Optimization of Machining Parameters for improved Surface Integrity of AISI H13 Tool Steel

DSpace Repository

Show simple item record

dc.contributor.author OUTEIRO, José
ensam.hal.laboratories
  127742 Laboratoire Bourguignon des Matériaux et Procédés [LaBoMaP]
dc.date.accessioned 2013-02-14T08:50:47Z
dc.date.available 2013-02-14T08:50:47Z
dc.date.issued 2012-10-17
dc.date.submitted 2013-02-13T08:44:56Z
dc.identifier.citation MUGV 2012 Conference
dc.identifier.citation Optimization of Machining Parameters for Improved Surface Integrity of AISI H13 Tool Steel, 1-10
dc.identifier.uri http://hdl.handle.net/10985/6783
dc.description.abstract The surface integrity plays a very important rule in this functional performance, being dependent of a large number of machining parameters. The major concern of the industry is to know which combination of machining parameters provides a better surface integrity of the machined components.
AISI H13 tool steel has been applied widely to produce many different types of hot working dies due to its excellent mechanical properties, such as: good resistance to thermal softening, high hardenability, high strength and high toughness. Traditionally, the surface roughness is considered to be the principal parameter to assess the surface integrity of the machined component. However, residual stress becomes an important parameter because it may increase the mould/die lifetime and their ability to withstand more severe thermal and mechanical cyclic loadings (fatigue) during its service. Therefore, significant improvements in the quality of the mould/die can be achieved with the control of the residual stresses induced during its manufacturing.
This paper examines the residual stresses induced by dry turning of AISI H13 tool steel. Residual stress was evaluated experimentally in function of the tool geometry, cutting speed, feed and depth of cut. The DOE method developed by G. Taguchi was used to reduce the number of experiments. An modelling and optimization procedure based in Artificial Neural Network (ANN) and a Genetic Algorithm (GA) was developed and applied to modelling the residual stresses and to identify the optimum combination of cutting parameters, which induces low tensile or compressive residual stresses, which contributes to a better surface integrity of machined components.
en_US
dc.language.iso en en_US
dc.publisher ENISE-CETIM en_US
dc.rights Post-print en_US
dc.subject Surface integrity en_US
dc.subject Residual Stresses en_US
dc.subject Surface Roughness en_US
dc.subject Modelling en_US
dc.subject Optimization en_US
dc.title Optimization of Machining Parameters for improved Surface Integrity of AISI H13 Tool Steel en_US
dc.title.alternative Intercut 2012 en_US
ensam.hal.id hal-00788241 *
ensam.hal.status accept *
dc.typdoc Communications avec actes en_US
dc.localisation Centre de Cluny en_US
dc.subject.hal Sciences de l'ingénieur: Mécanique: Génie mécanique en_US
ensam.workflow.submissionConsumer updateFiles *
ensam.audience Non spécifiée en_US
ensam.conference.title Machines et Usinage à Grande Vitesse (MUGV) 2012 en_US
ensam.conference.date 2012-10-17
ensam.country France en_US
ensam.title.proceeding Proceeding of the Conference Machines et Usinage à Grande Vitesse (MUGV) 2012 en_US
ensam.page 1-10 en_US

Files in this item

 

This item appears in the following Collection(s)

Show simple item record

Search


Number of documents in SAM

  • 2890 references in SAM

Newsletter

My Account

Reporting Suite

Help