Simulation-based analysis of 3D-printing industrial manufacturing processes

In the evolving landscape of Additive Manufacturing (AM), achieving scalable, efficient, and cost-effective production remains a critical challenge. As industries advance toward the industrialization of 3D printing, they face complex operational demands, including machine utilization, part flow synchronization, and post-processing efficiency. This thesis tackles these challenges by developing a simulation-based framework utilizing Discrete Event Simulation (DES) to enhance AM workflow efficiency across multiple technologies. Using FlexSim simulation software, the research models and analyzes production systems involving Selective Laser Sintering (SLS), Stereolithography (SLA), and Digital Light Processing (DLP). The DES approach enables detailed evaluation of workflow parameters, resource allocation, and job scheduling strategies. Through comprehensive case studies, the framework demonstrates its ability to identify system bottlenecks, minimize idle time, and improve throughput and labor efficiency. This thesis contributes a domain-specific DES framework tailored to AM environments, validated through empirical modeling and performance evaluation. The findings underscore the value of simulation in both the design and operational optimization of AM production lines. Furthermore, the research demonstrates the role of simulation tools in supporting data-driven decision-making, reducing production costs, and improving reliability in large-scale additive manufacturing. Ultimately, this thesis positions Discrete Event Simulation as a key enabler for the industrial adoption of AM technologies, offering a strategic path toward enhanced productivity, scalability, and operational resilience in modern manufacturing systems.