Application of Artificial Intelligence to Enhance sustainability and Transparency in Additive Manufacturing: A Simulation-Based Study using MATLAB

Sammanfattning

Additive Manufacturing (AM), commonly referred to as 3D printing, has known as a transformative approach in modern manufacturing. It enables the production of complex geometries with reduced material usage, localized fabrication, and improved design flexibility. Despite these advantages, challenges remain in optimizing AM processes to ensure resource efficiency, particularly in minimizing material waste and reducing energy consumption during production. This thesis investigates how the integration of Artificial Intelligence (AI) can enhance the sustainability of AM, while also contributing to the broader goals of production transparency and traceability through Digital Product Passports (DPPs). The study focuses specifically on Fused Deposition Modeling (FDM), one of the most widely used AM techniques. Within this context, machine learning methods—namely linear regression and decision tree models and classification learners—are employed to analyze and predict the outcomes of various print parameters. The aim is to develop AI-based strategies that can optimize settings such as layer height, infill density, and print speed in order to reduce both material consumption and production time. To support this investigation, a series of 3D-printed test components were designed using CREO software. These parts were simulated with different parameter combinations to generate a diverse dataset for analysis. The collected data were then used to train and test predictive models in MATLAB. Although the experimental phase is still in progress, early simulation results indicate that AI-driven optimization has the potential to significantly improve manufacturing efficiency. In addition, the research explores how the insights generated by these models could be structured and recorded within a Digital Product Passport, providing a foundation for improved traceability and informed decision-making in digital supply chains. Overall, the thesis presents both a technical and conceptual framework for integrating AI into sustainable AM processes. It also reflects on the future role of intelligent manufacturing systems in enabling circular economy principles. The final results, including full model validation and performance analysis, will be documented following the completion of experimental testing.