@inbook{1af7d9b3aac04055bcbf67dd70b0d329,
title = "Development and Evaluation of a Machine Learning Model for the Prediction of Failures in an Injection Moulding Process",
abstract = "Introduction: Developing and evaluating a machine learning model to predict failures in an injection moulding process offers significant potential to advance manufacturing competitiveness. Objectives: Failure prediction is considered one of the industry{\textquoteright}s main applications of machine learning since it is crucial in process cost avoidance. The study of control parameters of the injection moulding (IM) process is a direct application of artificial intelligence in the industry. Still, there is the problem of the human acquisition of data. This research proposes an approach for failure detection and prediction in a dataset from an IM machine. Results: Results indicate that the approach helps find the optimal operating values for an IM process and reveals an identification scheme of failures and their relationship with the steps of the IM process. Conclusion: By performing a data clustering based on unlabelled data, information about productivity and quality of the IM process are obtained.",
keywords = "Data analytics for applications in Industry 4.0, DBSCAN, Failure detection, Industry 4.0, Injection moulding, Machine learning, Manufacturing processes, Parameters optimisation, Unsupervised learning",
author = "A. Rojas-Rodr{\'i}guez and Chiwo, {F. S.} and H. Arcos-Guti{\'e}rrez and C. Ovando-V{\'a}zquez and Gardu{\~n}o, {I. E.}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-29775-5_5",
language = "English (US)",
series = "EAI/Springer Innovations in Communication and Computing",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "101--122",
booktitle = "EAI/Springer Innovations in Communication and Computing",
address = "Germany",
}