Fuzzy Analytic Hierarchy Process and Markov Chain-WSM/WPM/WASPAS Approaches to solving the Surface Roughness Problem in the Boring of Carbon Steel IS 2062 GR E250 Plates on CNC Machines

Isaac Temitope Abiola, Sunday Ayoola Oke


This paper establishes how the process engineer in a machine shop could capture the uncertainty and the transition of process parameters to improve the surfaced finish of bored work material (carbon steel IS 2062 GR E250 plates) and select the best parameters to achieve the aim. The fuzzy analytic hierarchy process method incorporating geometric mean and a novel Markov chain oriented weightage scheme were used as inputs into three multicriteria methods of weighted sum model (WSM), weighted product model (WPM) and weighted product model and weighted aggregated sum-product, assessment (WASPAS) model. Published literature data were used to validate the methods and their integrations. The novel Markov chain model borrows ideas from the orthogonal array, random number generation and the transition states of parameters. Finally, the optimal parametric setting idea is used to interprete the final results based on an initial response table determination, which are the averages of the signal-to-noise ratios summarized. The most important results are obtained from the fuzzy AHP-Markov WASPAS method. These are the feed parameter (preference score of 1.624) as the best parameter and the depth of cut with the preference score of 1.188 as the worst parameter. The findings indicate that process engineers should attach the most important interest to the feed rate as it is the most effective controlling parameter of surface finish during the boring operation of carbon steel IS 2062 GR E250 plates. Machining shops can employ the framework to evaluate and predict system performance before financial resource commitment to operations.


Selection, optimal values, preference scores, ranking, multicriteria

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DOI: http://dx.doi.org/10.22441/ijiem.v3i1.13242


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