Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach
German Terrazas, Giovanna MartÃnez-Arellano, Panorios Benardos, Svetan Ratchev
J. Manuf. Mater. Process. 2018, 2(4), 72; https://doi.org/10.3390/jmmp2040072
Â
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%.
Milling stability for slowly varying parameters
Zoltan Dombovaria, Jokin Munoa, Rachel Kuskec, Gabor Stepana
Â
Abstract
In order to predict the quality and the stability properties of milling processes, the relevant dynamics reduced to the cutting edges needs to be known. However, the dynamics varies through the workspace along the tool path during a given machining operation. This is the case for large heavy duty milling operations, where the main source of the relevant dynamics is related to the otherwise slowly varying machine structure rather than to the fairly steady milling tool dynamics. The effect of slowly varying dynamic parameters is presented for milling stability when the cutting process takes place in a region of the work space where the steady-state cutting would change from stable to unstable. After the separation of the slow and fast time scales, the governing non-autonomous delay differential equation is frozen in slow-time in order to determine the time- periodic stationary cutting solution of the milling operation for different parameters. The loss of stability is predicted from the correction to the time-periodic frozen time solution, for which we obtained non-autonomous equation for the accumulated growth over the slow-time. The growth shows loss of stability with a shift on the parameters compared to the static parameter solution.
Â
IDEKO, Elgoibar and BME, Budapest
Paper at the 8th CIRP Conference on High Performance Cutting
In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis
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
An optimization methodology for material databases to improve cutting force predictions when milling martensitic stainless steel JETHETE-M152
MGEP, Mondragon, Spain
Paper at the 8th CIRP Conference on High Performance Cutting
Software Defined Networking Opportunities for Intelligent Security Enhancement of Industrial Control Systems
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
Optimizing cutting parameters for energy efficient CNC milling
Rosa Basagoiti, Miren Illarramendi, Krystian Adam Kuzniarek, Xavier Beudaert, Michael Benguigui
MGEP, Mondragon, Spain
Â
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 efciency. 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 conrmed the correctness of the approach.
Â
Proceedings in II Jornadas de Computación Empotrada y Reconfigurable (JCER, 2017)Â
ISBN-13: 978-84-697-4835-0
Â
CloudMF: Applying MDE to Tame the Complexity of Managing Multi-Cloud Applications
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: 1533-5399Â EISSN: 1557-6051
Â
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.
Towards a Big Data Platform for Managing Machine Generated Data in the Cloud
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
DOIÂ 10.1109/INDIN.2017.8104782
2378-363X
Â
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."
Towards Meta-adaptation of Dynamic Adaptive Systems with Models@Runtime
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:Â 978-989-758-210-3
Model-driven Engineering for the Configuration and Deployment of Data Processing Applications
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
DOIÂ 10.5220/0006266505230528
ISBN:Â 978-989-758-210-3