journal article·2024-10-01Open access

AISI D2 steel machining and manufacturing process optimization for tooling applications in biomedical industry

Mehdi Tlija, Tayyiba Rashid, Muhammad Sana, Muhammad Umar Farooq, Muhammad Imam Ammarullah

AIP Advances· Volume 14· Issue 10

Abstract

Tool steels such as AISI D2 are famous in the manufacturing industry because of their engineering applications. The precise interplay of improved hardness and toughness makes the machining of complex geometries challenging through conventional machining options. Therefore, non-conventional processes such as wire electric discharge machining (WEDM) are preferred because of their simultaneous machining and surface modification actions. To investigate the complex process parameters and their sensitivity, material removal rate (MRR) and cutting surface roughness (SR) are the corresponding performance measure characteristics for WEDM machining on AISI D2 tool steel. The L18 mixed-level Taguchi technique has been used for obtaining combinations of experiments on two levels of thickness and three levels of other remaining factors (21 × 33). Analysis of variance (ANOVA) and signal-to-noise ratio have been applied to measure the magnitude of effects on each control factor, to investigate the optimum levels of input process parameters on machining characteristics, and to identify their significance. ANOVA analysis revealed that, for both responses, all main effect variables are highly significant, with p-values equal to zero. Moreover, the coefficient of determination (R2) value in the ANOVA findings for both responses is above 97%, indicating the high reliability of the model. In addition, the composite desirability (dG) is considered to maximize MRR and minimize the SR during WEDM of D2; the better combination of optimum levels of machining parameters (T = 25.4 mm, Pon = 4 µs, SV = 95 V, and WT = 5 kg-f) has a dG of 0.5614.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Machining and Optimization Techniques · Advanced machining processes and optimization · Additive Manufacturing and 3D Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Industrial Engineering Department, College of Engineering, King Saud University 1 , P.O. Box 800, Riyadh 11421,

Department of Industrial Engineering and Management, University of the Punjab 2 , Lahore 54590, Punjab,

Department of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology 3 , Lahore 54890, Punjab,

School of Mechanical Engineering, University of Leeds 4 , Leeds LS2 9JT,

Department of Mechanical Engineering, Faculty of Engineering, Universitas Diponegoro 5 , Semarang 50275, Central Java,

Funding

King Saud University

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