- Review Article
- Open Access
Robotic arc welding sensors and programming in industrial applications
© Kah et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the origi 2015
Received: 20 March 2014
Accepted: 24 April 2014
Published: 17 July 2015
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 available 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.
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 variety 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 used in production. Subsequently, robotics has been adopted with many welding processes. The advantages of robotic welding vary from process to process but common benefits generally include improved weld quality, increased productivity, reduced weld costs, and increased repeatable consistency of welding (Lane 1987).
Robots in arc welding
The following sections briefly discuss some of the key aspects of robotics in welding technology.
Phases in welding operations
The welding operation consists of three different phases that need critical consideration in designing a fully automated robotic welding system to achieve good performance and weld quality (Pires et al. 2006):
In this phase, the weld operator sets up the parts to be welded, the apparatus (power source, robot, robot program, etc.) and the weld parameters, along with the type of gas and electrode wires. When CAD/CAM or other offline programming is used, a robot weld pre-program is available and placed online. Consequently, the robotic program might only need minor tuning for calibration, which can be easily done by the weld operator performing selected online simulations of the process.
Automatic equipment requires the same capabilities as manual welding, i.e., the system should be capable of maintaining a torch orientation that follows the desired trajectory (which may be different from planned), performing seam tracking, and changing weld parameters in real time, thus emulating the adaptive behavior of manual welders.
The analysis phase is generally a post-welding phase where the welding operator examines the obtained weld to ascertain if it is acceptable or whether changes are required in the previous two phases. Use of advanced sensors, such as 3D laser cameras, enables execution of this phase online during the welding phase.
Robotic programming modes
Different methods exist for teaching or programming a robot controller; namely, manual methods, online programming (walk-through, lead-through), and offline programming. Manual methods are primarily used for pick-and-place robots and are not used for arc welding robots (Cary and Helzer 2005).
This category of robotic programming includes lead-through and walk-through programming. Use of the manual online programming method requires no special hardware or software on-site other than that which is used for the manufacturing process. The major drawback of online programming is that it is quite inflexible and it is only able to control simple robot paths (Pan et al. 2012a). In the walk-through method, the operator moves the torch manually through the desired sequence of movements, which are recorded into the memory for playback during welding. The walk-through method was adopted in a few early welding robots (Cary and Helzer 2005) but did not gain widespread use. The conventional method for programming welding robots is online programming with the help of a teach pendant, i.e., lead-through programming. In this approach, the programmer jogs the robot to the desired position with the use of control keys on the teaching pendant and the desired position and sequence of motions are recorded. The main disadvantage of the online teaching method is that the programming of the robot causes breaks in production during the programming phase (McWhirter 2012).
The teach and playback mode has limited flexibility as it is unable to adapt to the many problems that might be encountered in the welding operation, for example, errors in pre-machining and fitting of the workpiece, and in-process thermal distortion leading to change in gap size. Thus, advanced applications of robotic welding require an automatic control system that can adapt and adjust the welding parameters and motion of the welding robots (Hongyuan et al. 2009). Hongyuan et al. (2009) developed a closed loop control system for robots that used teach and playback based on real-time vision sensing for sensing topside width of the weld pool and seam gap to control weld formation in gas tungsten arc welding with gap variation in multi-pass welding. In spite of all the abovementioned drawbacks, online programming is still the only programming choice for most small to median enterprises (SMEs). Online programming methods using more intuitive human-machine interfaces (HMI) and sensors information have been proposed by several institutions (Zhang et al. 2006; Sugita et al. 2003). The assisted online programming can be categorized into assisted online programming and sensor-guided online programming. Although dramatic progress has been carried out to make online programming more intuitive, less reliant on operator skill, and more automatic, most of the research outcomes are not commercially available aside from Sugita et al. 2003.
Offline programming (OLP) with simulation software allows programming of the welding path and operation sequence from a computer rather than from the robot itself. 3D CAD models of the workpieces, robots, and fixtures used in the cell are required for OLP. The simulation software matches these 3D CAD models, permitting programming of the robot’s welding trajectory from a computer instead of a teaching pendant in the welding cell as in online programming. After simulation and testing of the program, the instructions can be exported from the computer to the robot controller via an Ethernet communication network. Ongoing research suggests, however, that the use of sensing technology would make it feasible to completely program the final trajectory only with OLP (Miller Electric Mfg Co. 2013). Pan et al. (2012a) developed an automated offline programming method with software that allows automatic planning and programming (with CAD models as input) for a robotic welding system with high degrees of freedom without any programming effort. The main advantages of OLP are its reusable code, flexibility for modification, ability to generate complex paths, and reduction in production downtime in the programming phase for setup of a new part. Nevertheless, OLP is mostly used to generate complex robot paths for large production volumes because the time and cost required to generate code for complex robotic systems is similar to if not greater than with online programming (Pan et al. 2012a). Currently, for a complex manufacturing process with small to median production volume, very few robotic automation solution are used to replace manual production due to this expensive and time-consuming programming overhead. Although OLP has the abovementioned advantages, it is not popular for small to median enterprise (SME) users due to its obvious drawbacks. It is difficult to economically justify an OLP for smaller product values due to the high cost of the OLP package and programming overhead required to customize the software for a specific application. Development of customized software for offline programming is time-consuming and requires high-level programming skills. Typically, these skills are not available from the process engineers and operators who often perform the robot programming in-process today. As OLP methods rely accurate modeling of the robot and work cell, additional calibration procedures using extra sensors are in many cases inevitable to meet requirements (Pan et al. 2012b).
It is very difficult and even impossible to anticipate and identify all situations that the robot could do during his task execution. Therefore, the software 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 uncertain, 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 environment 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 diversification requirements of welding production because these types of robots do not have the automatic functions to adapt circumstance changes and uncertain 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 uncertainty which influences the quality of the weld, it would be an effective approach to develop and improve the intelligent technology of welding robots such as vision sensing, multi-sensing for welding robots, recognition of welded environment, 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 machine 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, assembling it with fitting precision of 10 μm or less clearance 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 activities in the world to solve these challenges (Nof 2009).
Problems in robotic welding
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.
Unlike adaptive human behavior, robots cannot independently make autonomous corrective decisions and have to be supplemented by the use of sensors and a robust control system for decision-making.
Robotic welding cannot easily be performed in some areas like pressure vessels, interior tanks, and ship bodies due to workspace constraints (Robotics Bible 2011).
The majority of sensor-based intelligent systems available in the market are not tightly integrated with the robot controller, which limits the performance of the robotic system as most industrial robots only offer around a 20-Hz feedback loop through the programming interface. Consequently, the robot cannot respond to the sensor information quickly, resulting in sluggish and sometimes unstable performance.
Sensors in robotic welding
Need for sensors in robotic welding
Applications and quality of sensors
Type of sensors
Can recognize 3-dimensional offset of the workpiece. The wire tip or the gas nozzle can serve as a sensor. Can be used for accurate learning of the path before welding.
Can defect elastically, using tactile probes it is difficult, if not impossible, to provide information on the joint fit up. Poor weld joint repeatability.
Relatively low cost. The mechanically probes leads the welding spots.
Not adaptable to suit a variety of joint geometries.
Largely used in industry, configurations with one pick-up coil can provide a cross-seam or vertical path correction signal.
Different sensor is needed for each type of joint, should stay very close to the joint
Offer the opportunity to measure the distance between the workpiece and an electrically conduction plate of small dimension.
It is hard to extract a correction signal in two direction from the capacity variations
Apart from seam-tracking application, an acoustical sensing system can be used to explore the workpiece for obstacle and maybe to inspect a produced weld.
Line of sight must not deviate from the surface normal; another limitation is the temperature dependence of the speed of the sound.
Can be used for seam tracking as well as for geometrical recognition of the weld pool, to adapt process parameters in the case of possible deviations.
To prevent accessibility limitation, it may require additional axes for seam tracking, tremendous effort to introduce technical integration, regularly check the lens protection.
Weld pool observation
Dedicated to welding pool geometry and properties. The obtained image is processed and pattern recognition algorithms are used to extract the dimensions and form of the weld pool. Different sensors can be applied: optic sensing, thermal sensing, real-time radiography, weld pool oscillation sensing,
There should be a clear interpretation of the image by the system, in order to give torch corrective changes accordingly
No additional voluminous sensor needs to be fixed to the weld torch. Its simple operation and implementation have made arc sensing a commonly accepted off-the-shelf technique.
The torch has to be weaved during welding. The dimension of the joint must exceed some critical dimension, e.g., it is not applicable for sheet metal. In addition, a signal can be obtained only after the arc has been established. Therefore, it cannot be used for finding starting point of the weld.
Contact-type sensors, like nozzle or finger, are less expensive and easier to use than a non-contact. However, this type of sensors cannot be used for butt joints and thin lap joints. Non-contact sensors referred as through-the-arc sensors may be used for tee joints, U and V grooves, or lap joints over a certain thickness. These types of sensors are appropriate for welding of bigger pieces with weaving when penetration control is not necessary. However, it is not applicable to materials with high reflectivity such as aluminum. Great attention has been paid to joint sensing by welding personnel since the 1980s. The principal types of industrial arc-welding sensors that have been employed are optical and arc sensors (Nomura et al. 1986). Some of the most important uses of sensors in robotic welding are discussed below:
Seam finding (or joint finding) is a process in which the seam is located using one or more searches to make sure that the weld bead is precisely deposited in the joint. Seam finding is done by adjusting the robotic manipulator and weld torch to the right position and orientation in relation to the welding groove or by adjusting the machine program, prior to welding (Servo Robot Inc 2013a). Many robotic applications, especially in the auto industry, involve producing a series of short and repeated welds for which real-time tracking is not required; however, it is necessary to begin each weld in the correct place, which necessitates the use of seam-finding sensors (Meta Vision Systems Ltd 2006).
Seam tracking enables the welding torch to follow automatically the weld seam groove and adjust the robotic manipulator accordingly; to counter the effects of variation in the seam caused by distortion, uneven heat transfer, variability of gap size, staggered edges, etc. (Xu et al. 2012).
Automatic vertical and horizontal correction of the path (even path changes necessitated by thermal distortion)
Less stringent accuracy demands on objects and fixtures
Welding parameter adaptation
Reduced programming time
Lower rejection rates
Higher welding quality
Viability of short series
In adaptive control welding, i.e., a closed loop system using feedback-sensing devices and adaptive control, there is a process control system that detects changes in welding conditions automatically with the aid of sensors and directs the equipment to take appropriate action. Sensors are needed in adaptive control welding to find the joint, assess root penetration, 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 adjustment of process parameters such as welding current and voltage. For example, the capabilities of sensors in seam finding, identification of joint penetration and joint filling, 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.
Adaptive welding parameters table (ADAP table) (Chen et al. 2007)
Groove area [mm2]
Wire feeder control signal [V]
Wire feeding rate [cm.min−1]
Welding current [A]
The process of adaptive control consisted of calculation 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).
Use of automatic weld quality monitoring 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, irregular bead shape, excessive root reinforcement, incomplete penetrations and burn-through. Most commercial monitoring 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 welding procedure specification (WPS) (Pires et al. 2006). Currently, common nondestructive testing methods for inspection of weld bead include radiography, ultrasonic, 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 parameters 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 subsurface porosity.
Seam-tracking and seam-finding sensors
Several sensors for robotic welding, mainly for seam tracking and quality control, are commercially available. Some of the more renowned sensor products in the field of robotic welding are discussed below:
Robo-Find (Servo Robot Inc)
It is immune to arc process like spatter and can withstand radiated heat.
It can find seams for all weldable materials.
It has an embedded color video camera for remote monitoring and programming.
It has the ability to recognize joint type automatically.
It reduces repair and rework.
It can be retrofitted to existing equipment.
It employs smart camera technology with embedded control unit (no separate controller with everything inside the camera itself) such that setup can be done with a simple laptop interface.
Power-Trac (Servo Robot Inc)
It is a fully integrated system complete with laser camera, control unit, and software.
It offers automatic joint tracking and real-time trajectory control of the welding torch.
There is an option for an inspection module for quality control of the welds.
It is immune to the arc process like spatter and can withstand radiated heat.
The system is unaffected by ambient lighting conditions and can track all weldable materials.
The system offers true 3D laser measurements of joint geometry dimensions.
The high-speed digital laser sensor makes fast and reliable joint recognition possible.
The system is suitable for high-speed welding processes like tandem gas metal arc welding and laser hybrid welding.
The system has a direct interface with most brands of robot by advanced communication protocol on a serial or Ethernet link.
A large joint library is included, which allows almost any weld seam on any weldable material to be tracked and measured geometrically.
The adaptive welding module can adjust for joint geometry variability for optimization of the size of the weld and thus elimination of defects and reduced over-weld.
Laser Pilot (Meta Vision Systems Ltd.)
Laser Pilot MTF
Laser Pilot MTF is a seam finder and can be used in robotic welding applications which involve a series of short welds, as commonly found in the automotive industry, that do not require real-time tracking, although correct placement of the weld torch in the beginning of the weld is needed. MTF uses a standard interface for communication to the robot controller.
Laser Pilot MTR
Laser Pilot MTR is a seam tracker and available with interfacing with various leading robot manufacturers’ products. In addition to the seam-finding function, it can track seams in real time while welding (Meta Vision Systems Ltd 2006).
Circular Scanning System Weld-Sensor
ABB Weldguide III
Weldguide III is a through-the-arc seam-tracking sensor developed by ABB that uses two external sensors for the welding current and arc voltage. It has a measurement capacity at 25,000 Hz for quick and accurate path corrections and can be integrated with various transfer modes, like spray-arc, short-arc, and pulsed-arc GMAW.
A practical case: MARWIN
Currently available welding technologies such as manual welding and welding robots have several drawbacks. Manual welding is time-consuming, while existing robot are not efficient enough for manufacturing small batch-sized products but they also often face discrepancies when reprogramming is necessary. This reprogramming is also extremely time-consuming.
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 designed 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).
Robotics and sensors, together with their associated control systems have become important elements in industrial 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, sensors, 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 unstructured 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 systems, there are some issues associated with robotic welding 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 features 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.
The use of sensors is not new in this field, and sensors have successfully been used for seam tracking for more than 20 years in robotic arc welding. Basically, two different principles are used, through-arc sensing and optical sensors. Through-arc sensing uses the arc itself and requires a small weaving motion of the weld torch. Optical sensors are often based on a scanning laser light and triangulation to measure the distance to the weld joint. Both methods have some characteristic features that make them more suitable in certain situations. It should be noted that the through-arc sensing technique is rather inexpensive in comparison with an optical seam tracker. The principal types of industrial arc-welding sensors that have been employed are optical and arc sensors. If the arc sensing has been dominant till the 1980s, the trend nowadays is focused on optical improvement for intelligent programming as well as intelligent sensors.
Many sensors for seam tracking and seam finding are available in the market. The nature of the work defines the suitability of a particular type of sensor. However, due to an acceptable level of accuracy and reasonable cost, vision-based sensors are mostly used for seam tracking in most robotic weld applications, apart from through-the-arc sensing.
The research-based project MARWIN presented a semi-autonomous robotic weld system in which vision sensors scan the work piece assembly in 3D using structured light, which is compared to the CAD drawing to calculate the robot trajectory and weld parameters from an inbuilt database. This approach eliminates the necessity of tedious programming for robotic and welding parameters for each individual work part and the role of the user is limited to high-level specification of the welding task and confirmation and/or modification if required. SMEs with small production volumes and varied workpieces stand to benefit greatly from such semi-autonomous robotic welding.
Until recently, most robot programs were only taught through the robot teach pendant, which required the robot system to be out of production. Now, programmers are using offline program tools to teach the robot movements. After transferring the program to the robot controller, they use the robot teach pendant to refine the program positions. This greatly improves the productivity of the robot system. But still, calibration is needed between the model and the real work cell. The trend is the development of more intelligent programming, by use of sensors with the ability to scan the workpiece and working environment with high accuracy.
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