The global push for sustainable production, driven by initiatives like the Paris Agreement and the European Green Deal, necessitates reducing CO₂ emissions in industrial processes. Additive manufacturing (AM), with its potential for material efficiency and decentralization, offers promising opportunities for lowering carbon footprints. Due to the significant importance of enhancing the performance of AM via the fine-tuning of printing parameters, this study investigates the dual objectives of understanding parameter influences and leveraging artificial intelligence (AI) to predict CO₂ emissions in fused deposition modeling (FDM) processes. A full-factorial experimental design with 81 test prints was conducted, varying four key parameters—layer height, infill density, perimeters, and nozzle temperature—at three levels (min, mid, and max). The results highlight infill density as the most influential factor, significantly impacting material usage, energy consumption, and overall CO₂ emissions. Five AI algorithms were employed for predictive modeling, with XGBoost demonstrating the highest accuracy in forecasting emissions. By systematically analyzing process interdependencies and providing quantitative insights, this study advances sustainable 3D printing practices. The findings offer practical implications for optimizing AM processes, benefiting both researchers and industrial stakeholders aiming to reduce CO₂ emissions without compromising product integrity.

This study introduces a comprehensive knowledge graph (KG)-based framework designed to support sustainable decision-making by integrating, enriching, and analyzing heterogeneous data sources. The proposed methodology leverages domain expertise, real-world data, and synthetic data generated through language models to address challenges in life cycle assessment (LCA), particularly data scarcity and inconsistency. By modeling the entire product lifecycle, including engineering, production, usage, and disposal phases, the framework facilitates early-stage design decision-making and provides actionable insights for sustainability improvements. The methodology is validated through a case study on 3D printing (3DP), demonstrating its ability to manage complex data, highlight relationships between engineering decisions and environmental impacts, and mitigate data scarcity in the early phases of product development in the context of LCAs. In conclusion, the results demonstrate the framework’s potential to drive sustainable innovation in manufacturing.

Engineering education at the Institute for Information Management in Engineering integrates product lifecycle management (PLM), computer‐aided design (CAD), eXtended reality (XR), and artificial intelligence (AI) to enhance learning and prepare students for modern challenges. Our interdisciplinary approach, emphasizing digital twins and virtual twins, fosters immersive, hands‐on experiences. This paper reviews our strategies, comparing them with global initiatives, highlighting the transformative impact of our curriculum on preparing future engineers for complex industrial environments.

Knowledge graphs and AI enable rapid sustainability assessment in product development. Knowledge graphs structure interconnected semantic information, enhancing data interoperability and managing complex relationships. AI leverages this to automate analysis and aid decision-making. By embedding sustainability metrics, the environmental impacts of choices in product development can be compared. This article reviews knowledge graphs in sustainability assessment and AI’s role in enhancing these capabilities. It also presents a case study on carbon footprint analysis through knowledge graphs and AI.

Knowledge Graphs (KGs) offer a robust solution for integrating heterogeneous sustainability data and enhancing decision-making transparency. However, current KG-based sustainability assessments predominantly focus on carbon-related indicators and rarely address uncertainty and data completeness. This limitation restricts their ability to support holistic and robust sustainability evaluations. This paper presents an extended sustainability knowledge graph framework that integrates environmental, water-related, and social indicators while explicitly modeling uncertainty and completeness at the indicator level. Building upon an existing KG architecture, the proposed methodology incorporates water footprint assessment in accordance with ISO 14046 principles and social sustainability indicators derived from Social Life Cycle Assessment frameworks. Uncertainty is modeled using indicator-specific distributions and propagated through Monte Carlo simulation, enabling uncertainty-aware sustainability ranking. The methodology is demonstrated through a case study in polymer-based Additive Manufacturing. The results show that water and social indicators can significantly influence sustainability rankings and that uncertainty may reduce the robustness of conclusions derived from deterministic assessments. By enabling integrated, uncertainty-aware sustainability analysis, the proposed framework supports more informed decision-making in sustainable manufacturing.