Abstract
In the present study, Grey based fuzzy algorithm was used for the optimization of complex multiple performance characteristics of the ball burnishing process. Experiments have been planned according to Taguchi's L16 orthogonal design matrix. Burnishing force, number of passes, feed rate and burnishing speed were selected as input parameters, whereas surface roughness and microhardness were selected as output responses. Using Grey relation analysis (GRA), Grey relational coefficient (GRC) and Grey relation grade (GRG) were obtained. Then, Grey-based fuzzy algorithm was applied to obtain Grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) was carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, a confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the feasibility of the Grey-based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.
Kurzfassung
In der diesem Beitrag zugrunde liegenden Studie wurde ein Grey-basierter Fuzzy-Algorithmus eingesetzt, um die komplexen Performanzeigenschaften des Kugelpolierprozesses zu optimieren. Die Experimente wurden mittels der Taguchi-Methode entsprechend mit Hilfe von einer L16 orthogonaler Designmatrix geplant. Die Polierkraft, die Anzahl der Durchgänge, die Vorschubrate und die Poliergeschwindigkeit wurden als Inputparameter ausgewählt, wobei die Oberflächenhärte und die Mikrohärte als Output-Antworten ausgesucht wurden. Unter Verwendung der Grey-Relations-Analyse (Grey Relation Analysis – GRA), wurden der Grey Relationskoeffizient (Grey Relational Coefficient – GRC) und der Grey Relationsgrad (Grey Relation Grade – GRG) ermittelt. Daraufhin wurde der Grey-basierte Fuzzy-Algorithmus angewendet, um den Grey Fuzzy Reasoning Grade (GFRG) zu ermitteln. Es wurde eine Varianzanalyse (ANOVA) durchgeführt, um die Signifikanz und den Beitrag der Parameter hinsichtlich der vielfachen Performanzcharakteristik herauszufinden. Schließlich wurde ein Bestätigungstest auf dem optimalen GFRG-Niveau durchgeführt, um die Ergebnisse zu validieren. Die Ergebnisse zeigen auch, dass die Anwendung des Grey-basierten Fuzzy-Algorithmus für eine kontinuierliche Produktoptimierung in komplexen Produktionsprozessen möglich ist.
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