Abstract
Friction welding is a type of solid state welding which plays an important role in joining metal surfaces with the help of frictional heat accompanied with high force. Optimization of process parameters of friction welding is important for all types of materials. Optimization of input parameters of friction welding also plays a very significant role in determining the quality of a weld joint. Purpose of this study is to optimize the welding process parameters in friction welding of AISI 904L super austenitic stainless steel by using regression analysis and evolutionary algorithms. This study is to determine optimimum welding process parameters of friction welding with the help of genetic algorithm (GA) and simulated annealing (SA). Also, it explains how to obtain near optimum welding conditions in a wide range by conducting a relatively small number of experiments. Results of these evolutionary computational techniques were compared with experimental results. Finally, an optimization parameter is obtained for a maximum fatigue life and a minimum welding time.
Kurzfassung
Reibschweißen ist eine Schweißart im festen Zustand, die eine große Bedeutung für die Verbindung von Metalloberflächen mittels Reibwärme begleitet von großen Kräften hat. Die Optimierung der Prozessparameter beim Reibschweißen ist für alle Materialarten wichtig. Die Optimierung der Eingangsparameter beim Reibschweißen ist maßgebend für die Bestimmung der Schweißnahtqualität. Der Zweck der diesem Beitrag zugrunde liegenden Studie besteht darin, die Prozessparameter beim Reibschweißen eines AISI 904L superaustenitischen Stahls mittels Regressionsanalyse und evolutionären Algorithmen zu optimieren. Diese Studie hat das Ziel, die optimalen Parameter mit Hilfe eines genetischen Algorithmus' (GA) und simuliertem Annealing (SA) zu bestimmen. Außerdem beschreibt sie, wie nahezu optimale Schweißbedingungen in einem großen Suchbereich unter Durchführung einer relativ kleinen Zahl von Experimenten erhalten werden. Die Ergebnisse dieser evolutionären Algorithmustechniken wurden mit experimentellen Resultaten verglichen. Schließlich wurde ein Optimierungsparameter für eine maximale Lebensdauer unter Ermüdungsbeanspruchung und einer minimalen Schweißzeit gefunden.
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