Intelligent robot It is very difficult and even impossible to  anticipate  and  identify  all  situations  that  the    robot

could do during his task execution。 Therefore, the soft- ware developer must specify the categories of situation and provide the robot  with  sufficient  intelligence  and the ability to solve problems of any class of its program。 Sometimes, when situations are ambiguous and uncer- tain, the robot must be able to evaluate different possible actions。 If the robot’s environment does not change, the robot is given a model of its environment so that it can predict the outcome of his actions。 But if  the environ- ment changes, the robot should learn。  This  is  among other prerequisites, which calls for the development and embedding in robots’ system of artificial intelligence (AI) capable of learning, reasoning, and problem solving (Tzafestas and Verbruggen 1995)。

The most welding robots serving in practical production still are the teaching and playback type and  cannot  well meet quality and persification requirements of welding production because these types of robots do not have the automatic functions to adapt circumstance changes and un- certain disturbances (errors of pre-machining and fitting workpiece, heat conduction, dispersion during welding process) during  welding  process  (Tarn  et  al。  2004;  Tarn et al。 2007)。 In order to overcome or restrict different un- certainty which influences the quality of the weld, it would be an effective approach to develop and improve the intelli- gent technology of welding robots such as vision sensing, multi-sensing for welding robots, recognition of welded en- vironment, self-guiding and seam-tracking, and intelligent real-time control procedures for welding robots。 To  this end, the development of an intelligence technology to improve the current method of learning and  use  for playback programming for welding robots is essential to achieve high quality and flexibility expected of welded products (Chen and Wu 2008; Chen  2007)。

Intelligent robots are expected to take an active role in the joining job, which comprises as large a part of the ma- chine industry as the machining job。 The intelligent robot can perform highly accurate assembly jobs, picking up a workpiece from randomly piled workpieces on a tray, as- sembling it with fitting precision of 10 μm or less clear- ance with its force sensors, and high-speed resistant spot arc welding in automotive welding and painting。 However, the industrial intelligent robots still have tasks in which they cannot compete with skilled workers, though they have a high level of skills, as has been explained so far。 Such as assembling flexible objects like a wire harness, there are several ongoing research and development activ- ities in the world to solve these challenges (Nof 2009)。

Problems in robotic  welding

Despite the benefits from using robotic systems, associ- ated problems require due consideration。 Issues include the following:

●The consistency required for making part after part, which, in the absence of proper control, might fluctuate due to poor fixturing or variations in the metal forming process。

●In the case of low to medium volume manufacturing or repair work, the time and effort taken to program the robot to weld a new part can be quite high (Dinham and Fang 2013)。

●Robotic welding requires proper joint design, consistent gap conditions and gap tolerance not exceeding 0。5 to 1 mm。 Variation in gap condition requires the use of sensing technologies for gap filling (Robot et al。 2013b)。

●Automation of welding by robotic systems has high initial cost, so accurate calculation of return on investment (ROI) is essential (Rochelle 2010)。

●Possible shortages of skilled welders with the requisite knowledge and training pose limitations。