material specifications, etc.
There’s a spectrum of metadata that we might be interested in for engineering applications. At one end of the spectrum is direct declarative information, like specifying the surface finish on a face of the part. On the other end of the spectrum is metadata that results from a time-consuming process, such as data related to test builds or high-accuracy simulations. But the spectrum also varies in generalizability – the declarative information is likely much more consistent and standardizable than the more specific metadata. Let’s target that low-hanging fruit!
Many companies in the manufacturing space consider their design, simulation and manufacturing data to be their ‘secret sauce’ that they are not willing to share, sometimes even internally, how can build a warehouse of meaningful data both inside, and outside of these walled gardens?
This perfectly highlights the “AI Hierarchy of Needs” by Monica Rogati.
We all want to get to the top of the pyramid – deploying powerful AI models that revolutionize our businesses. However, many of us are still at the base of that pyramid, figuring out how to effectively log and clean data. I don’t have answers here, but there are some areas that might yield solutions.
The first is dataset distillation. The idea is this: is it possible to create a very small, synthetic dataset that can replicate the training results of a larger, ground truth dataset?
While this gets around the issues of directly sharing IP-protected data, it could still be possible to extract the “secret sauce” from the synthetic distilled dataset.
The second opportunity is federated learning. In this approach, multiple models are trained on multiple, distinct datasets and then combined to produce a single model.
There might be less technical solutions as well – industry consortia that align incentive structures to encourage data sharing, federal initiatives to generate open data, and others.
I am really excited that you will be presenting some of your research at CDFAM 23. What will you be talking about and what else are you looking forward to learning about at the event?
I’m excited too! I’ll be speaking about the DfAI framework. Specifically, I’ll be sharing tips for how to integrate AI seamlessly into design and manufacturing organizations to maximize value.
The full interview can be found via CDFAM Design for Artificial Intelligence: A Force Multiplier for Additive Manufacturing
Register to attend CDFAM to learn from, and network with experts in design, engineering, software manufacturing and AI.
CDfAM is a reader-supported publication. To receive new posts and support independent research and writing, consider becoming a free or paid subscriber.