A Human–AI Collaborative Framework for Additive Manufacturing Modeling and Decision-Making

Even though Additive Manufacturing (AM) has become a critical enabler of manufacturing in various industries, its full potential in terms of process quality and productivity has not been achieved yet. The recent developments in Artificial Intelligence (AI) can help toward this goal, especially through Human–AI Collaboration (HAIC). However, existing approaches are focused on certain aspects of the problem, without comprehensively tackling the issue. This study proposes a holistic and AM-specific HAIC framework that combines the different components of human expertise, explainable AI, simulation-based forecasting, and variable-based process control into an integrated decision-making structure. The key findings include the identification of the most important variables that should be utilized, including their classification through the input of experts in terms of importance (utilizing the presented M-S metric), controllability, and the most suitable agent (human, AI, both) to effectively control each variable. Finally, the concept of the framework for effective HAIC in AM is analyzed, including the operational sequence of sensing, AI analysis, human evaluation, decision implementation, and feedback loops. Two complementary case studies are presented; the first provides a conceptual example, and the second one develops a quantitative scenario that allows the comparison of three decision pathways—AI-only, Human-only, and HAIC.