Distributed manufacturing for and by the masses - Science

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Distribution and democratization represent two complementary paradigms that are gaining increasing attention in manufacturing. Distributed manufacturing (DM) allows for geographically dispersed production, often at small scales and near the end user. Democratization enables large populations to engage in manufacturing. Massively distributed manufacturing (MDM), which combines these paradigms, is performed on demand by a large network of people located anywhere. Rather than rely on mass production in centralized factories, MDM promises to improve the responsiveness and resilience of manufacturing to urgent production demands (such as emergencies like pandemics); promote mass customization and cost-effective, low-volume production; gainfully employ many informally trained citizens in manufacturing (such as through the gig economy); and reduce the environmental footprint of manufacturing by producing items near their points of use. The Fourth Industrial Revolution will play an important role in enabling MDM by way of cyber-physical operating systems (CPOSs).

From the First Industrial Revolution in the 18th century onward, manufacturing has been carried out predominantly through mass production in centralized factories, often far from the end user. Mass production enables large quantities of products to be produced with standardized quality, high productivity, and low cost. However, in the face of urgent demands or disruptions, it lacks flexibility, agility, and resilience and cannot readily provide consumers with personalized products in small quantities (mass customization) (1). Moreover, its environmental footprint is large, mainly because it often requires raw materials and finished goods to be transported over long distances.

During the past decade, there has been growing interest and activity in distributed and democratized manufacturing as alternative or complementary paradigms to mass production. DM has been emphasized by the United Nations International Development Organization (2), the World Economic Forum (3), and other major agencies (4, 5) as critical to the future of manufacturing. Several companies engaged in DM, such as 3D Hubs, 3Diligent, Fast Radius, and Xometry, have sprouted. Xometry, for example, enables its customers to access the manufacturing capacity of a network of >5000 carefully curated partners—typically small- and medium-sized enterprises—distributed across the world. In terms of democratization (6), perhaps the most compelling example is the proliferation of desktop three-dimensional (3D) printers, which currently retail on average for ∼$1000 (7), which is within the purchasing power of large portions of the population. In 2019, >700,000 desktop 3D printers were sold globally (7). These printers can now be found in homes, offices, schools, maker spaces, public libraries, and other facilities, and people can use them for prototyping and small-scale or micromanufacturing without extensive technical training.

Networked systems linking producers to consumers

Massively distributed manufacturing uses a cyber-physical operating system and artificial intelligence tools to connect and coordinate consumers with producers. Producers in micromanufacturing units can use three-dimensional (3D) printing to fabricate customized products. Smart logistics such as drones and rideshare services enable the physical product delivery.

GRAPHIC: C. BICKEL/SCIENCE

However, distributed and democratized manufacturing are still far from the goal of MDM (8–10), in which products are manufactured by a large, diverse, and geographically dispersed but coordinated network of individuals and organizations with agility and flexibility, but with near–mass-production quality, productivity, and cost effectiveness. For example, a company like Xometry would need to engage millions of users in micromanufacturing across the globe, similar to what companies like Uber and Lyft have achieved with transportation. The latent potential of MDM was evident during the early days of the COVID-19 pandemic, when personal protective equipment (PPE) were in short supply. Mass production was too slow to react to the sudden demands for PPE, including demands for simple but vital plastic products like face shields. Worldwide, thousands, if not millions, of people, many of whom did not have experience in making these products, organized themselves into small networks to produce millions of face shields and other PPE using desktop 3D printers and other small-scale manufacturing equipment (11). This effort exposed key challenges of MDM in terms of standardizing production requirements, guaranteeing quality and reliability, and attaining high production efficiencies that can rival those of mass production.

This example illustrates the important role of technology in enabling MDM. The First, Second, and Third Industrial Revolutions, driven by mechanization, electrification plus assembly lines, and digital computing, respectively, paved the way for the Fourth Industrial Revolution (or Industry 4.0), undergirded by networked cyber-physical systems and artificial intelligence. For example, Xometry leverages cloud computing and machine learning to power its instant quoting engine that enables customers to receive pricing, expected lead times, and manufacturability feedback within seconds. Similarly, 3Diligent uses cloud computing to enable manufacturers in its network to route jobs across their shop floors and track quality.

With advances in Industry 4.0, manufacturing machines (including low-cost 3D printers) are increasingly equipped with sensors and cloud connectivity (12). The large amounts of data generated by these sensors are being used in machine-learning algorithms to provide predictive and corrective actions (13). Advanced cloud-based controllers are being developed to improve the quality and productivity of the machines (14). These advances in technology and automation can converge into a cloud-based CPOS for MDM.

An inspiration for CPOSs is the central coordinator used in distributed computing to automate the allocation and execution of large-scale computing tasks on distributed networks of computers. The central coordinator has enabled Folding at Home (15), a distributed computing cluster that leverages the idle capacity of >100,000 personal computers to run simulations that help scientists to understand how proteins fold. Similarly, a CPOS will intelligently, efficiently, and securely coordinate large networks of cloud-connected, autonomous, and geographically-dispersed manufacturing resources. It will optimally allocate manufacturing jobs to the resources connected to it and leverage distributed and democratized delivery systems, such as shared vehicles and drones, for logistics (see the figure). It will apply machine learning to the data gathered from sensors to help assure and improve quality and to optimize operations. Furthermore, CPOSs will leverage the ingenuity of humans through the crowdsourcing of ideas to improve manufacturing operations across networks of manufacturers as well as cybersecurity measures to protect intellectual property and the privacy of participants. CPOSs will thus allow the collaboration of large, autonomous, heterogeneous, and geographically dispersed networks of manufacturers to rapidly respond to production demands and disruptions with agility and flexibility, while ensuring the high quality, productivity, and cost effectiveness of MDM.

References and Notes

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   Foresight, The Future of Manufacturing: A New Era of Opportunity and Challenge for the UK Summary Report (The Government Office for Science, London, UK, 2013).

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   European Factories of the Future Research Association, Factories of the Future: Multi-annual Roadmap for the Contractual PPP under Horizon 2020 (European Commission, 2013).

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Acknowledgments: C.E.O. is a founder of Ulendo, which has licensed research in advanced cloud-based 3D printer control algorithms.

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