| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Nouari, Mohammed
Institut de Mathématiques de Marseille
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (51/51 displayed)
- 2022Surface Quality in Dry Machining of CFRP Composite/Ti6Al4V Stack Laminatecitations
- 2022Effect of cryogenic friction conditions on surface qualitycitations
- 2022Effect of additive manufacturing process parameters on the titanium alloy microstructure, properties and surface integritycitations
- 2022Optimization of the milling process for aluminum honeycomb structurescitations
- 2022Surface integrity quantification in machining of aluminum honeycomb structurecitations
- 2022Optimization of the milling process for aluminum honeycomb structures ; Optimisation du procédé de fraisage des alliages d'aluminium nids d'abeillescitations
- 2021Thermomechanical modeling of crystallographic anisotropy effect on machining forces based on crystal plasticity frameworkcitations
- 2021Analysis of friction and cutting parameters when milling honeycomb composite structurescitations
- 2021Anisotropic elastoplastic phase field fracture modeling of 3D printed materialscitations
- 2021Study on the behavior law when milling the material of the Nomex honeycomb corecitations
- 20213D numerical modeling and experimental validation of machining Nomex® honeycomb materialscitations
- 2021Modeling and numerical simulation of the chip formation process when machining Nomexcitations
- 2020The Influence of Machining Conditions on The Milling Operations of Nomex Honeycomb Structure
- 2019Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawingcitations
- 2019Milling diagnosis using artificial intelligence approachescitations
- 2019Micromachining simulation using a crystal plasticity model: ALE and CEL approachescitations
- 2019Milling diagnosis using machine learning approaches
- 2018Investigation on the built-up edge process when dry machining aeronautical aluminum alloyscitations
- 2018Honeycomb Core Milling Diagnosis using Machine Learning in the Industry 4.0 Frameworkcitations
- 2018Milling Diagnosis Using Machine Learning Techniques Toward Industry 4.0
- 2017Prediction of the Cutting Forces and Chip Morphology When Machining the Ti6Al4V Alloy Using a Microstructural Coupled Modelcitations
- 2017Analytical modelling of the ball pin and plastic socket contact in a ball joint
- 2017A 3D FE Modeling of Machining Process of Nomex® Honeycomb Core: Influence of the Cell Structure Behaviour and Specific Tool Geometrycitations
- 2017Failure analysis of carbon fiber reinforced polymer multilayer composites during machining process
- 2016A thermomechanical analysis of sticking-sliding zones at the tool-chip interface in dry high-speed machining of aluminium alloy A2024–T351: A hybrid Analytical-Fe model
- 2016Numerical and experimental investigations of S-Glass/Polyester composite laminate plate under low energy impactcitations
- 2015Effect of the local friction and contact nature on the Built-Up Edge formation process in machining ductile metalscitations
- 2015A predictive hybrid force modeling in turning: application to stainless steel dry machining with a coated groove toolcitations
- 2015An Elastoplastic Constitutive Damage Model to Simulate the Chip Formation Process and Workpiece Subsurface Defects when Machining CFRP Compositescitations
- 2015An Elastoplastic Constitutive Damage Model to Simulate the Chip Formation Process and Workpiece Subsurface Defects when Machining CFRP Compositescitations
- 2015Multi-physics Modelling in Machining OFHC Copper – Coupling of Microstructure-based Flow Stress and Grain Refinement Modelscitations
- 2015Numerical analysis of the interaction between the cutting forces, induced cutting damage, and machining parameters of CFRP compositescitations
- 2015Numerical analysis of the interaction between the cutting forces, induced cutting damage, and machining parameters of CFRP compositescitations
- 2014Experimental and numerical analyses of the tool wear in rough turning of large dimensions components of nuclear power plantscitations
- 20142D and 3D numerical simulations of damage during the formation of successive chips when machining the aeronautical CFRP composites
- 20142D and 3D numerical simulations of damage during the formation of successive chips when machining the aeronautical CFRP composites
- 2014On the Physics of Machining Titanium Alloys: Interactions between Cutting Parameters, Microstructure and Tool Wearcitations
- 2014Modeling of the abrasive tool wear in metal cutting: Influence of the sliding-sticking contact zones
- 2014A new abrasive wear law for the sticking and sliding contacts when machining metallic alloyscitations
- 2014Tribological behaviour and tool wear analyses in rough turning of large-scale parts of nuclear power plants using grooved coated insertcitations
- 2013Experimental Study on Tool Wear when Machining Super Titanium Alloys: Ti6Al4V and Ti-555citations
- 2013Analytical stochastic modeling and experimental investigation on abrasive wear when turning difficult to cut materialscitations
- 2013Experimental investigation on the effect of the material microstructure on tool wear when machining hard titanium alloys: Ti–6Al–4V and Ti-555citations
- 2013Statistical approach for modeling abrasive tool wear and experimental validation when turning the difficult to cut Titanium Alloys Ti6Al4Vcitations
- 2013Quantification of the chip segmentation in metal machining: Application to machining the aeronautical aluminium alloy AA2024-T351 with cemented carbide tools WC-Cocitations
- 2013Modeling of the abrasive tool wear in metal cutting: Influence of the sliding-sticking contact zones
- 2013Experimental and analytical analyses of the cutting process in the deep hole drilling with BTA (Boring Trepanning Association) systemcitations
- 2013Modeling of velocity-dependent chip flow angle and experimental analysis when machining 304L austenitic stainless steel with groove coated-carbide toolscitations
- 2013Analysis of coating performances in machining titanium alloys for aerospace applicationscitations
- 2009Toward a better understanding of tool wear effect through a comparison between experiments and SPH numerical modelling of machining hard materialscitations
- 2009Toward a better understanding of tool wear effect through a comparison between experiments and SPH numerical modelling of machining hard materialscitations
Places of action
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conferencepaper
Milling diagnosis using machine learning approaches
Abstract
The Industry 4.0 framework needs new intelligent approaches. Thus, the manufacturing industries more and more pay close attention to artificial intelligence (AI). For example, smart monitoring and diagnosis, real time evaluation and optimization of the whole production and raw materials management can be improved by using machine learning and big data tools. An accurate milling process implies a high quality of the obtained material surface (roughness, flatness). With the involvement of AI-based algorithms, milling process is expected to be more accurate during complex operations.In this work, a smart milling diagnosis has been developed for composite sandwich structures based on honey-comb core. The use of such material has grown considerably in recent years, especially in the aeronautic, aerospace, sporting and automotive industries. But the precise milling of such material presents many difficulties. The objective of this work is to develop a data-driven industrial surface quality diagnosis for the milling of honey-comb material, by using supervised machine learning methods. Therefore, cutting forces and workpiece material vibrations are online measured in order to predict the resulting surface flatness.The workpiece material studied in this investigation is Nomex® honeycomb cores with thin cell walls. The Nomex® honeycomb machining presents several defects related to its composite nature (uncut fiber, tearing of the walls), the cutting conditions and to the alveolar geometry of the structure which causes vibration on the different components of the cutting effort.Given the low level of cutting forces, the quality of the obtained machined surface allows to establish criteria for determining the machinability of the honeycomb structures. Nearly 40 features are calculated in time domain and frequency domain from the raw signal in steady state behavior (transient zones are not taken into account). The features are then normalized. The input parameters for each experiment are: the tool rotation speed, the cutting speed and the depth of cut. It is then necessary to make a dimensional reduction of that feature table in order to avoid overfitting and to reduce the computing time of the learning algorithm.In this work, several classification algorithms have been implemented such as : k-nearest neighbor (kNN), Decision trees (DT), Support Vector Machine (SVM). The different supervised learning algorithms have been implemented and compared. Each AI-based model has been applied to a set of features. From the prediction results, SVM algorithm seems to be the most efficient algorithm in this application.