German Terrazas, Giovanna Martínez-Arellano, Panorios Benardos, Svetan Ratchev

J. Manuf. Mater. Process. 2018, 2(4), 72; https://doi.org/10.3390/jmmp2040072

 

Abstract

The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%.

Zoltan Dombovaria, Jokin Munoa, Rachel Kuskec, Gabor Stepana

IDEKO, Elgoibar and BME, Budapest

Paper at the 8th CIRP Conference on High Performance Cutting

A. Gouarir, S. Ratchev, G. Martinez, G. Terrazas, P. Benardos

University of Nottingham, Nottingham, United Kingdom

Paper at the 8th CIRP Conference on High Performance Cutting

MGEP, Mondragon, Spain

Paper at the 8th CIRP Conference on High Performance Cutting

MGEP, Mondragon, Spain

Proceedings of the International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6th-8th, 2017

ISBN 978-3-319-67180-2

Rosa Basagoiti, Miren Illarramendi, Krystian Adam Kuzniarek, Xavier Beudaert, Michael Benguigui

MGEP, Mondragon, Spain

 

Abstract

Energy efficiency is a very important issue for a sustainable manufacturing. Machining of parts is a very time consuming process and it is directly linked with the energy consumption and its efciency. The energy consumption depends on some process parameters such as spindle speed and feed rate. In cloud manufacturing environments, the deployed services can use cloud computing resources and parallelization power in order to optimize the cutting coefficients for the different machining operations inside a machining task that better t the user requests. First trials for the energy consumption reduction for part machining while saving Time and improving Material Removed Rate(MRR) is the aim of this work. For that, Spindle speed and Feed rate input parameters have been analyzed from different simulations and multiobjective optimization approach has been considered. In the presented work, a simple use case has been performed and its results conrmed the correctness of the approach.

 

Proceedings in II Jornadas de Computación Empotrada y Reconfigurable (JCER, 2017) 

ISBN-13: 978-84-697-4835-0

 

Nicolas Ferry, Franck Chauvel, Hui Song, Alessandro Rossini, Maksym Lushpenko, Arnor Solberg

SINTEF, Oslo, Norway

Article on ACM Transaction on Internet Technologies (TOIT), special issue on Emerging Software Technologies for Internet-Based Systems: Internetware and DevOps

Volume 18 Issue 2, January 2018, Article No. 16, January 2018

DOI 10.1145/3125621

ISSN: 1533-5399 EISSN: 1557-6051

 

The market of cloud computing encompasses an ever-growing number of cloud providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. The heterogeneity of these solutions hinders the proper exploitation of cloud computing since it prevents interoperability and promotes vendor lock-in, which increases the complexity of executing and managing multi-cloud applications (i.e., applications that can be deployed across multiple cloud infrastructures and platforms). Providers of multi-cloud applications seek to exploit the peculiarities of each cloud solution and to combine the delivery models of IaaS and PaaS in order to optimise performance, availability, and cost. In this paper, we show how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing: (i) a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for enacting the provisioning, deployment, and adaptation of these applications.

Nicolas Ferry, German Terrazas, Per Kalweit, Arnor Solberg, Svetan Ratchev, Dirk Weinelt

Sintef, University of Nottingham, Tagueri

proceedings of the IEEE 15th International Confetrence of Industrial Informatics (INDIN), pages 263-270

DOI 10.1109/INDIN.2017.8104782

2378-363X

 

Presentation at the Conference INDIN2017 by partners Sintef, University of Nottingham and Tagueri.

Hereafter the abstract:

"Industry 4.0 proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories.
With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimize the utilization of the machines as well as their operation and maintenance. However, analyzing the vast amount of data generated is resource demanding both in term of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data platform for the management of machine generated data in the cloud. It brings together standard open source technologies which can be adapted to and deployed on different cloud infrastructures, hence reducing costs, minimising deployment difficulty and providing ondemand access to a virtually infinite set of computing, storage and network resources."

Nicolas Ferry, Franck Chauvel, Hui Song and Arnor Solberg
SINTEF, Oslo, Norway

In Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017), Vol. 1 - 978-989-758-210-3, pages 503-508

DOI 10.5220/0006225905030508

ISBN: 978-989-758-210-3

Hui Song, Nicolas Ferry, Jakob Høgenes and Arnor Solberg

SINTEF, Oslo, Norway

In Proceedings of the 5th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2017), Vol. 1 - 978-989-758-210-3, pages 523-528

DOI 10.5220/0006266505230528

ISBN: 978-989-758-210-3