Programmable Sensing Composites

Most man-made objects are still in the analog era: Few man-made physical objects contain sensors and fewer still have any embedded computation alongside sensors. This situation is much like analog cameras at the turn of the century. Embedding digital computation alongside sensing in object fabrication processes and in fabricated structures could enable in situ metrology, in situ analysis during real-world use, and valuable statistics from every individual physical object. Such in-situ-processed digital summaries of the physical histories of internals and usage of objects could enable a fundamental shift in how we design, fabricate, and use physical objects.

Synthesized digital summaries of the internal conditions within physical objects could enable a revolution at the same scale or greater than the revolution in data-driven methods for computer vision enabled by ubiquitous digital cameras. Because they will enable embedding sensing combined with local in situ signal processing, sensor-augmented fabrication processes and sensor-augmented fabricated objects could enable a revolution with potential far-reaching benefits to society (better products that are more fit for purpose) and the environment (products manufactured with less waste).

Embedded sensing and computation could also enable a future where materials properties adapt, under control of computation, to the modes of usage of objects. Embeddable sense-and-compute devices and the associated research results and methods from this project could allow us, for the first time, to monitor those phenomena autonomously over a physical object's lifetime, to perform fundamentally new forms of structural integrity analysis based on real-time data monitored throughout the volume of products, and to enable new structural capabilities and commercial product capabilities, such as sensed-stress-driven adaptive recalls and data-driven product customization.

Our goal in this project is to investigate new fundamental methods for embedding computation and sensing into additively-manufactured objects and to use sensor data, analyzed in situ, to improve their materials formulation, design, and manufacturing. Data from continuous in-object metrology during real-world use could enable fundamentally-new and potentially-disruptive methods for manufacturing high-value items. Today, unlike in other areas of engineering, where large amounts of data from real-world use are revolutionizing tasks such as computer vision and speech recognition, manufactured objects are still largely un-instrumented, data-poor, and missing out on opportunities for data-driven usage-informed design, materials formulation, and manufacturing.

Today, manufactured objects are still in the analog era: Few manufactured objects contain sensors and fewer still have any embedded computation alongside sensors, like analog cameras from the turn of the century. Embedding digital computation alongside sensing in manufacturing processes and manufactured parts could enable in situ metrology, in situ analysis during real-world use, and valuable statistics from every manufactured object. Such synthesized digital summaries of the internal conditions within manufactured parts could enable a revolution at the same scale or greater than the revolution in data-driven methods for computer vision enabled by ubiquitous digital cameras. Because they will enable embedding sensing combined with local in situ signal processing, sensor-augmented manufacturing processes and sensor augmented manufactured objects could enable a revolution with potential far-reaching benefits to society (better products that are more fit for purpose) and the environment (products manufactured with less waste). Embedded sensing and computation could also enable a future where materials properties adapt, under control of computation, to the modes of usage of objects. This could enable the goals of the Manufacturing the Future research themes of Perpetual Transformable Products and Invisible Manufacturing.

The data that results from the embeddable sense-and-compute devices and the new methods for using that data to improve materials formulation and manufacturing parameter optimization could provide an opportunity to create new digital service industries, from data-driven computer-aided design (CAD), to data-driven materials chemistry formulation. And the embeddable sense-and-compute devices have the potential to influence new research, within the manufacturing research community and across disciplines.

The beneficiaries of the results from this project include:
(1) The manufacturing sector, both high-value manufacturers that use traditional methods (e.g., injection molding) and new high-value manufacturers using additive manufacturing. To both of these, embeddable sense-and-compute devices and new methods for using the data they generate to improve materials formulation and manufacturing parameter optimization could enable a step change in the ability to analyze the properties and physical phenomena within individual manufactured parts. Embeddable sense-and-compute devices and the associated research results and methods from this project could allow us, for the first time, to monitor those phenomena autonomously over a product's lifetime, to perform fundamentally new forms of structural integrity analysis based on real-time data monitored throughout the volume of products, and to enable new product capabilities, such as sensed-stress-driven adaptive recalls and data-driven product customization.

(2) The UK economy will benefit from the creation of new companies and their associated jobs enabled by the embeddable sense-and-compute devices, will benefit from new digital manufacturing industries based on embeddable sense-and-compute devices-derived data, will benefit from new computer-aided design (CAD) companies to use that data, and more.

(3) The UK population will benefit from the jobs and national pride resulting from embeddable sense-and-compute device-related industries, and from the capability provided by embeddable sense-and-compute device-enhanced materials for safer or more adaptive products that waste less raw material.

(4) The manufacturing, computing, and aerospace research communities, will benefit from the insights and results of this project and our results could enable new research directions, such as machine learning techniques using data from embeddable sense-and-compute devices.