Abstract Technical innovations in robotic welding and greater availability of sensor-based control features have enabled manual welding processes in harsh work environments with excessive heat and fumes to be replaced with robotic welding。 The use of industrial robots or mechanized equipment for high-volume productivity has become increasingly common, with robotized gas metal arc welding (GMAW) generally being used。 More widespread use of robotic welding has necessitated greater capability to control welding parameters and robotic motion and improved fault detection and fault correction。 Semi-autonomous robotic welding (i。e。, highly automated systems requiring only minor operator intervention) faces a number of problems, the most common of which are the need to compensate for inaccuracies in fixtures for the workpiece, variations in workpiece dimensions, imperfect edge preparation, and in-process thermal distortions。 Major challenges are joint edge detection, joint seam tracking, weld penetration control, and measurement of the width or profile of a joint。 Such problems can be most effectively solved with the use of sensory feedback signals from the weld joint。 Thus, sensors play an important role in robotic arc welding systems with adaptive and intelligent control system features that can track the joint, monitor in-process quality of the weld, and account for variation in joint location and geometry。 This work describes various aspects of robotic welding, programming of robotic welding systems, and problems associated with the technique。 It further discusses commercially available83323

seam-tracking and seam-finding sensors and presents a practical case application of sensors for semi-autonomous robotic welding。 This study increases familiarity with robotic welding and the role of sensors in robotic welding and their associated problems。Review Introduction Industrial robots and mechanized equipment have become indispensable for industrial welding for high-volume prod- uctivity because manual welding  yields  low production rates due to the harsh work environment and extreme physical demands (Laiping et al。 2005)。 Dynamic market behavior and strong competition are forcing manufacturing companies to search for optimal production procedures。 As shown in Fig。 1 (Pires et al。 2003), for small/medium production volumes, robotic production yields the best cost per unit performance when compared to manual and hard automation。 In addition to competitive unit costs, robotic welding systems bring other advantages, such as improved productivity, safety, weld quality, flexibility and workspace utilization,  and  reduced  labor  costs  (Robot  et  al。   2013a;

Robert et al。 2013)。 The increase in the range of applica- tions of robotic welding technology has led to a need to re- duce operator input and enhance automated control over welding parameters, path of robotic motion, fault detection, and fault correction (Schwab et al。 2008)。 Even though the level of complexity and sophistication of these robotic systems is high, their ability to adapt to real-time changes in environmental conditions cannot equal the ability of human senses to adapt to the weld environ- ment (Hohn and Holmes   1982)。

According to the Robotics Institute of America, a robot  is a “reprogrammable, multifunctional  manipulator  designed to move materials, parts, tools, or specialized devices, to variable programmed motions for the performance of a var- iety of tasks。” While the first industrial robot was developed by Joseph Engelburger already in the mid-1950s, it was not until the mid-1970s that robotic arc welding was first usedin  production。   Subsequently,   robotics   has  been  adopted

Laboratory of Welding Technology, Lappeenranta University of Technology, Lappeenranta  FI-53851, Finland

with many welding processes。 The advantages of robotic welding vary from process to process but common   benefits