Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System...

This protocol describes the semi-automated simulation of a scaled production line for assembling an educational worm gear set, using a collaborative robotic arm and a computer vision system to monitor product quality by evaluating two main criteria: shape and color. The objective of this study is to generate consistent and reliable data to assess the capability, stability, and conformity of the process according to customer specifications. The protocol provides a clear methodological framework for collecting and analyzing key indicators through Statistical Process Control (SPC), using capability indices, such as process capability (Cp), process capability index adjusted for centering (Cpk), upper process capability (Cpu), and lower process capability (Cpl), and graphical tools such as histograms and control charts. These enable the identification of deviations and trends in critical product characteristics. The results of the shape evaluation indicate that the automated process is under statistical control, although with a tendency toward the upper specification limit, suggesting the need to adjust the process mean. In contrast, the color evaluation reveals greater variability, low capability (Cpk = 0.539), and points outside control, indicating instability that requires immediate corrective actions. Based on these findings, it is recommended to implement corrective actions to reduce color variability, such as stricter control of inputs, standardization of lighting conditions, and review of operational methods. In general, the results reinforce the importance of integrating automated technologies with statistical tools such as SPC to identify critical deviations, optimize processes, and ensure product conformity. This synergy between automation and statistical analysis forms a key pillar to maintain competitiveness in increasingly demanding industrial environments. In addition, this protocol provides a solid foundation for implementing improvements in real production lines.