Robotic additive manufacturing by laser metal deposition 
in the context of industry 4. 0

Manufatura aditiva robotizada por deposição de metal a laser 
no contexto da indústria 4. 0

Autores

Resumo

The integration of a KUKA robot with a Meltio Engine head to create a laser metal deposition by wire (LMD-wire) cell involves a meticulous installation and must be highly controlled to function with the required precision. To this end, the creation of a digital twin became essential to operate the cell and to generate data that enable its monitoring, through an artificial intelligence system that optimizes its performance and prevents equipment wear. The goal of this article is to present the research that the Industrial Automation Innovation Group at the University of Brasília has been developing. The procedures are detailed, such as the installation of the robot, the Meltio-KUKA integration, the elaboration of a planar slicer with Rhino3D-Grasshopper and the simulation process with KUKA.Sim. The simulations’ results confirmed the robustness of the system for planar deposition methods and demonstrated its versatility to adapt to non-planar, multiplanar and hybrid deposition methods. The integration of the LMD-wire cell will remain under evaluation until its complete installation, when it will be ready for the first deposition experiments.

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Biografia do Autor

Igor Lacroix, Universidade de Brasilia

Possui graduação em Arquitetura e Urbanismo pelo Centro Universitário de Brasília (2004), mestrado em Teoria, História e Crítica da Arquitetura e Urbanismo pela Universidade de Brasília (2013) e doutorado em Tecnologia, Ambiente e Sustentabilidade pela Universidade de Brasília (2020). Atualmente, é investigador no Digital Fabrication Laboratory - DFL da Faculdade de Arquitetura da Universidade do Porto - FAUP. Tem experiência em docência e prática de Arquitetura, com ênfase em projeto paramétrico e fabricação digital.

Marco Maron, Universidade de Brasilia

Arquiteto e urbanista graduado pelo Centro Universitário de Brasília (UniCEUB - 2017). Pós graduação em andamento em Reabilitação Ambiental Sustentável no Laboratório de sustentabilidade (LASUS) da Faculdade de Arquitetura e Urbanismo (FAU) da Universide de Brasília (UnB).Tem experiência na área de Conforto ambiental, Eficiência energética e Ecologia, com ênfase no ambiente construido.Arquiteto sócio em M.4.M_A.A.D. Finalista do concurso Opera Prima. Indicado ao concurso Archiprix International. Mestrando em Sistemas Mecatônicos da Universidade de Brasilia.

Brayan Figueroa, Universidade de Brasilia

Brayan S. Figueroa concluiu com sucesso sua graduação em Engenharia Mecatrônica pela Universidade de Pamplona (UP) localizada em Pamplona, Colômbia. Seus diversos interesses de pesquisa abrangem uma ampla gama de áreas, incluindo redes neurais artificiais, modelagem e design 3D, automação industrial e controle, visão artificial, controle inteligente, redes complexas, agricultura de precisão, energias renováveis, robótica, controle não linear, processamento digital de imagens e Internet das Coisas (IoT). Atualmente, ele está cursando um mestrado em Sistemas Mecatrônicos na Universidade de Brasília (UnB) no Brasil, onde está focado nas áreas de robótica, sensoriamento, controle e automação. Suas áreas atuais e principais de expertise incluem manufatura aditiva de metais, Indústria 4.0, robótica, gêmeos digitais e desenvolvimento de software em manufatura aditiva.

Referências

ALVARES, Alberto José. Simulation with Kuka.Sim to Robotic Additive Manufacture. Vídeo tutorial, 2023. Disponível em: <https://youtu.be/ukc_CrEFBRE>.

ALVARES, A.J.; LACROIX, I.; MARON, M.A.; FIGUEROA, B. Desenvolvimento de uma célula de manufatura aditiva robotizada baseada no processo deposição de metal a laser usando arame de soldagem. Peer Review, v. 5, n. 21, p. 17-39, 2023.

BIEGLER, M.; GRAF, B.; RETHMEIER, M. In-situ distortions in LMD additive manufacturing walls can be measured with digital image correlation and predicted using numerical simulations. Additive Manufacturing, v. 20, p. 101-110, 2018.

CABRAL, J.V.A.; GASCA, E.A.R.; ALVARES, A.J. Digital twin implementation for machining center based on ISO 23247 standard. IEEE Latin America Transactions, v. 21, n. 5, p. 628-635, 2023.

CHEN, X.; XIAO, M.; KANG, D.; SANG, Y.; ZHANG, Z.; JIN, X. Prediction of geometric characteristics of melt track based on direct laser deposition using M-SVR algorithm. Materials, v. 14, n. 23, 2021.

CUEVAS, Diego García; PUGLIESE, Gianluca. Advanced 3D Printing with Grasshopper, Clay and FDM. Publicação independente, 2020. ISBN 9798635379011.

FENG, A.; CHEN, C.; WU, C.; WEI, Y.; WANG, Y. Modeling of laser melting deposition equipment based on digital twin. Metals, v. 12, n. 2, 2022.

FIGUEROA, Brayan. Digital twin LMD additive manufacturing cell. Vídeo tutorial, 2023. Disponível em: <https://www.youtube.com/watch?v=80m1G-9dw7k>.

GARMENDIA, I.; PUJANA, J.; LAMIKIZ, A.; MADARIETA, M.; LEUNDA, J. Structured light-based height control for laser metal deposition. Journal of Manufacturing Processes, v. 42, p. 20-27, 2019.

GLAESSGEN, E.; STARGEL, D. The digital twin paradigm for future of NASA and U.S. Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, p. 1-14, 2012.

GODWYLL, Robin. Programming Robots in Grasshopper. Vídeo tutorial, 2022. Disponível em: <https://tinyurl.com/4tffnsnk>.

GU, H.; LI, L. Computational fluid dynamic simulation of gravity and pressure effects in laser metal deposition for potential additive manufacturing in space. International Journal of Heat and Mass Transfer, v. 140, p. 51-65, 2019.

HUANG, Z.; SHEN, Y.; LI, J.; FEY, M.; BRECHER, C. A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics. Sensors, v. 21, n. 19, p. 1-35, 2021.

ISO 23247. Automation systems and integration — digital twin framework for manufacturing — part 1: Overview and general principles. 2021.

JAN, Z.; AHAMED, F.; MAYER, W.; PATEL, N.; GROSSMANN, G.; STUMPTNER, M.; KUUSK, A. Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, v. 216, 2023.

KIRDEIKIS, Gediminas. Robotic manufacturing course: Part 2 - Robot arm setup for cutting (Rhino + Grasshopper). Vídeo tutorial, 2021. Disponível em: <https://www.youtube.com/watch?v=6pJKKV-aWgc>.

LETTORI, J.; RAFFAELI, R.; BILANCIA, P.; PERUZZINI, M.; PELLICCIARI, M. A review of geometry representation and processing methods for cartesian and multiaxial robot-based additive manufacturing. The International Journal of Advanced Manufacturing Technology, v. 123, n. 11, p. 3767-3794, 2022.

LIU, C.; LE ROUX, L.; KÖRNER, C.; TABASTE, O.; LACAN, F.; BIGOT, S. Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems, v. 62, p. 857-874, 2022.

LU, Y.; LIU, C.; WANG, K.I.K.; HUANG, H.; XU, X. Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, v. 61, 2020.

LUO, W.; HU, T.; YE, Y.; ZHANG, C.; WEI, Y. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robotics and Computer-Integrated Manufacturing, v. 65, 2020.

PIRES, J.N.; AZAR, A.S.; NOGUEIRA, F.; ZHU, C.Y.; BRANCO, R.; TANKOVA, T. The role of robotics in additive manufacturing: review of the AM processes and introduction of an intelligent system. Industrial Robot, v. 49, n. 2, pp. 311–331, 2022.

QIAO, Q.; WANG, J.; YE, L.; GAO, R.X. Digital twin for machining tool condition prediction. Procedia CIRP, v. 81, p. 1388-1393, 2019.

SCHWAB, K. The fourth industrial revolution. Londres: Penguin Books, 2017.

SHAO, G.; HELU, M. Framework for a digital twin in manufacturing: Scope and requirements. Manufacturing Letters, v. 24, p. 105-107, 2020.

TONG, X.; LIU, Q.; PI, S.; XIAO, Y. Real-time machining data application and service based on IMT digital twin. Journal of Intelligent Manufacturing, v. 31, p. 1113-1132, 2020.

YAN, L.; CHEN, Y.; LIOU, F. Additive manufacturing of functionally graded metallic materials using laser metal deposition. Additive Manufacturing, v. 31, 2020.

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Publicado

2023-12-21

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Articles