Beatriz Bretones Cassoli

Energieeffizienz durch intelligentes in-Prozess Quality Monitoring (Endi-QM); Förderbereich: Industrie, Gewerbe, Handel und Dienstleistungen

David Krönert, Robin Spies, Beatriz Bretones Cassoli, Andreas Clément et autres

Das übergeordnete Ziel von Endi-QM besteht darin, ein autonom agierendes System durch die intelligente Nutzung von Produktions-, Maschinen- und Prozessdaten aufzubauen, welches über Machine Learning basierte Optimierungsverfahren selbstständig die Prozessparameter von Fertigungszelle und Maschine regelt, um die Nutzung der natürlichen und betrieblichen …

SEA4DQ 2024 Workshop Summary

Tim Menzies, Bowen Xu, Hong Jin Kang, Jie M. Zhang et autres

Welcome to the sixth edition of the workshop on Machine Learning Techniques for Software Quality Evaluation (SEA4DQ 2024), held in Brazil, July 16th, 2024, co-located with ESEC / FSE 2024 [1]. Three papers from all over the world were submitted, and all …

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A new benchmark dataset for machine learning applications in discrete manufacturing: CiP-DMD

Nicolas Jourdan, Tobias Biegel, Beatriz Bretones Cassoli, Joachim Metternich

The development of machine learning applications in manufacturing depends primarily on the availability of meaningful and reliable data. In this paper, we present the Center for industrial Productivity - Discrete Manufacturing Dataset (CiP-DMD), the first open-source discrete manufacturing dataset of a multi-step …

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Data-driven long-term condition-based maintenance using anomaly detection under concept drift: A case study of an ultrasonic sieve machine

Feng Zhua, Nicolas Jourdan, Beatriz Bretones Cassoli, Joachim Metternich

Class imbalance and concept drift are two critical barriers to applying AI-based condition monitoring models in industrial manufacturing. In practical industrial production, there are usually more normal samples than abnormal samples, and the data distribution varies over time, limiting the training and …

Challenges for Predictive Quality in Multi-stage Manufacturing: Insights from Literature Review

Beatriz Bretones Cassoli, Joachim Metternich

This paper investigates data quality challenges in applying predictive quality solutions for multi stage discrete manufacturing. Through an analysis of existing research via systematic literature search, we highlight key obstacles that affect the implementation of machine learning approaches for quality control, such …

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A new benchmark dataset for machine learning applications in discrete manufacturing: CiP-DMD

Nicolas Jourdan, Tobias Biegel, Beatriz Bretones Cassoli, Joachim Metternich

The development of machine learning applications in manufacturing depends primarily on the availability of meaningful and reliable. In this paper, we present the Center for industrial Productivity - Discrete Manufacturing Dataset (CiP-DMD), the first open-source discrete manufacturing dataset of a multi-step machining …

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Toward the Sustainable Development of Machine Learning Applications in Industry 4.0Applications in Industry 4.

Sara Ellenrieder, Nicolas Jourdan, Tobias Biegel, Beatriz Bretones Cassoli et autres

Abstract As the level of digitization in industrial environments increases, companies are striving to improve efficiency and resilience to unplanned disruptions through the development of Machine Learning (ML)- based applications. Still, sustainable deployment and operation beyond proofs-of-concept is a challenging and resource-intensive …

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Multi-source data modelling and graph neural networks for predictive quality

Beatriz Bretones Cassoli, Nicolas Jourdan, Joachim Metternich

State-of-the-art predictive quality (PQ) applications use machine learning and deep learning methods to learn patterns and classify a product's quality. Models typically estimate quality control labels based on process data, such as machine control and sensor data, substituting time-consuming manual quality checks. …

Knowledge Graphs For Data And Knowledge Management In Cyber-Physical Production Systems

Beatriz Bretones Cassoli, Nicolas Jourdan, Joachim Metternich

Abstract Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and …

Software Engineering and AI for Data Quality in Cyber- Physical Systems - SEA4DQ'21 Workshop Report

Phu H. Nguyen, Sagar Sen, Nicolas Jourdan, Beatriz Bretones Cassoli et autres

Cyber-physical systems (CPS) have been developed in many industrial sectors and application domains in which the quality requirements of data acquired are a common factor. Data quality in CPS can deteriorate because of several factors such as sensor faults and failures due …

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Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review

Beatriz Bretones Cassoli, Nicolas Jourdan, Phu H. Nguyen, Sagar Sen et autres

Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS …

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Künstliche Intelligenz zur Umsetzung von Industrie 4.0 im Mittelstand : Expertise des Forschungsbeirats der Plattform Industrie 4.0

Joachim Metternich, Tobias Biegel, Beatriz Bretones Cassoli, Felix Hoffmann et autres

Der Forschungsbeirat der Plattform Industrie 4.0 berät als strategisches und unabhängiges Gremium die Plattform Industrie 4.0, ihre Arbeitsgruppen und die beteiligten Bundesministerien, insbesondere das Bundesministerium für Bildung und Forschung (BMBF). Als Sensor von Entwicklungsströmungen beobachtet und bewertet der Forschungsbeirat die Leistungsprofilentwicklung von …