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首页> 外文期刊>Journal of Computing and Information Science in Engineering >Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining
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Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining

机译：基于机器学习的逆向模拟方法，用于快速磨削微电路电气放电加工中的快速工具形状优化

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摘要

This paper focuses on efficient computational optimization algorithms for the generation of micro electro discharge machining (μEDM) tool shapes. In a previous paper, the authors presented a reliable reverse modeling approach to perform such tasks based on a crater-by-crater simulation model and an outer optimization loop. Two-dimensional results were obtained but 3D tool shapes proved difficult to generate due to the high numerical cost of the simulation strategy. In this paper, a new reduced modeling optimization framework is proposed, whereby the computational optimizer is replaced by an inexpensive surrogate that is trained by examples. More precisely, an artificial neural network (ANN) is trained using a small number of full reverse simulations and subsequently used to directly generate optimal tool shapes, given the geometry of the desired workpiece cavity. In order to train the ANN efficiently, a method of data augmentation is developed, whereby multiple features from fully simulated EDM cavities are used as separate instances. The performances of two ANN are evaluated, one trained without modification of process parameters (gap size and crater shape) and the second trained with a range of process parameter instances. It is shown that in both cases, the ANN can produce unseen tool shape geometries with less than 6% deviation compared to the full computational optimization process and at virtually no cost. Our results demonstrate that optimized tool shapes can be generated almost instantaneously, opening the door to the rapid virtual design and manufacturability assessment of μEDM die-sinking operations.
机译：本文侧重于有效的计算优化算法，用于产生微电路电气放电加工（μEDM）刀具形状。在以前的论文中，作者呈现了一种可靠的反向建模方法，以基于逐步陨石坑模拟模型和外部优化循环执行这些任务。获得二维结果，但由于仿真策略的高数值成本，3D刀具形状难以产生。在本文中，提出了一种新的降低的建模优化框架，其中计算优化器被诸如示例训练的廉价代理代替。更精确地，考虑到所需工件腔的几何形状，使用少量的完整反向模拟训练了人工神经网络（ANN），并且随后用于直接产生最佳工具形状。为了有效地训练ANN，开发了一种数据增强的方法，由此从完全模拟的EDM腔中的多个特征用作单独的实例。评估两个ANN的性能，在没有修改工艺参数（间隙尺寸和火山口形状）的情况下进行一次培训，以及一系列工艺参数实例的第二次训练。结果表明，在这两种情况下，与完整的计算优化过程相比，ANN可以产生低于6％偏差的看不见的工具形状几何形状，并且实际上没有成本。我们的结果表明，可以几乎瞬间产生优化的刀具形状，为μEDM模沉作业的快速虚拟设计和可制造性评估开门。

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