Single-Valued Trapezoidal Neutrosophic Preference Relations for Evaluating Design Quality in Mechanical Automation Production Control Systems within Intelligent Manufacturing Frameworks

Design quality in mechanical automation control systems must be reevaluated due to the quick integration of intelligent manufacturing technologies into industrial automation. The goal of this research is to provide a thorough assessment methodology that will evaluate these systems' efficacy, flexibility, and technical resilience in the context of Industry 4.0. The performance of various control system options used in intelligent manufacturing environments is examined using eight important criteria, such as system stability, reaction time, energy efficiency, and real-time data processing capabilities. Neutrosophic numbers tools are incorporated into the process to guarantee systematic and objective comparison. We use SingleValued Trapezoidal Neutrosophic Preference Relations to evaluate the criteria and alternatives and overcome uncertainty information. The results emphasize how important fault tolerance, maintainability, and integration compatibility are in determining the overall sustainability and performance of a system. Manufacturing companies looking to optimize control system design in accordance with digital transformation objectives may find this evaluation methodology to be a useful tool.