Pacini Alessio

CV: https://drive.google.com/file/d/1-lLfsONRUXVeeds5NpB63AO2NDiQIFTZ/view?usp=sharing

LinkedIn: https://it.linkedin.com/in/alessio-pacini-2ab67921a

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Ciclo: XXXIX

Curriculum: Mechanical Engineering

Title of the PhD Project: Self-X in Reconfigurable Manufacturing Systems

Supervisor(s): Prof. Ing. Michele Lanzetta

In collaboration with: _

Abstract of the PhD project:

In response to increasing product variability and the need for customization, transitioning assembly systems from engineer-to-order to configure-to-order is imperative. However, achieving rapid and cost-effective system reconfiguration, control, and programming remains a challenge. This has prompted emerging trends focused on enhancing the affordability and efficiency of reconfigurable assembly systems. My PhD project aims to address these challenges through the strategic integration of robotics, computer vision, machine learning, online control, and additive manufacturing, leveraging Self-X principles. The overarching objectives are: (i) to establish a new comprehensive framework for reconfigurable assembly systems with Self-X capabilities; (ii) to develop tools and approaches enabling streamlined reconfiguration, simplified product setup changes, minimized design and programming efforts, and facilitated system integration and interoperability. The methodology encompasses theoretical modelling, experimental validation, and real-world case studies. Central to this approach is the utilization of enriched CAD models as guiding tools for system design and programming. Throughout the project, particular emphasis will be placed on leveraging the enriched CAD models to expedite the design of assembly tools and explore their potential for assembly-related task programming at the high task-level. Additionally, the use of these models for efficient human-machine knowledge transfer for process automation will be explored in comparison to recent trends in artificial intelligence. Industrial case studies will play a pivotal role in implementing and validating various components of the proposed framework.

Teaching experience:

• Fondo Giovani 2023/2024, Course: Ottimizzazione dei processi produttivi. CdLM Mechanical Engineering (1 h), University of Pisa, Italy
• Additive Manufacturing (AM): Masked Stereolithography (MSLA) and Fused Deposition Manufacturing (FDM), support generation, warping. Introduction to commercial software: Ultimaker Cura and Chitubox. Course: Tecnologia Meccanica. CdL Energy Engineering (2h), University of Pisa, Italy
• Industrial grippers and gripping technology. Course: Assembly Technology. CdLM Management Engineering (1h), KTH Royal Institute of Technology, Stockholm, Sweden

International experience:

• Visiting PhD Student: March 2024 to June 2024, KTH Royal Institute of Technology, Stockholm, Sweden

Publications

Scopus:

https://www.researchgate.net/profile/Alessio_Pacini

Orcid ID: https://orcid.org/0009-0003-1864-4576

Articles:

Alessio Pacini, Francesco Lupi, Andrea Rossi, Maurizia Seggiani, Michele Lanzetta (2023). Direct Recycling of WC-Co Grinding Chip. Materials. 16(4):1347.

Link: Materials | Free Full-Text | Direct Recycling of WC-Co Grinding Chip (mdpi.com)

Francesco Lupi, Alessio Pacini, Michele Lanzetta (2023). Laser powder bed additive manufacturing: A review on the four drivers for an online control, Journal of Manufacturing Processes, Volume 103, Pages 413-429

Link: Laser powder bed additive manufacturing: A review on the four drivers for an online control - ScienceDirect

Conferences