Pregled bibliografske jedinice broj: 973631
Optimization of Welding Parameters in Robotics Hard-surfacing Process of AISI 316 Substrate with Durmat OA Using Genetics Algorithm
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)
CROSBI ID: 973631 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
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
4th IIW South-East European Welding Congress
Mjesto i datum
Beograd, Srbija, 10.10.2018. - 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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