| 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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Myszka, Dawid
Warsaw University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (23/23 displayed)
- 2023Abrasive Wear Resistance of Ultrafine Ausferritic Ductile Iron Intended for the Manufacture of Gears for Mining Machinerycitations
- 2023Supported by 2D and 3D Imaging Methods Investigation of the Influence of Fiber Orientation on the Mechanical Properties of the Composites Reinforced with Fibers in a Polymer Matrixcitations
- 2023Highly Accurate Structural Analysis of Austempered Ductile Iron Using EBSD Techniquecitations
- 2023Numerical and Experimental Analysis of Strength Loss of 1.2709 Maraging Steel Produced by Selective Laser Melting (SLM) under Thermo-Mechanical Fatigue Conditionscitations
- 2022The Microstructure of Cast Steel Subjected to Austempering and B-Q&P Heat Treatmentcitations
- 2021Influence of Tungsten on the Structure and Properties of Ductile Iron Containing 0.8% Cucitations
- 2020Influence of rare earths metals (Rem) on the structure and selected properties of grey cast iron
- 2019Transformation kinetics of austempered dductile iron: Dilatometric experiments and model parameter evaluationcitations
- 2018High Strain Rate Dynamic Deformation of ADI
- 2018High Strain Rate Dynamic Deformation of ADIcitations
- 2018Comparison of Some Properties of Selected Co-Cr Alloys Used in Dental Prosthetics
- 2018Determination of susceptibility of cast iron with a predetermined chemical composition to shape properties and microstructure through bainitic transformation
- 2018Influence of pre-heat treatment on mechanical properties of austempered ductile cast iron
- 2018Evaluation of Mechanical Properties of Al7050-cenosphere Metal Matrix Composites
- 2018The effect of addition of germanium to the surface phenomena in silver alloys
- 2017The comparative study of the microstructure and phase composition of nanoausferritic ductile iron alloy using SEM, TEM, magnetometer and X-ray diffraction methodscitations
- 2014Preliminary evaluation of the applicability of F, V and aesignals in diagnosis of ADI machining processcitations
- 2014Influence of heat treatment conditions on microstructure and mechanical properties of austempered ductile iron after dynamic deformation test
- 2012Mikrostructure transformations in austempered ductile iron during deformation by dynamic hardness test
- 2012New possibilities of shaping the surface properties in austempered ductile iron castings
- 2011Advanced metrology of surface defects measurement for aluminum die casting
- 2009Detonation sprayed coatings Al 2O 3-TiO 2 and WC/Co on adi investment castings
- 2007Austenite-Martensite transformation in austempered ductile iron
Places of action
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article
Preliminary evaluation of the applicability of F, V and aesignals in diagnosis of ADI machining process
Abstract
<p>In this study, a preliminary evaluation was made of the applicability ofthe signalsof the cutting forces, vibration and acoustic emission in diagnosis of the hardness and microstructure of ausferritic ductile iron and tool edge wear rate during its machining. Tests were performed on pearlitic-ferritic ductile iron and on three types of ausferritic ductile iron obtained by austempering at 400, 370 and 320°C for 180 minutes. Signals of the cutting forces (F), vibration (V) and acoustic emission (AE) were registered while milling each type of the cast iron with a milling cutter at different degrees of wear. Based on individual signals from all the sensors, numerous measures were determined such as e.g. the average or maximum signal value. It was found that different measures from all the sensors tested depended on the microstructure and hardness of the examined material, and on the tool condition. Knowing hardness of the material and the cutting tool edge condition, it is possible to determine the structure of the material .Simultaneous diagnosis of microstructure, hardness, and the tool condition is probably feasible, but it would require the application of a diagnostic strategy based on the integration of numerous measures, e.g. using neural networks.</p>