Dr. Ahmad Farhadi (seen here with Stephen Lee, a CONFIRM PhD researcher with a focus on the application of machine learning, AI, and soft computing in Ahmad’s project) is a CONFIRM Smart 4.0 Marie Skłodowska-Curie Fellow who is undertaking research in ‘developing a digital twin for a robotic drilling process’. Digital twin is a concept in Industry 4.0 that provides a digital representation of a manufacturing element through a stream of data collected from the physical world. This data is used to update digital entities, and control commands are sent back to manufacturing elements for manipulation. Therefore, a seamless, continuous information exchange between physical and digital twins occurs, allowing prediction and optimisation of manufacturing processes. In robotic drilling, physical entities include industrial robots, drilling operations and final products that are considered for digital twining.
Drilling is mainly used in the final production stage of structures in aircraft (e.g., fuselage, wings, etc.), with robotic arms to ensure precise and cost-efficient drilling operations. Although robotic drilling offers increased flexibility and speed, hole quality and drilling performance may be compromised due to errors during the process. Therefore, it is evident that robotic drilling needs further investigation, regarding error compensation and process accuracy through online monitoring and real time optimization of deficiencies during the drilling process. A digital twin has the potential for observation of the drilling process and predicting and mitigating errors during robotic drilling.
In the following video, Ahmad talks about his project on ‘developing a digital twin for a robotic drilling process’
