The proposed project aims to enhance the autonomous manufacturing capability of UK industry in metal forming and forging. The project brings together two departments of the University of Strathclyde, namely DMEM and EEE. It augments this knowledge with the experts of Strathclyde’s global strategic partner Nanyang Technology University (NTU, Singapore). With Industry 4.0 being currently widely acknowledged as a key driver of industrial advancement, a strong technologic shift has become apparent within industry to move towards both, more intelligence and more autonomy. Currently, hot forging and forming has benefitted only little from this shift beyond traditional automation. There is a vast opportunity to systematically transform the inherently challenging technologies, namely forming and forging into truly smart and flexible manufacturing systems.
The AFRC offers an outstanding practical background for the applied transformation of Industry 4.0 theories. This project aims at delivering practical demonstrators at TRL 6 through implementing advanced knowledge into intelligent robot behaviour and simulation environments for robotic manipulation and flexible automation into the hot forging area considering the “living” and “dirty” environment of such industries, which requires the consideration of humans, hazardous, dynamic, hot and noisy conditions which did not experience much smart automation yet.
The aim of the project is to demonstrate how to benefit traditionally less automated sectors such as forming and forging through Digital Manufacturing. Advanced smart robotics promises to offer the most flexible option to demonstrate this challenging goal. Environments such as forming and forging challenge robots due to their inherently poor structure (in the following called “dirty”) and rapid dynamics. This requires rapid, dynamic and smart path planning and real-time control of the manipulator.
There are three main objectives in this project:
1. Develop an intelligent path planning algorithm with dynamic obstacle avoidance capability that considers dynamic real-time information from its environment.
2. Demonstrate the power of the algorithm through a set of case studies.
3. Provide a set of best practice examples through working with the AFRC showing the gradual phasing in of the solutions into a real-world environment.