Abstract
The surface roughness is one of the major parameters for determining the level of machining quality. The cutting parameters and conditions have great importance to achieve the desired values during the turning process. In the present work, a new approach was considered for modelling the effect of various turning process parameters and conditions on surface roughness. The experimental studies about the surface roughness after the turning process documented in the literature were collected and compiled into a model based on a genetic learning algorithm. As input parameters for modeling the work piece alloy type, tool type, tool tip radius, tool coating type, cooling conditions, cutting speed, feed rate, and cut depth were used in the study and were comprehensivly compiled.
Kurzfassung
Die Oberflächenrauheit ist einer der wichtigsten Parameter für die Bestimmung der Bearbeitungsqualität. Die Schnittparameter und –bedingungen haben große Bedeutung, um die hierfür geforderten Werte während des Drehprozesses zu erreichen. In der vorliegenden Arbeit wurde ein neuer Ansatz berücksichtigt, um die Auswirkung der verschiedenen Drehprozessparameter und –bedingungen auf die Oberflächenrauheit zu modellieren. Die in der Literatur dokumentierten experimentellen Studien zur Oberflächenrauheit bei Drehprozessen wurden hierzu gesammelt und darauf wurde basierend auf einem genetischen Algorithmus ein Modell erstellt. Als Eingabeparameter für die Modellierungen wurden der Werkstücklegierungstyp, der Werkzeugtyp, der Werkzeugspitzenradius, der Werkzeugbeschichtungstyp, die Abkühlbedingungen, die Schnittgeschwindigkeit, die Zufuhrrate, und die Schnitttiefe herangezogen und umfassend zusammengetragen.
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