IEEE Access (Jan 2025)
Improving Quality Evaluation for Control Systems of Machining Line Product Processing With Smart Algorithmic Techniques in Intuitionistic Fuzzy Multiple-Attribute Group Decision-Making
Abstract
The evaluation of a machining line product quality monitoring system assesses its effectiveness in ensuring consistent product quality during manufacturing. It involves analyzing the system’s ability to detect defects, monitor key process parameters, and provide real-time feedback for corrective actions. Key evaluation criteria include accuracy, reliability, response speed, and integration with other production systems. By identifying strengths and areas for improvement, the evaluation ensures the system meets quality standards, enhances operational efficiency, and reduces waste. This process is crucial for maintaining high-quality output in automated or semi-automated machining lines. The quality evaluation control systems for machining line product processing fall under the category of multi-attribute group decision-making (MAGDM). To address this, the TODIM and MABAC approaches were applied, leveraging their strengths in solving MAGDM problems. Intuitionistic Fuzzy Sets (IFSs) were utilized as a decision-making tool to handle uncertain and imprecise data during the evaluation process. In this study, an integrated intuitionistic fuzzy TODIM-MABAC (IF-TODIM-MABAC) approach in light with intuitionistic fuzzy Euclidean distance (IFED) and intuitionistic fuzzy logarithmic distance (IFLD) is proposed to tackle MAGDM problems under the framework of IFSs. This approach combines the advantages of the TODIM and MABAC methods, enhanced with IFSs, to better manage uncertainty in decision-making. A numerical study is conducted to demonstrate the application of the proposed approach for the quality evaluation control systems of machining line product processing, validating its effectiveness and practicality. The main contributions of this study are outlined: 1) TODIM-MABAC approach in light with IFED and IFLD is extended and enhanced with IFSs to handle uncertainty in MAGDM. 2) The entropy method is employed to objectively determine weight values under the IFS framework. 3) IF-TODIM-MABAC approach is developed to solve MAGDM problems effectively. 4) A numerical example, along with comparative analyses, is provided to validate the practicality and reliability of the proposed approach.
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