Hazard Regression Modeling for Robotic Performance Prediction

Robotic systems are widely applied in process industry to reduce manufacturing labor costs and increase production productivity. Due to the uncertainties existed in the manufacturing environment, the performance improvement of the assembly process is important yet challenging. This paper proposes a regression-based method to predict the performance of the robotic assembly process. Statistical hazard models are introduced to quantify the influence of possible controllable parameters on the process performance metrics. A real-world case study of an assembly production process is provided to demonstrate the effectiveness of the proposed method.