Smart-AM2025 is the first workshop which was carried out within the “OPTIMA: depOsition of cold sPray in the realm of green addiTIve manufacturing through construction of MAchine learning models” and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – Missione 4 Istruzione e ricerca COMPONENTI M4C1 e M4C2, D53D23017410001).  This workshop aims to explore the emerging convergence between AM, AI, and HCI, with a specific focus on integrating data-driven strategies, machine learning (ML), deep learning (DL), and hybrid physics-informed models into AM workflows. Using real-time data from sensors, thermal/optical imaging, simulations, and digital twins, AI offers powerful tools to optimize process parameters, predict outcomes, detect anomalies, and ultimately increase automation, reproducibility, and throughput in additive processes. The workshop will serve as a platform for researchers, developers, and practitioners to discuss novel research, methodologies, case studies, and industrial applications in which AI and HCI enhance the performance, traceability, and adaptability of additive manufacturing processes.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full papers exploring the intersection of HCI, AI, and digital manufacturing, with a focus on designing interfaces and interaction paradigms that empower users, facilitate collaboration, and drive innovation in intelligent production environments. Special emphasis will be placed on the role of HCI in fostering transparent, adaptive, and user-centered manufacturing environments, especially, in light of the evolving paradigms of Industry 5.0. 

List of Topics

• HCI strategies for human-in-the-loop systems: collaborative AI, interactive model debug-ging, and intuitive control of AM systems
•  AI for process parameter optimization in AM
• Multi-output and real-time prediction of AM outcomes (e.g., porosity, adhesion, microstructure)
• Deep learning (e.g., CNNs) for layer-wise defect detection
• Integration of thermal/optical imaging into AM monitoring
• Reinforcement learning for adaptive process control
• Explainable and trustworthy AI in manufacturing
• Simulation-informed and physics-based ML models
• Multimodal data fusion (image, sensor, numerical data)
• Generative design for additive manufacturing
• Virtual Reality & Augmented Reality in AM
• Ethics, sustainability and environmental impact of smart AM

Committees

Program Committee

       • Fabiola De Marco, University of Salerno

       • Alessia Auriemma Citarella, University of Salerno

       • Alessia Serena Perna, University of Naples Federico II

       • Francesco Napolitano, Consiglio Nazionale delle Ricerche (CNR)

       • Luigi D’Arco, University of Naples Federico II

       • Antonio Viscusi, Universitas Mercatorum

       • Domenico Rossi, University of Salerno

       • Federica Donadio, Consiglio Nazionale delle Ricerche (CNR)

Organizing committee

• Fabiola De Marco
• Alessia Serena Perna
• Alessia Auriemma Citarella

Venue

The conference will be held  as part of the 16th Biannual Conference of the Italian SIGCHI Chapter in Salerno, Italy (CHITALY 2025 < https://chitaly2025.di.unisa.it>).

Contact

All questions about submissions should be emailed to fdemarco@unisa.it, alessiaserena.perna@unina.it and aauriemmacitarella@unisa.it