Development of an Artificial Grey Fuzzy Inference System to Optimize Hole Quality and Tool Performance in Drilling
How to Cite?
Satya Sai Ravi Kiran Dwibhashyam, Balla Srinivasa Prasad, "Development of an Artificial Grey Fuzzy Inference System to Optimize Hole Quality and Tool Performance in Drilling," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 179-187, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P221
In this work, a sincere effort is made towards developing a fuzzy-based inference model to elevate the performance of drill tool materials (flank wear, VB and temperature, T) and hole quality (Circularity error, Cr). Three different drill bit materials, namely HSS, uncoated tungsten carbide (WC), and coated tungsten carbide (WC), are used to drill holes on the Ti-6Al-4V specimen. Taguchi`s L25 orthogonal array is operated to draft the conducting order of the experiments. The machining factors like rotational speed (N) and feed rate (f) are optimized by keeping cutting depth constant for mobilizing the outcomes. ANOVA is executed, and it is observed that the rotational speed played the foremost role of feed rate in the ascertainment of tool performance. Affirmation tests are performed to corroborate the outcomes, and it is found that grey-fuzzy methodology remains effective in defining the optimal machining parameters.
Grey fuzzy inference system, circularity error, flank wear, infrared thermography, drilling.
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