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Adaptive neuro-fuzzy and regression models for predicting microhardness and electrical conductivity of solid-state recycled EN AW 6082

  • Jure KroloEmail author
  • Branimir Lela
  • Zrinka Švagelj
  • Sonja Jozić
ORIGINAL ARTICLE
  • 28 Downloads

Abstract

In the last few years, there is a demand for developing new technologies in order to increase scrap reuse potential and CO2 emission savings. In this paper, aluminum was recycled from chips obtained by machining without any remelting in order to reduce environmental pollution and to increase material yield during the process. This process is called solid-state recycling (SSR) or direct recycling. SSR process consists of chips cleaning, cold pre-compaction, and hot direct extrusion followed by equal channel angular pressing (ECAP) at different temperatures. Influence of direct extrusion temperature, ECAP temperature, and number of ECAP passes on electrical conductivity and microhardness of the recycled EN AW 6082 aluminum chips was investigated. Microhardness and electrical conductivity of the recycled samples were comparable with commercially produced EN AW 6082. Experiments were planned utilizing design of experiments approach. Both adaptive neuro-fuzzy interference system (ANFIS) and regression models were developed and compared to describe the influence of input SSR process parameters on electrical conductivity and microhardness. Density and metallographic analysis of the recycled samples were also performed.

Keywords

Solid-state recycling Aluminum Electrical conductivity Microhardness Regression analysis 

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Notes

Compliance with ethical standards

Conflict of interest

None.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Mechanical Engineering and Naval ArchitectureUniversity of SplitSplitCroatia
  2. 2.Faculty of Mechanical Engineering and Naval ArchitectureUniversity of ZagrebZagrebCroatia

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