Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

This study analyzes the lead time of bending operation in wind turbine tower manufacturing process. Since involves a significant amount employee interaction and parts processed are heavy voluminous, there is considerable variability recorded times. Therefore, machine learning regression analysis has been applied to Two algorithms have used: multivariate Linear Regression M5P method. The goal gain better understanding effect several factors (technical, organizational, experience-related) on process times, attempt predict these times as way increase planning controlling capacity plant. inclusion experience-related variables serves basis for analyzing impact age experience time-wise efficiency workers. proposed approach case Spanish manufacturer, using data from its plant gathered between 2018 2021. results show that trained models moderate predictive power. Additionally, shown by output analysis, would presumably found be non-factors, well some generate an unexpected degree variability.