The manufacturing world is undergoing a profound transformation based on new design technologies that couple 3D representations of highly complex structures with artificial intelligence, model-based reasoning, and data-driven learning. Design representations of the future will be hybrid, fusing complex geometric information with physics and machine-learning models. At the same time, we are seeing the introduction of intricate new materials into a broad range of manufacturing processes. In many ways, design technologies have not been able to keep up with this rapid pace of change. As a result, manufacturing capabilities are driving the evolution of design technologies, not the other way. For example, hybrid manufacturing technologies that allow seamless interleaving of additive and subtractive processes are already available, but there are very few designs that are truly enabled by these technologies.
Product designers today are forced to make key tradeoffs in the preliminary stages of the design process. Standard computer aided design and computer aided manufacturing software (CAD/CAM) and product lifecycle management systems are still useful for describing design geometries and materials. But the use of aging legacy tools and materials causes manufacturers to think very narrowly about their designs, limiting their ability to innovate. In other words, legacy designers and manufacturers are trapped by their current software tools that cannot scale up to meet the escalating levels of hardware and process complexity.
This is why PARC researchers are working to enable product designers to create novel designs that exploit the geometric and material complexity enabled by additive and hybrid manufacturing. The goal is to harness the tidal wave of new materials and fabrication methods out there to enable designs that are unimaginable today.
Manufacturers today face a clear need to move beyond current siloed design tools. The industry’s increasing levels of complexity will require smarter systems that can guide manufacturing decisions much earlier in the design process. What’s needed is an integrated view of all possible manufacturing options, materials and parts at the start of the design process. Artificial intelligence and material physics are quickly converging to give us that clearer picture by incorporating necessary processes and parts to drive real manufacturing innovations – at the earliest possible stages of a product’s design.