AI and Robotics: A Symbiotic Relationship in Digital Manufacturing

This study illustrates the relationship between robotics and machine learning, two disciplines that are revolutionising our surroundings. The synergy that makes it possible to create intelligent, flexible, and self-sufficient machines that are capable of carrying out a greater variety of activities than previously possible is demonstrated by this research. Machine learning (ML) has gained popularity as a computational tool in manufacturing in recent years. The current examination analyzed various ML calculations to foresee the geometry of the bead in WAAM (wire-arc additive manufacturing). A WAAM method in view of mechanical controlled GMAW (gas metal arc welding) was utilized to store the beads. Five machine learning models were used in the study: CNN, DT, SVM, XGBoost algorithms, and LR. Statistical measures like the coefficient of determination (R2 value), mean square error (MSE), mean absolute error (MAE), and index of merit (IM) were used to assess the performance of ML models. The comparison of ML algorithms showed that machine learning-based computational labour produces high-performance outcomes with less time and resource consumption. Therefore, in contemporary manufacturing businesses, ML may be the most efficient computational instrument.