Abstract
An efficient method based on Taguchi's design of experiment coupled with the grey relational analysis was studied, concentrating on the optimization of process parameters over surface roughness, cutting force and tool wear rate in milling of mild steel. This study consists of three stages: experimental work, single response optimization using Taguchi's S/N value and multi-response optimization using grey relational analysis. In the first stage, the experimental work was carried out using Taguchi's design of experiments. The effects of process parameters (spindle speed, feed rate and depth of cut) on surface roughness, cutting force and tool wear rate were investigated using analysis of variance. In the second stage, Taguchi's signal-to-noise ratio was used to optimize the responses. Finally, multi-response optimization was carried out using grey relational analysis. Additionally, the analysis of variance (ANOVA) was applied to determine the most significant factor for the optimal response for milling of mild steel. From the ANOVA table, the most significant factor is the spindle speed. This proposed method can be an effective approach to enhance the multi-response optimization for milling process.
Kurzfassung
Ein effizientes Verfahren basierend auf Taguchi's Design von Experimenten gekoppelt mit der Grey-Beziehungsanalyse wurde in der diesem Beitrag zugrunde liegenden Studie untersucht, in dem sich auf die Optimierung der Prozessparameter Oberflächenrauheit, Schnittkraft und Werkzeugverschleißrate beim Fräsen eines Kohlenstoffstahls konzentriert wurde. Die Studie bestand aus drei Schritten: Experimentelle Arbeiten, eine einfache Antwortoptimierung mittels des Taguchi S/N-Wertes und einer Mulit-Antwort-Optimierung mittels der Grey-Beziehungsanalyse. Im ersten Schritt wurden die experimentellen Versuche mittels des Taguchi-Designs durchgeführt. Die Effekte der Bearbeitungsparameter (Spindelgeschwindigkeit, Vorschubrate und Schnitttiefe) auf die Oberflächenrauheit, die Schnittkraft und die Werkzeugverschleißrate wurden mittels einer Varianzanalyse untersucht. Im zweiten Schritt wurde Taguchi's Signal-Rausch-Abstand verwendet, um die Antworten zu optimieren. Anschließend wurde eine Multi-Antwort-Optimierung mittels der Grey-Beziehungsanalyse vorgenommen. Zusätzlich wurde eine Varianzanalyse (ANOVA) angewandt, um den signifikantesten Einflussfaktor für die optimale Antwort beim Fräsen von Stahl zu bestimmen. Aus der ANOVA-Tabelle ergibt sich, dass der signifikanteste Parameter die Spindelgeschwindigkeit ist. Das vorgeschlagene Verfahren kann einen effektiven Ansatz darstellen, um die Multi-Antwort-Optimierung für den Fräsprozess voranzubringen.
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