IFAB is an information architecture. “Rather than a single factory built around something, it is a distributed reprogrammable manufacturing capability,' says Wiedenman. IFAB will take the design representation in Meta, select manufacturing processes and equipment, sequence product flow and production steps, and generate computer numerical codes for machines and instructions for human workers.

IFAB will receive the design and flow it down the supply chain to get realistic cost-versus-schedule trades back to the designers. “The analog is Google Documents. It spell-checks as you go, but the spell-checker is not on your computer,” Yukish says. “On the manufacturing side, iFAB is constantly responding during design—push a button and you get a cost and schedule assessment.”

Here AVM bumps up against reality. “You cannot take an arbitrary design file and tell how much the part will cost. You only know when you have a process to make it,” he says. “So we parameterize and pre-negotiate between the designer and manufacturer. It's what they do in the chip industry, get together ahead of time and agree on materials, general topology, etc. By restricting the design space they can do incredibly fast design and manufacture without iterations,” Yukish says.

Restricting designers to parameters that allow automatic manufacturability assessments “determines the type of vehicle you can build,” says Yukish. “It's about living within our means, instead of pushing technology and getting cost growth.” This could limit the ability to optimize the design, but Darpa plans to tap the creativity of a wider pool of designers using a web portal to enable collaborative development.

“We've learned from the semiconductor and software industries that when you do this, you open the aperture for innovation by increasing the number, diversity and speed of those who can contribute,” Wiedenman says. In aerospace, limitations imposed by current tools ensure “the only entities able to contribute to the design are corporations with the wherewithal to build multimillon-dollar prototypes. That limits us to a handful of companies and maybe a few hundred brains.”

“There are a lot of people out there with those skills who don't work for that handful of large companies. Creating tools that allow an engineer to understand how a system works before building it opens the aperture to a much broader collection of brains that we can bring to bear in designing complex systems,” Wiedenman says.

“If we only use architectures that can be automatically assessed for manufacturability, the crowd can help us overcome the limits of living within that constraint through the innovative assembly of easy-to-assess components,” says Yukish. “Manufacturing constraints force us to be simpler and the crowd means we have enough people with expertise staring at the design.”

“What we are trying to achieve is the ability to reach an optimal design much faster, before there is opportunity for requirements to creep and the threat environment to modify,” says Wiedenman. “It's not about high performance, it's how quickly it can come out,” says Yukish. “What's Darpa-hard is exercising discipline, staying within what we can do. The program's absolute insistence on schedule is unique.”