The integration of robots into various industries, including manufacturing, has introduced new challenges in achieving efficient human-robot collaboration. A crucial aspect of successful collaboration is the ability of robots to understand and respond to human emotions. In the context of human-robot collaboration in manufacturing, accurately predicting human emotions is essential for enhancing efficiency and safety. This paper presents a setup for human emotion detection, focusing on facial emotion recognition. The proposed model and descriptive summary involve the utilising state-of-the-art algorithms such as AlexNet, HaarCascade (HCC), MTCNN (Multi-Task Cascaded Convolutional Neural Networks), and SVM (Support Vector Machine), applied to datasets like CK+, JAFFE, and AffectNet. The performance of each facial recognition model is evaluated in real-time scenarios, resulting in significant progress with an accuracy improvement from 40% to 78.1%. These results demonstrate the effectiveness of the approach in enabling adaptive robot control based on human emotions and enhancing collaboration quality. This research uniquely integrates facial emotion recognition and robot control to enable adaptive responses during human-robot collaboration in manufacturing settings. By understanding and responding to human emotions, robots can improve their interactions with humans, leading to increased productivity and improved overall collaboration efficiency (If EquinOCS, our proceedings submission system, is used, then the disclaimer can be provided directly in the system).