TY - GEN
T1 - Automated MOLDAM Robotic System for 3D Printing
T2 - International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023
AU - Antolín-Urbaneja, Juan Carlos
AU - Bengoa Ganado, Pablo
AU - Mateu, Alex
AU - Fernández Valares, Jon Borha
AU - Hernández Vicente, Jose
AU - Bellvert Rios, Eduard
AU - Vallejo Artola, Haritz
AU - Alberdi Olaizola, Nerea
AU - Pacheco Goñi, Rakel
AU - González Ojeda, Itzel de Jesús
AU - Luengo Pizarro, Ana Isabel
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Additive manufacturing by extrusion of thermoplastic materials is becoming a promising technology due to the flexibility for generating parts of various sizes with complicated geometries, reducing lead time. Specifically, the additive manufacturing of thermoplastics in pellet format allows material deposition rates of tens of kg/h, being able to generate thermoplastic pre-molds in a reasonable time that can help improve profitability compared to the manufacture of metal moulds, contributing to material saving. Under this approach, a new automated robotic cell has been developed to manufacture pre-moulds for the aeronautical sector integrating a Fanuc M900iB/700 robot and a high feed rate pellet extruder. Along the project, the printed material has been selected and characterized under certain conditions, taking into account the requirements of the most demanding applications. On the other hand, the behaviour of the designed parts under working con-ditions has been simulated. Finally, the implementation of the robotic system has allowed the preliminary characterization of the pre-mould geometrical performance under the customer’s requirements. This article presents the manufacturing results of one use case using a new automated robotic system for 3D printing including future challenges.
AB - Additive manufacturing by extrusion of thermoplastic materials is becoming a promising technology due to the flexibility for generating parts of various sizes with complicated geometries, reducing lead time. Specifically, the additive manufacturing of thermoplastics in pellet format allows material deposition rates of tens of kg/h, being able to generate thermoplastic pre-molds in a reasonable time that can help improve profitability compared to the manufacture of metal moulds, contributing to material saving. Under this approach, a new automated robotic cell has been developed to manufacture pre-moulds for the aeronautical sector integrating a Fanuc M900iB/700 robot and a high feed rate pellet extruder. Along the project, the printed material has been selected and characterized under certain conditions, taking into account the requirements of the most demanding applications. On the other hand, the behaviour of the designed parts under working con-ditions has been simulated. Finally, the implementation of the robotic system has allowed the preliminary characterization of the pre-mould geometrical performance under the customer’s requirements. This article presents the manufacturing results of one use case using a new automated robotic system for 3D printing including future challenges.
KW - 3D printing
KW - Additive Manufacturing
KW - material extrusion (MEX)
KW - Moulds
KW - pellet extrusion
KW - Robotic cell
KW - thermoplastic extrusion
UR - http://www.scopus.com/inward/record.url?scp=85214380931&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70981-4_29
DO - 10.1007/978-3-031-70981-4_29
M3 - Conference contribution
AN - SCOPUS:85214380931
SN - 9783031709807
T3 - Lecture Notes in Networks and Systems
SP - 429
EP - 447
BT - Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application
A2 - Garcia, Marcelo V.
A2 - Gordón-Gallegos, Carlos
A2 - Salazar-Ramírez, Asier
A2 - Nuñez, Carlos
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 November 2023 through 10 November 2023
ER -