Preprint Article Version 1 This version is not peer-reviewed

ANN Laser Hardening Quality Modeling Using Geometrical and Punctual Characterizing Approaches

Version 1 : Received: 25 April 2018 / Approved: 27 April 2018 / Online: 27 April 2018 (05:47:56 CEST)

A peer-reviewed article of this Preprint also exists.

Maamri, I.; Barka, N.; El Ouafi, A. ANN Laser Hardening Quality Modeling Using Geometrical and Punctual Characterizing Approaches. Coatings 2018, 8, 226. Maamri, I.; Barka, N.; El Ouafi, A. ANN Laser Hardening Quality Modeling Using Geometrical and Punctual Characterizing Approaches. Coatings 2018, 8, 226.

Journal reference: Coatings 2018, 8, 226
DOI: 10.3390/coatings8060226

Abstract

Maximum hardness and hardened depth are the responses of interest in relation to the laser hardening process. These values define heat treatment quality and have a direct impact on mechanical performance. This paper aims to develop models capable of predicting the shape of the hardness profile depending on laser process parameters for controlling laser hardening quality (LHQ), or rather the response values. An experimental study was conducted to highlight hardened profile sensitivity to process input parameters such as laser power (PL), beam scanning speed (VS) and initial hardness in the core (HC). LHQ modeling was conducted by modeling attributes extracted from the hardness profile curve using two effective techniques based on the punctual and geometrical approaches. The process parameters with the most influence on the responses were laser power, beam velocity and initial hardness in the core. The obtained results demonstrate that the geometrical approach is more accurate and credible than the punctual approach according to performance assessment criteria.

Subject Areas

laser hardening; geometrical approach; punctual approach; Taguchi method; artificial neural network

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