Ersin Uzun and Sai Nelaturi explore how artificial intelligence and smart manufacturing are driving advances in product design

Manufacturing 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.

As design technologies struggle to keep up with this rapid pace of change, manufacturing capabilities are driving the evolution of design innovation. For example, hybrid manufacturing that allows seamless interleaving of additive and subtractive processes are already available, but there are very few designs that truly harness these technologies.

Standard computer-aided design and computer-aided manufacturing (CAD/CAM) software and product lifecycle management (PLM) 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. The result is that 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.

Computer-aided design tools and processes have reached their fundamental limits, requiring next-generation design programmes that can propose breakthrough concepts, shapes and structures, which are impossible to imagine through the use of our current tools, much less by a human individual acting alone.

Work at PARC is focused on empowering product designers to create designs that exploit the geometric and material complexities enabled by additive and hybrid manufacturing.

This new approach aims to streamline the production process from initial mock-ups to final parts production. The goal is to harness the wave of new materials, artificial intelligence technologies and fabrication methods to enable designs that are unimaginable today. This approach has the ability to cater to objects with billions of geometric attributes such as jet engines or gas turbines and can automatically optimise shape and material layout along with some design parameters for an object and determine the best settings for fabrication.

It is humanly impossible to think through the combinatorics of such material and structural complexity. Therefore, design software must include artificial intelligence (AI) as a problem-solving team mate. The coupling of 3D representations of highly complex structures with AI, model-based reasoning, and data-driven decision-making is a fundamental innovation required to realise the next generation of design software systems.

We envision that the future of manufacturing will be AI-enabled with hybrid design representations, hybrid processes and hybrid materials. Hybrid manufacturing approaches will combine subtractive manufacturing and additive manufacturing techniques by incorporating the widespread use of 3D printing and design tools.

Additive and subtractive manufacturing each offer certain advantages and disadvantages that can be exploited by a hybrid manufacturing process planner. For example, a hybrid process can first additively manufacture complex product features. But while printing the design, it may require some support structures to facilitate the process. Later, the AI engine can automatically direct the system to subtract those support structures without introducing too much complexity. In other words, an AI planner can ask an additive process to deliberately add excess material knowing that a future subtractive process will remove this material. This addition and deletion of excess material may be the key step to making the design manufacturable. More complex parts can be fabricated by interleaving additive and subtractive processes intelligently; such parts may be impossible to manufacture solely with additive and subtractive processes. We expect a similar adoption of hybrid materials. For instance, to create a composite layer of materials with current systems, each layer must be designed separately and then stitched together. This complexity creates a restrictive pain point requiring extensive design planning that drives up costs. By contrast, 3D printing systems today are moving towards being able to fabricate smoothly, gradient material properties from hard to soft, which standard PLM software simply cannot represent.

PARC’s digital graded material fabrication technology is revolutionary because it enables what is known as voxel-level control over material composition, which enables the production and optimisation of digital gradients in complex objects. It is pre-programmed to work with a range of materials and composites, with specific tools integrated for additive and hybrid manufacturing.

Manufacturers are beginning to leverage the computational power accessible today via cloud computing and ever faster CPUs in advanced design, modelling, and production. They can also take helpful cues from the animation industry, which deploys extreme processing power to render highly complex animated scenes. In a similar manner, material scientists can now apply animated computer graphics to render a high-resolution CAT scan of a patient’s femur bone, for example. 3D printers can then replicate the resolution of that specific bone structure to manufacture it accurately.

We can also inspire fresh thinking by adopting AI planning tools and model-based reasoning systems. No legacy computer-aided design system today can automatically determine how to set up a tools platform and connect that geometry to model-based AI and planning. But future manufacturing systems will take in diverse 3D geometries to suggest extensive options for the creation of cost-effective designs with existing tools. In this way, we can fully grasp the material properties of original product designs and thus understand all the physics that will be required for manufacturing.

To succeed, we will need to enhance the role of the human engineer by having our tools represent, plan, and manage complex, graded geometries and multiple-length scales for materials, while asking the engineer to include domain-specific expertise and experience to curate designs efficiently. Doing this effectively will require incorporating material and manufacturing uncertainty into the physics analysis of all functional parts.

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.

Ersin Uzun and Sai Nelaturi
Ersin Uzun is Vice President and Director of the System Sciences Laboratory (SSL), and Sai Nelaturi manages the Computation for Automation in Systems Engineering area in the System Sciences Lab at PARC. PARC, a Xerox company, is a renowned Open Innovation company that has pioneered many technology platforms – from the Ethernet and laser printing to the GUI and ubiquitous computing. Parc provides custom R&D services, technology, expertise, best practices, and intellectual property, creating new business options, accelerating time to market, augmenting internal capabilities, and reducing risk for clients.