Reverse simulation of sinking EDM applicable to large curvatures

Kunieda, Masanori; Kaneko, Y.; Natsu, Wataru

doi:10.1016/j.precisioneng.2011.10.003

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…It is difficult and laborious to simulate the entire micro-EDM milling machining process from the perspective of physical processes [ 39 ]. In some previous studies, researchers have proposed geometric simulation models to simulate various EDM processes [ 40 , 41 , 42 , 43 ].…”

Section: Modelling and Simulation Of Materials Removal In Micro-edm Millingmentioning

confidence: 99%

An Investigation into Accumulative Difference Mechanism in Time and Space for Material Removal in Micro-EDM Milling

Zhang

et al. 2021

Micromachines

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In micro-electrical discharge machining (micro-EDM) milling, the cross-section of the microgroove machine is frequently not an ideal rectangle. For instance, there are arc shapes on the bottom and corners, and the sidewall is not steep. The theoretical explanation for this phenomenon is still lacking. In addition to the tip discharge effect, the essential reason is that there is an accumulative difference in time and space during the shape change process of a tool electrode and the microstructure formation on a workpiece. The process parameters are critical influencing factors that determine this accumulative difference. Therefore, the accumulative difference mechanism in time and space is investigated in this paper, and then a theoretical model is developed to simulate the micro-EDM milling process with a straight-line single path. The simulation results for a cylindrical electrode at the two rotational speeds of 0 (nonrotating) and 300 rpm are compared, while the results for a cylindrical electrode and a square electrode at a rotation speed of 0 are also compared to verify that different process parameters generate accumulative differences in the time and space of material removal. Finally, micro-EDM milling experiments are carried out to verify the simulation model. The maximum mean relative deviation between the microgroove profiles of simulation results and those of experiments is 11.09%, and the profile shapes of simulations and experiments have a good consistency. A comparative experiment between a cylindrical electrode and a hollow electrode is also performed, which further verifies the mechanism revealed in the study. Furthermore, the cross-section profile of a microgroove can be effectively controlled by adjusting the process parameters when utilising these accumulative differences through fabricating a microgroove with a V-shaped cross-section by a square electrode and a microgroove with a semi-circular cross-section by a cylindrical electrode. This research provides theoretical guidance for solving the problems of the machining accuracy of detail features in micro-EDM milling, for instance, to machine a microgroove with an ideal rectangular cross-section.

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Section: Modelling and Simulation Of Materials Removal In Micro-edm Millingmentioning

confidence: 99%

An Investigation into Accumulative Difference Mechanism in Time and Space for Material Removal in Micro-EDM Milling

Zhang

et al. 2021

Micromachines

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“…If the features that need to be machined are comparable to the size of the beam, a more sophisticated approach is needed. Some reports on the inverse problem for other time-dependent processes include the following: electrochemical machining (15), where the tool/electrode works in tangential mode to envelope the required surface, and electrodischarge machining (16), where the electrode copies the geometry of the final surface so that a solution of the inverse prob-lem is not required. We recently reported on a solution of the inverse problem in AWJ, working in the linear erosion regime to minimize errors in the generation of simple two-dimensional shapes (17).…”

Section: Energy Beammentioning

confidence: 99%

New models for energy beam machining enable accurate generation of free forms

2017

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“…In turn, MRR models may be used as inputs for the automatic design of manufacturing strategies using layer-by-layer toolpath generation algorithms, the material removal rate being required to characterize the parameters of an "equivalent mechanical mill." Alternatively, one may wish to make use of MRR models to perform crater-by-crater simulations [7][8][9]. Although the resulting transient simulators are numerically expensive, they are expected to predict the effect of sharp, localized features more accurately than models employing a spatially homogeneous removal rate.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining

Surleraux

Lepert

Pernot

et al. 2020

Journal of Computing and Information Science in Engineering

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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.

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Reverse simulation of sinking EDM applicable to large curvatures

Cited by 14 publications

References 8 publications

An Investigation into Accumulative Difference Mechanism in Time and Space for Material Removal in Micro-EDM Milling

An Investigation into Accumulative Difference Mechanism in Time and Space for Material Removal in Micro-EDM Milling

New models for energy beam machining enable accurate generation of free forms

Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining

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