Past PhD Students
“Climbing Advanced Drilling RoBOT (CADBOT)”
Large-scale manufacturing is a key part of current UK manufacturing enterprise and the retention and growth of this industry is a key focus politically, economically and for sustainability purposes. One way towards doing this is intelligent automation; the use of highly capable technologies to cope with process variation and difficult conditions. Within manufacturing here are numerous tasks that are highly unsuitable for humans to do and, if possible, they should be automated. Such jobs involve much repetition, are dangerous or could cause long-term harm to the operator. These jobs, with the right automation, could be reassigned to a robot. The purpose of this research was to investigate the potential for the removal of a number of barriers preventing robots from working in large-scale manufacturing through the design of a next generation crawler robot for advanced robotic drilling.
"Automation of TIG Welding – Process Control and Human Skills"
Traditionally the introduction of automation into mass manufacturing has concentrated upon lower skilled areas. High volume production is typically complemented with the use of high cost fixturing, to overcome part disparity and provide a reliable means of accurate and repeatable manufacturing. Competition to established manufacturing comes from rising economies where labour costs are significantly lower, overcoming a technological disadvantage with large numbers of human operatives. In established manufacturing centres knowledge transfer from human operatives to autonomous systems enables product manufacture to remain competitive. The aims of this research are to identify Key Process Variables (KPVs) within autonomous welding, to capture human skill during the welding process and identify the key sensory feedback utilised in the TIG welding process and to enable additional monitoring and control of the welding process with the addition of enhanced sensory feedback.
"Numerical and Experimental Study of Electroadhesion to Enable Manufacturing Automation”
Electroadhesion is an electrostatically controllable attractive effect between an electroadhesive pad and a substrate. Although electroadhesion is a promising and potentially revolutionising material handling technology due to its distinctive advantages such as enhanced adaptability, gentle handling, reduced complexity, and ultra-low energy consumption, the applicability of this technology is currently constrained as there is a lack of an in-depth understanding of electroadhesion, both theoretically and experimentally. In addition, there is a lack of an effective, efficient, and confident research methodology and platform aiding the electroadhesive pad design, manufacture, and testing. The aims of this research were to identify the factors influencing the electroadhesive forces, and to conduct theoretical optimisation and electrostatic simulation modelling of ectroadhesion and the experimental verification based on a confident pad design, manufacture, testing platform and procedure.
“Intelligent 3D Seam Tracking and Adaptable Weld Process Control for Robotic TIG Welding”
Tungsten Inert Gas (TIG) welding is extensively used in manufacturing, due to its unique ability to produce higher quality welds compared to other shielded arc welding types. However, most TIG welding is performed manually and it has not achieved the levels of automation that other welding techniques have. These types of applications need intelligent decision making capabilities to accommodate any unexpected variation and to carry out the welding of complex geometries. Such decision making procedures must be based on feedback about weld profile geometry. For this research, a real-time position based closed loop system was developed with a six axis industrial robot, a laser triangulation based sensor and data acquisition system for full computer control.
“Human Skill Capturing and Modelling using Wearable Devices”
This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. The thesis provides a methodology to produce a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute.