The transition toward Industry 5.0 introduces a new paradigm for manufacturing systems, shifting the focus from pure automation to human-centric, sustainable and resilient industrial ecosystems. In this evolving landscape, production environments are increasingly modeled as complex cyber-physical systems, characterized by multi-scale interactions, nonlinear dynamics, uncertainty and strong interdependencies between physical processes, digital infrastructures and human operators. Traditional modeling and control strategies struggle to capture this complexity, creating a pressing need for adaptive and learning-based approaches. Recent advances in Artificial Neural Networks and Deep Learning are redefining the foundations of Advanced Manufacturing by enabling intelligent, data-driven, and self-optimizing production processes. Among manufacturing technologies, Additive Manufacturing (AM) stands out as one of the most promising and simultaneously most challenging domains for the deployment of Artificial Intelligence. Its multi-physics and multi-scale nature, coupled with strong sensitivity to process parameters and material variability, makes AM an ideal testbed for next-generation AI methodologies. While AM provides unprecedented geometric freedom, mass customization, and functional integration, its large-scale industrial adoption remains constrained by challenges related to predictive modeling, real-time monitoring, defect detection, process stability, certification, and sustainability. The management of tightly coupled thermal, mechanical, and microstructural phenomena requires computational frameworks capable of integrating physics-based knowledge with data-driven learning. In this context, neural networks, Physics-Informed Neural Networks (PINNs) and hybrid physics–data-driven approaches are emerging as key enablers for high-fidelity surrogate modeling, learning under limited and heterogeneous data regimes, real-time control architectures, autonomous defect detection, and multi-objective optimization of process parameters including energy efficiency and environmental sustainability metrics. This workshop aims to bring together researchers and practitioners from engineering, computer science, and industry to advance the state of the art in AI-driven Additive Manufacturing.
Workshop Organizers
- Fabrizia Devito
- Donato Impedovo
- Fulvio Lavecchia