MARWIN (Rodrigues et al。 2013a)。 The MARWIN   con-cept is illustrated in Fig。 16。

Fig。 13 Adaptive fill mode (ABB Group 2010)

Sensing The vision system in MARWIN is based on a structured light scanning method。 As shown in Fig。 17, multiple planes of light of known pattern are projected onto the target surface, which is recorded by  a  camera。  The spatial relationship between the light source and the camera is then combined with the shape of the captured pattern to get the 3D position of the surface along the pattern。 The advantages of such system are that both camera and projector can be placed as close together as practically possible which may offer advantages to design miniaturization。   Moreover,   the   mathematical  formula-

  tion   of   such   arrangement   is   simple   than   those   of

standard scanners which results in less  computing cycles, thus, making the parallel design more appropriate for 3D real-time processing (Rodrigues et al。  2013a)。

Results

The parallel arrangement requires 35 % fewer arithmetic operations to compute a point cloud in 3D being thus more appropriate for real-time applications。 Experiments show that the technique is appropriate to scan a variety of surfaces and, in particular, the intended metallic parts for robotic welding tasks (Rodrigues et al。 2013b)。 The method allows the robot to adjust the welding path de- signed from the CAD model to the actual workpiece。 Alternatively, for non-repetitive tasks and where a CAD model is not available, it is possible to interactively define the path online over the scanned surface (Rodrigues et al。 2013c)。论文网

Conclusions

Robotics and sensors, together with their associated control systems have become important elements in in- dustrial manufacturing。 They offer several advantages, such as improved weld quality, increased productivity, reduced weld costs,  increased  repeatable  consistency of welding, and minimized human input for selection of weld parameters, path of robotic motion, and fault detection  and  correction。

Continuous development in the field of robotics, sen- sors, and control means that robotic welding has reached the third-generation stage in which a system can operate in real-time and can learn rapid changes in the geometry of the seam while operating in unstruc- tured environments。

Of the programming methods commonly used with welding robots, conventional online programming with a teach pendant, i。e。, lead-through programming, has the disadvantage of causing breaks in production during programming。 Furthermore, it is only able to control simple robot paths。 Offline programming, due to its reusable code, flexibility of modification, and ability to generate complex paths, offers the benefit of a  reduction in production downtime in the programming phase for setup of new parts and supports autonomous robotic welding with a library of programming codes for weld parameters and trajectories for different 3D  CAD models of workpieces。

Despite the advantages of sensor-based robotic weld sys- tems, there are some issues associated with robotic weld- ing that need to be addressed to ensure proper selection based on work requirements and the work  environment。

A variety of sensors are used in robotic welding for detection and measurement of various process fea- tures and parameters, like joint geometry, weld pool geometry,  location,  etc。,  and  for  online  control  of the

weld process。 The primary objectives of these sensors, along with the control system, are seam finding, seam tracking, adaptive control, and quality monitoring of welds。