Section: Overall Objectives

Overall Objectives

Digital fabrication has had a profound impact on most industries. It allows complex products to be modeled in Computer Assisted Design (CAD) software, and then sent to Computer Aided Manufacturing (CAM) devices that physically produce the products. Typical CAM devices are computer controlled lathes and milling machines that are ubiquitous in mass-production chains, along with injection molding and assembly robots. The design of a new product requires a large pool of expertise consisting of highly skilled engineers and technicians at all stages: design, CAD modeling, fabrication and assembly chains.

Within CAM technologies, the advent of additive manufacturing (AM) (i.e., 3D printing) together with powerful and inexpensive computational resources let us envision a different scenario. In particular, these technologies excel where traditional approaches find their limitations:

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  Parts with complex geometry can be fabricated in a single production run and the cost has no direct relation with the geometric complexity.

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  The cost-per-unit for fabricating an object is constant and significantly lower than that of producing a small series of objects with traditional means. Though it is not competitive on a mass production scale where cost-per-unit decreases as the number of produced units increases.

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  The machine setup is largely independent from the object being fabricated, and thus these technologies can be made available through generic 3D printing companies and online print services. Additionally, the machines are significantly easier to operate than traditional fabrication means to the extent of making them accessible to the general public.

 

As a consequence, it becomes possible to design and produce parts with short development cycles: physical objects are uniquely and efficiently fabricated from digital models. Each object can be personalized for a specific use or customer. The core difficulty in this context lies in modeling parts, and this remains a major obstacle as functional and manufacturability constraints have to be enforced. By functional constraint we refer here to some desired behavior in terms of rigidity, weight, balance, porosity, and other physical properties. This is especially important as AM allows the fabrication of extremely complex shapes, the scales of which vary from a few microns to a few meters. All this moves AM well beyond traditional means of production and enables the concept of metamaterials; materials where parameterized microstructures change the behavior of a base shape fabricated from a single material.

Exploiting this capability turns the modeling difficulties into acute challenges. With such a quantity of details modeling becomes intractable and specifying the geometry with standard tools becomes a daunting task, even for experts. In addition, these details have to combine in subtle and specific ways to achieve the final functionality (e.g., flexibility, porosity) while enforcing fabrication constraints. On the process planning side (i.e, the set of computations turning the part into printing instructions), large parts filled with microstructures, porosities and intricate multi-scale details quickly lead to huge data-sets and numerical issues.

Our overall objective is to allow experts and practitioners alike to fully exploit the advantages of AM. We aim to achieve this by developing novel algorithms that automatically synthesize or complete designs with functional details. We consider the full chain, from modeling to the geometry processing onto the optimization of 3D printer instructions.