A detailed machinability assessment of DC53 steel for die and mold industry through wire electric discharge machining

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Khan, Sarmad Ali and Rehman, Mudassar and Farooq, Muhammad Umar and Ali, Muhammad Asad and Naveed, Rakhshanda and Pruncu, Catalin I. and Ahmad, Waheed (2021) A detailed machinability assessment of DC53 steel for die and mold industry through wire electric discharge machining. Metals, 11 (5). 816. ISSN 2075-4701 (https://doi.org/10.3390/met11050816)

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Abstract

Recently, DC53 die steel was introduced to the die and mold industry because of its excellent characteristics i.e., very good machinability and better engineering properties. DC53 demonstrates a strong capability to retain a near-net shape profile of the die, which is a very challenging process with materials. To produce complex and accurate die features, the use of the wire electric discharge machining (WEDM) process takes the lead in the manufacturing industry. However, the challenge is to understand the physical science of the process to improve surface features and service properties. In this study, a detailed yet systematic evaluation of process parameters investigation is made on the influence of a wire feed, pulse on duration, open voltage, and servo voltage on the productivity (material removal rate) and material quality (surface roughness, recast layer thickness, kerf width) against the requirements of mechanical-tooling industry. Based on parametric exploration, wire feed was found the most influential parameter on kerf width: KW (45.64%), pulse on time on surface roughness: SR (84.83%), open voltage on material removal rate: MRR (49.07%) and recast layer thickness: RLT (52.06%). Also, the optimized process parameters resulted in 1.710 µm SR, 10.367 mm3/min MRR, 0.327 mm KW, and 10.443 µm RLT. Moreover, the evolution of surface features and process complexities are thoroughly discussed based on the involved physical science. The recast layer, often considered as a process limitation, was explored with the aim of minimizing the layers’ depth, as well as the recast layer and heat-affected zone. The research provides regression models based on thorough investigation to support machinists for achieving required features.

ORCID iDs

Khan, Sarmad Ali, Rehman, Mudassar, Farooq, Muhammad Umar, Ali, Muhammad Asad, Naveed, Rakhshanda, Pruncu, Catalin I. ORCID logoORCID: https://orcid.org/0000-0002-4926-2189 and Ahmad, Waheed;
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