A bipolar triangular fuzzy ARLON based decision support system for optimal CNC machine tool selection in smart manufacturing

Computer Numerical Control (CNC) machine is a very crucial point to be taken in contemporary manufacturing settings because a wrong machine can reduce the level of machining precision, increase production costs, operation, and maintenance costs, thus reducing the overall production efficacy. The classical and fuzzy ARLON methods, as traditional multi-criteria decision-making techniques, deal with uncertainty in a partial manner, and have fewer capabilities to consider concurrently three critical decision properties: bipolar preference structures, triangular linguistic patterns, and gradual transition among heterogeneous evaluation criteria. To address these methodological shortcomings, this research offers a superior bipolar triangular fuzzy alternative ranking using a two-step logarithmic normalization (BTF-ARLON) model for systematic assessment and prioritization of CNC machines in the backdrop of conflicting technical and economic provisions. A case study is performed on five CNC machine options and analyzed on the basis of nine key performance indicators. The BTF-ARLON process calculates superiority and inferiority flows of every CNC machine, generating intuitionistic fuzzy balanced and interpretable rankings, which comprehensively represent positive and negative measurements. An analytical comparison between classical ARLON, fuzzy ARLON, intuitionistic fuzzy ARLON, and bipolar fuzzy ARLON methods demonstrates that the BTF-ARLON model is the only method that is capable of simultaneously supporting uncertainty, bipolar reasoning, and triangular fuzziness in a single decision-support framework. The results of the empirical studies prove that the BTF-ARLON framework provides more consistent, discriminative, and practically significant results, and can be considered a potent and comprehensive instrument of CNC machine selection and assessment in the complicated decision-making conditions of manufacturing.