Geometric influence and multi-objective optimization of WEDM for hardened tool steels using ANN and NSGA-II.

Muhammad Sana, Muhammad Asad, Anamta Khan, Muhammad Umar Farooq, Sana Hassan, Saqib Anwar

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Muhammad SanaDepartment of Mechanical and Industrial Engineering, Montana State University, Bozeman, MT, 59717, USA.ORCID https://orcid.org/0000-0003-1613-4188

Muhammad AsadDepartment of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.

Anamta KhanDepartment of Computer Science, University of Engineering and Technology, Lahore, 54890, Pakistan.

Muhammad Umar FarooqDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, 48109, United States. Umarmuf0@gmail.com.

Sana HassanDepartment of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.

Saqib AnwarIndustrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, 11421, Riyadh, Saudi Arabia.

Funding

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6 · The paper itself

Abstract

Nowadays, due to their exceptional strength, hardness, and resistance to wear, hardened AISI D2 and DC53 tool steels are frequently used in the tool and die-making business. But these materials are difficult to machine conventionally because they include metallic carbides that are abrasive and shorten the life of the cutting tool. Wire electric discharge machining (WEDM) is broadly used to obtain exact details on these hardened steels. However, differences in geometric attributes may result in differences in the machining process. The impact of machining parameters on linear cutting speed (CS) and volumetric material removal rate (MRR) for both flat and curved profiles is examined in this work. These parameters include peak current (I

Indexed as

AISI D2Artificial neural networkCutting speedDC53Material removal rateWire electric discharge machining

Identifiers

PMID41286342

DOI10.1038/s41598-025-26450-x

PMCPMC12644543

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