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

Mandolfino, Chiara

  • Google
  • 16
  • 12
  • 196

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (16/16 displayed)

  • 2024Adhesive bonding of glass-fibre thermoplastic composite: process optimisation and sustainability analysis using LCA methodology5citations
  • 2023Energy absorption properties of a 3D-printed lattice-core foam composite under compressive and low-velocity impact loading9citations
  • 2022A Response Surface Methodology Approach to Improve Adhesive Bonding of Pulsed Laser Treated CFRP Composites8citations
  • 2021Comparative evaluation of the effect of the substrate thickness and inherent process defects on the static and fatigue performance of FSW and adhesive-bonded overlap-joints in an AA6016 alloy11citations
  • 2019Influence of Adhesive in FSW: Investigation on Fatigue Behavior of Welded, Weld-Bonded,and Adhesive-Bonded Joints in Aluminum AA 6082 T614citations
  • 2018Laser surface texturing of polypropylene to increase adhesive bonding3citations
  • 2017Comparison between FSW and bonded lap joints - A preliminary investigation5citations
  • 2016Experimental investigation of fiberglass sandwich composite bending behaviour after severe aging conditioncitations
  • 2015Friction stir welding between extrusions and laminates3citations
  • 2015Effect of laser and plasma surface cleaning on mechanical properties of adhesive bonded joints81citations
  • 2014Ti 6Al-4V FSW weldability: mechanical characterization and fatigue life analysis2citations
  • 2014Environmental effects on methacrylate adhesive5citations
  • 2014Investigation on gas metal arc weldability of a high strength tool steel1citations
  • 2014Mechanical Behaviour of Inconel 718 Thin-Walled Laser Welded Components for Aircraft Engines17citations
  • 2014Cold Plasma Pretreatment of Carbon Fibre Composite Substrates to Improve Adhesive Bonding Performance9citations
  • 2014Effect of cold plasma treatment on surface roughness and bonding strength of polymeric substrates23citations

Places of action

Chart of shared publication
Pizzorni, Marco
6 / 11 shared
Cassettari, Lucia
2 / 2 shared
Lertora, Enrico
16 / 21 shared
Saccaro, Stefano
1 / 1 shared
Fratini, Livan
2 / 70 shared
Campanella, Davide
2 / 26 shared
Buffa, Gianluca
2 / 53 shared
Gambaro, Carla
12 / 19 shared
Pedemonte, Matteo
2 / 2 shared
Genna, Silvio
1 / 24 shared
Leone, Claudio
1 / 49 shared
Davini, L.
1 / 1 shared
Chart of publication period
2024
2023
2022
2021
2019
2018
2017
2016
2015
2014

Co-Authors (by relevance)

  • Pizzorni, Marco
  • Cassettari, Lucia
  • Lertora, Enrico
  • Saccaro, Stefano
  • Fratini, Livan
  • Campanella, Davide
  • Buffa, Gianluca
  • Gambaro, Carla
  • Pedemonte, Matteo
  • Genna, Silvio
  • Leone, Claudio
  • Davini, L.
OrganizationsLocationPeople

article

A Response Surface Methodology Approach to Improve Adhesive Bonding of Pulsed Laser Treated CFRP Composites

  • Pizzorni, Marco
  • Mandolfino, Chiara
  • Cassettari, Lucia
  • Lertora, Enrico
  • Saccaro, Stefano
Abstract

<jats:p>In this work, a response surface-designed experiment approach was used to determine the optimal settings of laser treatment as a method of surface preparation for CFRP prior to bonding. A nanosecond pulsed Ytterbium-doped-fiber laser source was used in combination with a scanning system. A Face-centered Central Composite Design was used to model the tensile shear strength (TSS) of adhesive bonded joints and investigate the effects of varying three parameters, namely, power, pitch, and lateral overlap. The analysis was carried out considering different focal distances. For each set of joints, shear strength values were modeled using Response Surface Methodology (RSM) to identify the set-up parameters that gave the best performance, determining any equivalent conditions from a statistical point of view. The regression models also allow the prediction of the behavior of the joints for not experimentally tested parameter settings, within the operating domain of investigation. This aspect is particularly important in consideration of the process optimization of the manufacturing cycle since it allows the maximization of joint efficiency by limiting the energy consumption for treatment.</jats:p>

Topics
  • impedance spectroscopy
  • surface
  • experiment
  • strength
  • composite
  • Ytterbium