Abstract
This work meets Metal Additive Manufacturing and Time Series Processing. It presents a four-step analytical procedure addressed to support the discovery of defect causes in 3D metal printing. The method has a phase of data space transformation, where the features space is firstly reduced and secondly exploited in a higher dimensional space. Later, a procedure for knowledge discovery is applied. Finally, by analyzing the results, it is concluded the most probable causes of the high rate of defects in the production phase. This procedure is proved with data obtained from a SLM machine, and the results are convincing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu, Y.: Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017). https://doi.org/10.1016/j.jii.2017.04.005
Frank, A.G., Dalenogare, L.S., Ayala, N.F.: Industry 4.0 technologies: implementation patterns in manufacturing companies. Int. J. Product. Econ. 210, 15–26 (2019). https://doi.org/10.1016/j.ijpe.2019.01.004
Mohan, N., Kangasharju, Y.J.: «Edge-fog cloud: a distributed cloud for Internet of things computations». In: 2016 Cloudification of the Internet of Things (CIoT), pp. 1–6, November 2016. https://doi.org/10.1109/ciot.2016.7872914
Herzog, D., Seyda, V., Wycisk, E., Emmelmann, Y.C.: Additive manufacturing of metals. Acta Mater. 117, 371–392 (2016). https://doi.org/10.1016/j.actamat.2016.07.019
Duque Anton, S., Ahrens, L., Fraunholz, D., Schotten, Y.H.D.: «Time is of the essence: machine learning-based intrusion detection in industrial time series data». In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1–6, November 2018. https://doi.org/10.1109/icdmw.2018.00008
Strauss, T., von Maltitz, Y.M.J.: Generalising Ward’s Method for Use with Manhattan distances. PLOS ONE 12(1), e0168288 (2017). https://doi.org/10.1371/journal.pone.0168288
Kruth, J.P., Mercelis, P., Vaerenbergh, J.V., Froyen, L., Rombouts, M.: Binding mechanisms in selective laser sintering and selective laser melting . Rapid Prototyping J. 11(1), 26–36 (2005). https://doi.org/10.1108/13552540510573365
Yadroitsev, I., Smurov, I.: Selective laser melting technology: from the single laser melted track stability to 3D parts of complex shape. Phys. Proc. 5, 551–560 (2010). https://doi.org/10.1016/j.phpro.2010.08.083
Rashid, R., Masood, S.H., Ruan, D., Palanisamy, S., Rahman Rashid, R.A., Brandt, M.: Effect of scan strategy on density and metallurgical properties of 17-4PH parts printed by selective laser melting (SLM) . J. Mater. Process. Technol. 249, 502–511 (2017). https://doi.org/10.1016/j.jmatprotec.2017.06.023
Spears, T.G., Gold, S.A.: In-process sensing in selective laser melting (SLM) additive manufacturing. Integr. Mater. Manufact. Innov. 5(1), 16–40 (2016). https://doi.org/10.1186/s40192-016-0045-4
Malekipour, E., El-Mounayri, H.: Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review. Int. J. Adv. Manufact. Technol. 1, 527–550 (2017). https://doi.org/10.1007/s00170-017-1172-6
Moreno, R., Pereira, J.C., López, A., Mohammed, A., Pahlevannejad, P.: Time series display for knowledge discovery on selective laser melting machines. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) IDEAL 2019. LNCS, vol. 11872, pp. 280–290. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33617-2_29
Acknowledgments
Data collection and curation have been accomplished within the DIGIQUAM Project, which has received funding from the EIT Manufacturing, and is supported by the EIT, a body of the European Union under grant agreement nº 20122. Time Series Analysis part has been founded by the project KK-2019/00095 (Departamento de Desarrollo Economico e Infraestructuras del Govierno Vasco. Programa ELKARTEK 2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Aramburu-Zabala, M., Masurtschak, S., Moreno, R., Jean-Jean, J., Veiga, A. (2020). Time Series Clustering for Knowledge Discovery on Metal Additive Manufacturing. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-62365-4_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62364-7
Online ISBN: 978-3-030-62365-4
eBook Packages: Computer ScienceComputer Science (R0)