Intelligent manufacturing paradigms: linking design optimization and sustainability in large-area additive manufacturing

Abstract The next generation of computer-aided intelligent manufacturing systems must enable the exploration and exploitation of cause-and-effect relationships across multiple disciplines. This capability strengthens human decision-making and supports sustainability-by-design in digital design-to-manufacturing workflows. To enhance system intelligence, seamless integration is needed between material systems, design methods, manufacturing processes, and sustainability metrics. This study presents a case study on large-scale mold manufacturing using large area additive manufacturing. A multidisciplinary design optimization (MDO) framework combines parametric and generative design strategies with manufacturing process planning, material selection, and environmental impact analysis. The study examines the trade-offs between structural integrity, production efficiency, and ecological impact, focusing on two different short fiber-reinforced polymer materials. Empirical and model-driven analyses methods reveal a direct correlation between mass reduction and improved sustainability. While carbon fiber reinforcement offers better structural performance, it also increases the carbon and water footprints by approximately 400% and 100%, respectively, compared to glass fiber alternatives. The case study on wind turbine rotor blade mold manufacturing highlights how parametric and generative design approaches can produce both structurally sound and sustainable solutions. Future research should focus on improving the algorithmic transparency of commercial software, increased flexibility to add manufacturability constraints, and potentially including sustainability models to enhance the intelligence in design-to-manufacturing workflows. This study highlights the potential of intelligent manufacturing systems to drive cleaner, more efficient, and sustainable production processes.