Materials Map

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

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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.

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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.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023Optimization of electro-discharge machining process using rapid tool electrodes via metaheuristic algorithms2citations

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Chart of shared publication
Mahapatra, Siba Sankar
1 / 2 shared
Patterson, Albert E.
1 / 1 shared
Thomas, Joji
1 / 1 shared
Leite, Marco
1 / 2 shared
Chart of publication period
2023

Co-Authors (by relevance)

  • Mahapatra, Siba Sankar
  • Patterson, Albert E.
  • Thomas, Joji
  • Leite, Marco
OrganizationsLocationPeople

article

Optimization of electro-discharge machining process using rapid tool electrodes via metaheuristic algorithms

  • Mahapatra, Siba Sankar
  • Sahu, Anshuman Kumar
  • Patterson, Albert E.
  • Thomas, Joji
  • Leite, Marco
Abstract

<p>The present study explores the application of rapid prototyping (RP) for manufacturing tool electrodes in electro-discharge machining process. The performance of a metallic electrode built via selective laser sintering is compared to solid copper and brass tools during machining of D2 tool steel. In order to efficiently evaluate the influence of several parameters, Taguchi’s L<sub>18</sub> design is adopted to plan the experimental layout. The machining parameters considered in this study are tool type, a categorical parameter and three quantitative parameters such as duty cycle, pulse-on-time and peak current. Multiple performance measures such as material removal rate, tool wear rate, surface roughness and radial over cut of the machined cavity are considered. The multiple performance responses are converted into an equivalent single response known as grey relational grade using grey relational analysis. A nonlinear regression model is developed to relate grey relational grade with process parameters with a coefficient of determination of 0.97. In order to obtain optimal parameter settings satisfying the performance measures, three meta-heuristic algorithms are used due to their computational elegance. The comparative study indicates that particle swarm optimization and simple optimization are effective in delivering the optimized results in substantially less time compared to teaching-learning-based optimization algorithms. It is found that RP tool can perform in a superior manner for simultaneous optimization of multiple responses when compared to copper and brass tools.</p>

Topics
  • impedance spectroscopy
  • surface
  • laser emission spectroscopy
  • copper
  • tool steel
  • sintering
  • laser sintering
  • brass