Materials Map

Discover the materials research landscape. Find experts, partners, networks.

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

×

Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Filho, A. R. S.

  • Google
  • 1
  • 3
  • 4

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2012Multivariate optimization of the cutting parameters when turning slender components4citations

Places of action

Chart of shared publication
Abrao, A. M.
1 / 1 shared
Paiva, A. P.
1 / 2 shared
Ferreira, João
1 / 1 shared
Chart of publication period
2012

Co-Authors (by relevance)

  • Abrao, A. M.
  • Paiva, A. P.
  • Ferreira, João
OrganizationsLocationPeople

article

Multivariate optimization of the cutting parameters when turning slender components

  • Filho, A. R. S.
  • Abrao, A. M.
  • Paiva, A. P.
  • Ferreira, João
Abstract

<jats:p>The geometric features of the work piece and the cutting parameters considerably affect the quality of a finished part subjected to any machining operation owing to the imposed elastic and plastic deformations, especially when slender components are produced. This work is focused on the influence of the work piece slenderness ratio and cutting parameters on the quality of the machined part, assessed in terms of surface roughness and both geometric (run-out) and dimensional (diameter) deviations. Turning tests with coated tungsten carbide tools were performed using AISI 1045 medium carbon steel as work material. Differently from the published literature, a statistical analysis based on the multivariate one-way analysis of variance (MANOVA) was applied to the data obtained using a Box-Behnken experimental design. In order to identify the combination of parameters (slenderness ratio, cutting speed, feed rate and depth of cut) levels which simultaneously optimize the responses of interest (surface roughness, run-out and diameter deviation), a multivariate optimization method based on principal component analysis (PCA) and generalized reduced gradient (GRG) was employed.</jats:p>

Topics
  • impedance spectroscopy
  • surface
  • polymer
  • Carbon
  • carbide
  • steel
  • tungsten