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Pregled bibliografske jedinice broj: 973631

Optimization of Welding Parameters in Robotics Hard-surfacing Process of AISI 316 Substrate with Durmat OA Using Genetics Algorithm


Salopek, Goran; Dunđer, Marko; Samardžić, Ivan
Optimization of Welding Parameters in Robotics Hard-surfacing Process of AISI 316 Substrate with Durmat OA Using Genetics Algorithm // IIW SOUTH-EAST EUROPEAN WELDING CONGRESS
Beograd, Srbija, 2018. str. 151-157 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Optimization of Welding Parameters in Robotics Hard-surfacing Process of AISI 316 Substrate with Durmat OA Using Genetics Algorithm

Autori
Salopek, Goran ; Dunđer, Marko ; Samardžić, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IIW SOUTH-EAST EUROPEAN WELDING CONGRESS / - , 2018, 151-157

Skup
THE 4 TH IIW SOUTH-EAST EUROPEAN WELDING CONGRESS

Mjesto i datum
Beograd, Srbija, 10-13.10.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Hardfacing, Genetic Algorithm, Robotic arc welding, wear resistance, 3D scanning, Factorial Design Approach

Sažetak
Hardfacing or hard surfacing may be defined as the application of a hard, wear resistant alloy to the surface of a softer metal to restore it dimensionally and reduce wear caused by abrasion, impact, erosion and heat. The benefits also include the minimization if downtime needed to replace worn part, reduce spare parts inventory and saves money. This study investigates welding parameters for semi- automated hard surfacing process of AISI 316 stainless steel substrate with open arc iron- based tubular wire filled with fused tungsten carbide. The goal was to find near optimal welding voltage, torch speed, wire feed rate, gas flow rate and torch angle of robotics welding machine for deposition process. Because, welding wire filed with tungsten carbide is expensive, our goal was to find near optimal welding parameters for deposition of predefined thickness height above substrate. In order to achieve the above objective, a set of mathematical models has been developed for the prediction of height of deposited material. Genetic Algorithm computational model was used for the optimization of welding parameters to achieve desired thickness of deposited material.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Ustanove
Strojarski fakultet, Slavonski Brod,
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