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Fabio Fruggiero

Assistant Professor

University of Basilicata

Italy

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About

Fabio Fruggiero is an Assistant Professor at the University of Basilicata, Italy. His research areas focus on Industry 4.0 and 5.0, sustainable supply chains, human-robot interaction, and predictive maintenance in manufacturing. He has published several articles exploring advanced frameworks for collaborative workplaces, optimization techniques in additive manufacturing, and the application of machine learning in predictive maintenance.

Articles (15)

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2027

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2026

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2026

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2025

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2025

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2021

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2021

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2021

Collaborators (6)

Mario Tucci

Full Professor

University of Florence

ITALY

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Federico Briatore

University of Genoa

ITALY

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Fabrizio Caccavale

Associate Professor

Università degli Studi di Napoli Federico II

ITALY

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Duc Pham

University of Birmingham

UNITED KINGDOM

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Alexandra Lagorio

Assistant Professor

University of Bergamo

ITALY

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Leonardo Leoni

Associate Professor

Università degli Studi eCampus

ITALY

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Fabio Fruggiero

Research Interests

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About

Articles (15)

Inclusion of Workers with Functional Diversity in Human-Robot Collaboration: A Systematic Mapping Review

Multi-agent analysis of remanufacturing and leasing integration in closed-loop supply chains

From Proactive to Embodied Collaboration: A System-Centric Approach to Advancing HRC Quality

Exploring Industry 4.0’s Role in Sustainable Supply Chains: Perspectives from a Bibliometric Review

Sustainability-Aware Maintenance for Machine Tools: A Quantitative Framework Linking Degradation Management with Life-Cycle Cost and Environmental Performance

A scoping review of human robot interaction research towards Industry 5.0 human-centric workplaces

A Graph-Based Multi-level Framework to Support the Designing of Collaborative Workplaces

A cloud-aided remanufacturing framework to assess the relative complexity

Adjusted Iterated Greedy for the optimization of additive manufacturing scheduling problems

Explorative hybrid digital twin framework for predictive maintenance in steel industry

Feature investigation with Digital Twin for predictive maintenance following a machine learning approach

Human gesture system in Human Robot Interaction for reliability analysis

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector

Human Fatigue Evaluation During Smart Working Sessions

Collaborators (6)

Mario Tucci

Federico Briatore

Fabrizio Caccavale

Duc Pham

Alexandra Lagorio

Leonardo Leoni

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