Tungsten Inert Gas (TIG) welding is extensively used in aerospace applications, 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 has not achieved the levels of automation that other welding techniques have. This is mostly attributed to the lack of process knowledge and adaptability to complexities, such as mismatches due to part fit-up. Recent advances in automation have enabled the use of industrial robots for complex tasks that require intelligent decision making, predominantly through sensors. Applications such as TIG welding of aerospace components require tight tolerances and need intelligent decision making capability to accommodate any unexpected variation and to carry out welding of complex geometries. Such decision making procedures must be based on the feedback about the weld profile geometry. In this thesis, a real-time position based closed loop system was developed with a six axis industrial robot (KUKA KR 16) and a laser triangulation based sensor (Micro-Epsilon Scan control 2900-25).

An interest in developing the intelligent machine system that works in conjunction with human has been growing rapidly in recent years. A number of studies were conducted to shed light on how to design an interactive, adaptive and assistive machine system to serve a wide range of purposes including commonly seen ones like training, manufacturing and rehabilitation. In the year 2003, Human Adaptive Mechatronics (HAM) was proposed to resolve these issues. According to past research, the focus is predominantly on evaluation of human skill rather than human performance and that is the reason why intensive training and selection of suitable human subjects for those experiments were required. As a result, the pattern and state of control motion are of critical concern for these works. In this research, a focus on human skill is shifted to human performance instead due to its proneness to negligence and lack of reflection on actual work quality. Human performance or Human Performance Index (HPI) is defined to consist of speed and accuracy characteristics according to a well-renowned speed-accuracy trade-off or Fitts' Law. Speed and accuracy characteristics are collectively referred to as speed and accuracy criteria with corresponding contributors referred to as speed and accuracy variables respectively. This research aims at proving a validity of the HPI concept for the systems with different architecture or the one with and without hardware elements. A direct use of system output logged from the operating field is considered the main method of HPI computation, which is referred to as a non-model approach in this thesis. To ensure the validity of these results, they are compared against a model-based approach based on System Identification theory. Its name is due to being involved with a derivation of mathematical equation for human operator and extraction of performance variables. Certain steps are required to match the processing outlined in that of non-model approach. Some human operators with complicated output patterns are inaccurately derived and explained by the ARX models.

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