{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:53:19Z","timestamp":1753879999367,"version":"3.41.2"},"reference-count":27,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T00:00:00Z","timestamp":1582070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/J004901\/1"],"award-info":[{"award-number":["EP\/J004901\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper focuses on efficient computational optimization algorithms for the generation of micro electro discharge machining (\u00b5EDM) 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 \u00b5EDM die-sinking operations.<\/jats:p>","DOI":"10.1115\/1.4045956","type":"journal-article","created":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T13:49:05Z","timestamp":1578750545000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":8,"title":["Machine Learning-Based Reverse Modeling Approach for Rapid Tool Shape Optimization in Die-Sinking Micro Electro Discharge Machining"],"prefix":"10.1115","volume":"20","author":[{"given":"Anthony","family":"Surleraux","sequence":"first","affiliation":[{"name":"Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, UK"}]},{"given":"Romain","family":"Lepert","sequence":"additional","affiliation":[{"name":"Arts et M\u00e9tiers, LISPEN EA 7515, HeSam, Aix-en-Provence 13617, France"}]},{"given":"Jean-Philippe","family":"Pernot","sequence":"additional","affiliation":[{"name":"Arts et M\u00e9tiers, LISPEN EA 7515, HeSam, Aix-en-Provence 13100, France"}]},{"given":"Pierre","family":"Kerfriden","sequence":"additional","affiliation":[{"name":"Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, UK;"},{"name":"Centre des Mat\u00e9riaux, Mines ParisTech, PSL University, \u00c9vry cedex BP87 F-91003, France"}]},{"given":"Samuel","family":"Bigot","sequence":"additional","affiliation":[{"name":"Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, UK"}]}],"member":"33","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"issue":"1","key":"2021022709544568700_CIT0001","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.techfore.2008.03.021","article-title":"Nanotechnology Innovation System: Understanding Hidden Dynamics of Nanoscience Fusion Trajectories","volume":"76","author":"Islam","year":"2009","journal-title":"Technol. 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