Multi-Scale Surface Roughness Optimization Through Genetic Algorithms

Paolo Cinat,Marco Paggi,Giorgio Gnecco

doi:10.3389/fmech.2020.00029

Paolo Cinat, Marco Paggi + Show 1 more

Open Access

https://doi.org/10.3389/fmech.2020.00029

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Abstract

Artificial intelligence is changing perspectives of industries about manufacturing of components, introducing emerging techniques such as additive manufacturing technologies. These techniques can be exploited to manufacture not only precision mechanical components, but also interfaces. In this context, we investigate the use of artificial intelligence and in particular genetic algorithms to identify optimal multi-scale roughness features to design prototype surfaces achieving a target contact mechanics response. Exploiting an analogy with biology, the features of roughness at a given length scale are described through model profiles named chromosomes. In the present work, the mathematical description of chromosomes is firstly provided, then three genetic algorithms are proposed to superimpose and combine them in order to identify optimal roughness features. The three methods are compared, discussing the topological and spectral features of roughness obtained in each case.

Highlights

It is well-established in the literature that surface topology and texture are important for enhancing the tribological behavior of contacts
Artificial intelligence based on genetic algorithms (Zain et al, 2008) and artificial neural networks (Moghri et al, 2014) have been recently exploited to control milling operations and surface roughness manufacturing by material removal
The threshold pressure used for both the Genome Cross-Over (GCO) and Chromosomes Cross-Over (CCO) is set equal to p = 0.08 [1/mm]

Summary

Introduction

It is well-established in the literature that surface topology and texture are important for enhancing the tribological behavior of contacts. Artificial intelligence based on genetic algorithms (Zain et al, 2008) and artificial neural networks (Moghri et al, 2014) have been recently exploited to control milling operations and surface roughness manufacturing by material removal. As another strategy, additive manufacturing (Brettel et al, 2014) is opening new perspectives to produce surfaces with specified roughness (see e.g., Farina et al, 2016; Ko et al, 2019; Wüst et al, 2020)

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