SSC2025 Programme Booklet

Next-Generation Manufacturing: Challenges, Issues, and Future Strategies Peony 4504 9 December 9:50am

Tae-Eog Lee Korea Advanced Institute of Science & Technology

As we venture into the era of next-generation manufacturing, the need to achieve higher quality, efficiency, flexibility, and sustainability becomes more pressing. This is especially true under unprecedented uncertainty, variability, and risk. To meet these demands, we must better deploy digital transformation, AI, robotics, etc. However, we still face significant challenges, issues, and limitations. We critically review them and propose urgent future strategies. Digital transformation based on computers, the Internet, IoT, etc., is more vital than ever, and generates massive data, leading to extensive AI deployment and extreme automation. However, digitalization tools and data are confronted with interoperability and integration with legacy systems. Standardization, middleware platforms, information sharing, cross-functional collaboration, end-to-end process integration, and the removal of organizational silos may resolve the issues, but still stall. We discuss problems and new strategies. AI has transformed manufacturing processes, engineering and design, and operation management toward a smart factory, and has automated human labor and even decision making in detection, classification, prediction, control, design, and optimization, leading to mass customization. However, its effectiveness is compromised by the inconsistency, quality, and fragmentation of training data, and the opaque nature of AI decision models. Solutions such as unified data lakes, master data management, and explainable AI have been proposed. However, there is still a long way to go. Our workforce's growing skill gaps in AI and data management are another serious issue. We discuss the feasible strategies. The manufacturing industry has further ongoing pressing concerns. Automation by AI and robotics causes tremendous job displacement. The contribution of the salary from the manufacturing industry to the national economy is significantly diminishing. Sustainability issues such as global warming due to CO2 emissions, energy consumption, material waste, and environmental pollution have become increasingly urgent. Global supply chain vulnerabilities and uncertainties demand our immediate actions for trustworthy nation-level collaboration. International politics and competition on trading, customs taxes, make the problems more complicated. It is time to rethink and redesign the sustainable manufacturing mission in the context of sustainability systems, global trading, supply chains, international politics, and the economy.

An AI-powered Self-driving Biofoundry for Synthetic Biology Peony 4401 9 December 9:50am

Huimin Zhao University of Illinois Urbana-Champaign

Naturally occurring or engineered biological systems such as protein machines, genetic circuits, and microbial cell factories have promised to solve many grand challenges of modern society. However, the existing processes for discovery, characterization, and engineering of biological systems are slow, expensive, and inconsistent, representing a major obstacle in synthetic biology. To address these limitations, my lab has been developing an AI-powered self-driving biofoundry since 2013. In this presentation, I will highlight a few case studies including: (a) PlasmidMaker: a versatile, robust, automated end-to-end platform that allows error-free construction of plasmids with virtually any sequences in a high throughput manner (Enghiad et al. Nature Communications 2022). (b) FAST-RiPPs and FAST-NP: a scalable platform that combines genome mining with automated refactoring or direct cloning of biosynthetic gene clusters for discovery of bioactive natural products (Ayikpoe et al. Nature Communications 2022; Yuan et al. Cell Systems 2025). (c) CLEAN: an AI tool for enzyme function prediction (Yu et al. Science 2023), (d) EZPSecificity: an AI tool for enzyme substrate specificity prediction (Cui et al. Nature 2025), and (e) BioAutomata: an AI-powered self-driving biofoundry for pathway optimization and protein engineering (Hamedi et al. Nature Communications 2019; Singh et al. Nature Communications 2025). In addition, I will discuss our current and future efforts in making our AI-powered self-driving biofoundry accessible to the broader research community and bringing the power of innovation in synthetic biology to everyone.

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