Data for: Self-optimizing tool path generation for 5-axis machining processes

Published: 19 April 2018| Version 1 | DOI: 10.17632/smyg6cfwpk.1
Contributors:
Florian Uhlich, Marc-Andre Dittrich, Berend Denkena

Description

Shape deviation measurements and corresponding simulated cutting conditions. Please refer to the corresponding paper for the experimental setup. ----- Feature Description MeasuredDeviation - Actual measured deviation [mm] WidthOfCut - Width of cut determined with process parallel material simuation [mm] DepthOfCut - Width of cut determined with process parallel material simuation [mm] HeightAtTool - Projected distance from probed surface point to tool tip along tool axis determined with process parallel material simuation [mm] MaterialRemovalRate - Material removal ratedetermined with process parallel material simuation [cm^3/min] Feedrate - Feedrate determined with process parallel material simuation [m/min] X,Y,Z - Location of probed point in workpiece coordinates [mm] ----- Pocket A - no compensation: Systematic and Random Point Selection from 9 sample parts Systematic Selection: 128 points upper (5-axis) wall + 61 points lower wall (not accessible points near floor were skipped) Random Selection: 50 points upper wall + 50 points lower wall ----- Pocket B - no compensation: Random Point Selection from 3 sample parts Random Selection: 100 points upper wall + 100 points lower wall ----- Pocket A - with compensation: Systematic and Random Point Selection from 3 sample parts Systematic Approach: 128 points upper (5-Axis) wall + 61 points lower wall (not accessible points near floor were skipped) Random Selection: 100 points upper wall + 100 points lower wall ----- Pocket B - with compensation: Random Point Selection from 3 sample parts Random Selection: 100 points upper wall + 100 points lower wall

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Categories

Computer-Aided Manufacturing, Machine Learning, Milling

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