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Influence of Process Parameters and Electrode Geometry on Feature Micro-Accuracy in Electro Discharge Machining of Tool Steel

机译：工艺参数和电极几何形状对工具钢放电加工特征微观精度的影响

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摘要

Agile manufacturing capabilities require fast responses in workshops to cope with different demands in product geometrical features and qualities in fabricating moulds. Therefore, direct machining of complex mould features using simple shape machined electrodes with the electro-discharge machining (EDM) process, empowers the flexibility and the capacity of the enterprises and dramatically reduces lead times resulting in much more efficient production processes. In the case of machining geometrical features with characteristic dimensions in the order of few millimeters, the EDM process requires further understanding. This article focuses on investigating the influence of EDM parameters and electrode geometry on feature micro-accuracy in tool steel for mould fabrication purposes. A set of designed experiments with varying EDM process parameters such as pulsed current, open voltage, pulse time, and pulse pause time is carried out in H13 steel using differently shaped copper electrodes. Microdimensional and geometrical accuracies are the measures of response. Artificial neural network and regression models have been constructed to capture the influence of the process parameters on the geometrical feature quality such as flatness, depth, slope, width, and dimension variation between the entrance and the exit (DVEE).
机译：敏捷制造能力要求车间内快速响应，以应对产品几何特征和制造模具质量方面的不同要求。因此，使用简单的形状加工的电极通过放电加工（EDM）工艺直接加工复杂的模具特征，可增强企业的灵活性和产能，并显着减少交货时间，从而提高生产效率。在加工具有几毫米量级特征尺寸的几何特征时，需要进一步了解EDM工艺。本文重点研究用于模具制造目的的电火花加工参数和电极几何形状对工具钢中特征微精度的影响。在H13钢中使用不同形状的铜电极进行了一组设计实验，这些实验具有变化的EDM工艺参数，例如脉冲电流，开路电压，脉冲时间和脉冲停顿时间。微观和几何精度是响应的量度。已经构建了人工神经网络和回归模型来捕获过程参数对几何特征质量的影响，例如平坦度，深度，坡度，宽度以及入口和出口之间的尺寸变化（DVEE）。

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