As the automotive industry accelerates its shift toward electric vehicles (EVs), manufacturers must adapt to meet the evolving demands of production. A critical factor in EV manufacturing is the precise alignment of the stator and rotor within electric motors. A parameter that directly influences motor performance, reliability, and efficiency. Currently, the company has no preventive method to address misalignment during assembly, emphasizing the need for a robust quality assurance process.

An important part of this work is understanding both the assembly process and the requirements of the end customer. To achieve this, the study begins with a review of literature on similar assembly processes, which is collected and analyzed in detail to identify potential issues, determine their root causes, evaluate their impact on performance and quality, and explore possible solutions.

At the same time, the specific needs and expectations of the end customer are examined to ensure that proposed improvements align with performance, reliability, and operational requirements. This dual approach provides a comprehensive understanding of manufacturing challenges and customer priorities, forming a strong foundation for targeted quality assurance measures.

This work presents a structured Quality Function Deployment (QFD)-based methodology for identifying, evaluating, and selecting the most suitable existing sensor technology for stator–rotor integration. The methodology is divided into three stages. First, the integration process is analyzed to identify key challenges, and customer requirements are translated into measurable technical specifications. Second, relevant sensor technologies are compared in terms of features, applications, and limitations related to quality assurance. Finally, detailed selection criteria are defined, and a scoring system is applied to objectively rank the technologies. This structured process ensures a transparent and evidence-based selection of the optimal solution.

Six measurement techniques are examined: laser displacement measurement, laser Doppler vibrometry, inductive proximity, capacitive sensing, eddy current sensing, and wire strain gauges. Their applications, advantages, and limitations are assessed through a comparative evaluation, which forms the basis of the QFD-based ranking and selection process. Sensors were evaluated on key factors including precision, environmental durability, response speed, ease of integration, and automation compatibility. Each was scored from 1 to 5 across eight weighted criteria, with results identifying laser displacement sensors as the most effective solution.