conduct bead placement and seam tracking, and ensure proper joint fill (Cary and Helzer 2005)。 Use of sensors allows adaptive control for real-time control and adjust- ment of process parameters such as welding current and voltage。 For example, the capabilities of sensors in seam finding, identification of joint penetration and joint fill- ing, and ensuring root penetration and acceptable weld bead shape mean that corrective modification of relevant welding parameters is done such that constant weld quality is maintained (Cary and Helzer 2005; Drews and Starke 1986)。 An adaptive welding robot should have the capabilities to address two main aspects。 The first aspect is the control of the end effector’s path and orientation so that the robot is able to track the joint to be welded with high precision。 The second one is the control of welding process variables in  real  time,  for  example, the control of the amount of metal deposition into the joint as per the dimensions of the gap separating the parts  to  be welded。

Chen et al。 (2007) studied the use of laser vision sensing for adaptive welding of an aluminum alloy  in  which the wire feed speed and the welding current are adjusted auto- matically as per the groove conditions。 The sensor was used to precisely measure the weld groove and for automatic seam tracking involving automatic torch traverse   alignment

and torch height adjustment during welding。 An adaptive software was employed that calculated the wire feed rate according to the variation in the  gap  and  the  weld area。 The software included extraction of groove geometry, cal- culation and filtering, querying of the adaptive table (ADAP table as shown in Table 2), and generation of the control output signal。

Figure 4 shows the control flow module for adaptive control of weld parameters for the  system。

The process of adaptive control consisted of calcula- tion of groove area from geometry data transmitted from the image processing module, followed by filtering of the calculated area data to remove invalid data and noise。 Next, the module queried the ADAP table to get the proper welding parameters, i。e。, weld current and wire feed rate。 The corresponding values of analog  signals were then transmitted to control  the  power  source and the wire feeder (Chen et al。  2007)。

Quality monitoring Use of automatic weld quality moni- toring systems results in reduced production  costs through the reduced manpower required  for inspection。 An automatic detection system for welding should be able to classify weld defects like porosity, metal spatter, irregu- lar bead shape, excessive root reinforcement, incomplete penetrations and burn-through。 Most commercial moni- toring systems work in a similar way: voltage, current, and other process signals are measured and compared with preset nominal values。 An alarm is triggered when any difference from the preset values exceeds a given threshold。 The alarm thresholds are correlated with real weld defects or relate to specifications defined in the weld- ing procedure specification (WPS) (Pires et al。 2006)。 Currently, common nondestructive testing methods for inspection  of weld bead include  radiography,  ultrasonic,

Table 2 Adaptive welding parameters table (ADAP table) (Chen et al。 2007)

vision, magnetic detection, and eddy current and acoustic measurements (Abdullah et al。 2013)。

Quinn et al。 (1999) developed a method for detection of flaws in automatic constant-voltage gas metal arc welding (GMAW) using the process current and voltage signals。 They used seven defect detection algorithms to process the current and voltage signals to get quality pa- rameters and flag welds that were different from the baseline record of previously made  defect-free  welds。 The system could effectively sense melt-through, loss of shielding gas, and oily parts that cause surface and sub- surface porosity。