**3. Methods or techniques used for modeling and control of the geometry of weld bead**

The welding process is characterized as multivariable, nonlinear, and timevarying, with stochastic behavior and having a strong coupling among welding parameters. For this reason, it is very difficult to find a reliable mathematical model to design an effective control scheme by conventional modeling and control methods.

Due to these characteristics, the use of adaptive techniques has spread in the last decades with favorable results, only overcome by a proportional-integral-derivative controller. The adaptive control has been implemented in some researchers to cope with the problem of the high dependence of process parameters to its operating condition. The main drawback of this method is that it requires online estimation or tuning of the parameters, which is usually a time-consuming operation. The single implementation of PID and low computational resources make it still the most used, as in the rest of industrial applications. A graphic summary is shown in **Figure 9**, and **Table 1** shows the document references analyzed.

Neural networks, fuzzy methods, and their combinations also stand out. Note that the magnificent behavior of the neural network can be clouded by a slow convergence because of the excessive quantity of neurons or hidden layers. Many research efforts use this approach but neglected the need for the quick response of the control system.

Statistical methods, such as the classic autoregressive moving-average and expert systems, are represented too. Other less used techniques include state space, model-free adaptive, first- and second-order model, support vector machine, and finite elements.

### **3.1 Some scientific research about the geometry control in arc welding processes**

Developments in the field of automatic control of the geometry in the arc welding process have been intense in the last four decades. The most representative

#### **Figure 9.**

*Graphic summary about the main methods or techniques used for control of the weld bead geometry, including the quantity of references and the percentage they represent in the total papers consulted.*


parameters of welding speed (*V*), current (*I*), and arc length (*L*). Another neural network estimates the width (*W*) and weld depth penetration (*P*). Two independently closed-loop PIs adjust continuously the start parameters in function of weld bead width and penetration control error. The experimental results are satisfactory. In [72], a similar structure was proposed for the control of weld bead width, without the estimator and using a fuzzy controller. The same philosophy is used in

*Control loop proposed for Andersen [24] when the initial set point conditions are defined by neural network*

Zhang et al. [74] developed an adaptive control of full penetration for GTAW processes, based on the generalized predictive control (GPC) algorithm presented by Clarke [75–77]. The controlled variables are the width and reinforcement of the weld bead, measured by a vision system and a laser stripe. The control or manipulated variables are the welding current and arc length. The sampling interval of the control system was 0.5 s. The process has been described by a moving-average model with a predictive decoupling algorithm. The system is not controlling the penetration directly but has a satisfactory performance in the weld quality control. Brown et al. [26] developed a control loop with a PID controller and an adaptive dead-time compensator for GMAW processes in a horizontal welding position. The controlled variable is the weld pool width and the welding speed is manipulated.

A dynamic model, based on neural network, was designed in [53] to predict the maximum backside width in pulsed GTAW processes. Also, a self-learning fuzzy neural network controller was developed for controlling the maximum backside width, and the fuzzy rules were modified online. Another intelligent multivariable controller, type double-input and double-output (DIDO), based on a neuro-fuzzy algorithm and combined with an expert system, was developed to control the maximum backside width and the shape of the weld pool. Experimental results

In [55], a neuro-fuzzy controller was designed for the GTAW process. This method overcomes the dependency of human experts for the generation of fuzzy rules and the non-adaptive fuzzy set. The adaptation of membership function and

[54] for width and reinforcement control in independent control loops.

The simulation results show an acceptable response.

*and the penetration are estimated. Adapted from [73].*

*Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

**Figure 10.**

**63**

showed the best behavior using the DIDO intelligent controller.

the fuzzy rule self-organization are carried out by the self-learning and

#### **Table 1.**

*References classified according to the methods or techniques used for control of the weld bead geometry.*

research efforts found in the literature are in this area in the last two decades. In this section, some relevant works are discussed in chronological order.

In 1986, Nied and Baheti [46] registered a patent in which a robotic welding system has an integrated vision sensor to obtain images and analyze the welding scene in real-time. It used an adaptive feedback control to assure the full penetration, the weld puddle area, and the maximum width in the TIG process. The adaptive control system determines a puddle geometry error and uses the nominal welding current to change the heat input to the weld pool, regulating the combination of puddle area and width. Arc voltage is modulated to reflect changes in welding current and maintain a constant arc length.

In 1989 Edmund R. Bangs and others [38] registered a patent that described a system for real-time welding adaptive control using infrared images, artificial intelligence (expert system), and distributed processors.

Already in 1990, Andersen designed a control system for the GMAW process [24]. As shown in **Figure 10**, a neural network set point selector defines the start *Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

#### **Figure 10.**

*Control loop proposed for Andersen [24] when the initial set point conditions are defined by neural network and the penetration are estimated. Adapted from [73].*

parameters of welding speed (*V*), current (*I*), and arc length (*L*). Another neural network estimates the width (*W*) and weld depth penetration (*P*). Two independently closed-loop PIs adjust continuously the start parameters in function of weld bead width and penetration control error. The experimental results are satisfactory. In [72], a similar structure was proposed for the control of weld bead width, without the estimator and using a fuzzy controller. The same philosophy is used in [54] for width and reinforcement control in independent control loops.

Zhang et al. [74] developed an adaptive control of full penetration for GTAW processes, based on the generalized predictive control (GPC) algorithm presented by Clarke [75–77]. The controlled variables are the width and reinforcement of the weld bead, measured by a vision system and a laser stripe. The control or manipulated variables are the welding current and arc length. The sampling interval of the control system was 0.5 s. The process has been described by a moving-average model with a predictive decoupling algorithm. The system is not controlling the penetration directly but has a satisfactory performance in the weld quality control.

Brown et al. [26] developed a control loop with a PID controller and an adaptive dead-time compensator for GMAW processes in a horizontal welding position. The controlled variable is the weld pool width and the welding speed is manipulated. The simulation results show an acceptable response.

A dynamic model, based on neural network, was designed in [53] to predict the maximum backside width in pulsed GTAW processes. Also, a self-learning fuzzy neural network controller was developed for controlling the maximum backside width, and the fuzzy rules were modified online. Another intelligent multivariable controller, type double-input and double-output (DIDO), based on a neuro-fuzzy algorithm and combined with an expert system, was developed to control the maximum backside width and the shape of the weld pool. Experimental results showed the best behavior using the DIDO intelligent controller.

In [55], a neuro-fuzzy controller was designed for the GTAW process. This method overcomes the dependency of human experts for the generation of fuzzy rules and the non-adaptive fuzzy set. The adaptation of membership function and the fuzzy rule self-organization are carried out by the self-learning and

research efforts found in the literature are in this area in the last two decades. In this

*References classified according to the methods or techniques used for control of the weld bead geometry.*

*Graphic summary about the main methods or techniques used for control of the weld bead geometry, including*

*the quantity of references and the percentage they represent in the total papers consulted.*

**Control technique or control model References** On/off, classic PID, or PID combined with other method [2, 24–37] Adaptive [26, 29, 38–50] Neural network and fuzzy logic or neuro-fuzzy [3, 4, 51–60] Neural network [27, 61–63] Fuzzy logic [39, 64, 65] Autoregressive moving average (ARMA or similar) [29, 66, 67] Expert systems [38, 53, 40] State space [1, 68] Model-free adaptive [68, 69] First- or second-order model [70, 71] Support vector machine [64] Finite elements [52]

In 1986, Nied and Baheti [46] registered a patent in which a robotic welding system has an integrated vision sensor to obtain images and analyze the welding scene in real-time. It used an adaptive feedback control to assure the full penetration, the weld puddle area, and the maximum width in the TIG process. The adaptive control system determines a puddle geometry error and uses the nominal welding current to change the heat input to the weld pool, regulating the combination of puddle area and width. Arc voltage is modulated to reflect changes in

In 1989 Edmund R. Bangs and others [38] registered a patent that described a system for real-time welding adaptive control using infrared images, artificial intel-

Already in 1990, Andersen designed a control system for the GMAW process [24]. As shown in **Figure 10**, a neural network set point selector defines the start

section, some relevant works are discussed in chronological order.

welding current and maintain a constant arc length.

**Figure 9.**

*Welding - Modern Topics*

**Table 1.**

**62**

ligence (expert system), and distributed processors.

competitiveness of the neural network with three hidden layers. The simulation test got promising behavior.

In [31], the difficulty to tune the PI controller parameters to achieve the desired performance, across the entire range of the process operation, is shown. Therefore, the design and implementation of more complex controllers are required to obtain

A method developed by Casalino in [52] defines an automated methodology for selecting the weld process parameters based on artificial intelligence. While there are many methods available to improve the reliability of traditional open-loop control schemes, these can only be used with a particular welding power source and

Lu et al. [43] developed a non-transferred plasma charge sensor-based vision system to measure the depth of the weld pool surface in orbital GTAW processes. The sensor measures the welding voltage when the welding current is off and calculates the arc distance by an inversely linear relation. An insulated gate bipolar transistor (IGBT) power module is utilized to temporarily switch off the main power supply. During this period, the large arc pressure associated with the main arc is not present, the depth of the weld pool surface decreases, and the sensor output increases, obtaining the arc measurement. An adaptive interval algorithm controls the depth of the weld pool surface, regulating the main-arc-on period. Four experiments were

Smith et al. [34] use two independent PIs to control a pulsed TIG process in a horizontal position. Both controllers use the control error of the top face weld pool width as input. The independent outputs are the welding current and the wire feed speed. The active adjustment of the welding current and the wire feed speed allows compensating variations on the weld pool size. A camera and image processing algorithm measures the top face weld pool width. The controllers and image processing algorithms run concurrently on a PC. The controllers use CAN serial communication protocol to adjust the outputs in two distributed actuator nodes, based on a microcontroller system. A step-change in plate thickness was used to test the controller system. The experimental results produced welds with a more con-

In [63], a three-dimensional vision system is used for obtaining the geometrical parameters of the weld joint. A closed-loop neuronal-network controller is developed to control the width and depth of the weld joint, by regulating the welding voltage, welding speed, and wire feed speed. The neuronal model has two hidden layers with six and four neurons, respectively, as shown in **Figure 12**. The experimental results, using the neural network learning data and the error range of width

Fuzzy and PID controllers are employed in [65] to control the geometry stability in a GTAW process, by regulating the welding current (*ΔI*) and wire feed rate (*v*). A real-time image analysis algorithm was developed for seam tracking and seam gap measuring using passive vision. The control was applied in a teach and playback robotic welding system, which helps the robot track the seam with the gap variation. The quality of the products obtained reached the standard of first-order welding seam (Q J2698-95) in terms of dimensions and soundness, which was

executed under different conditions and show a satisfactory response.

sistent profile when faced with variations in process conditions.

verified with an X-ray inspection. **Figure 13** shows the block diagram.

behavior to control this relatively complex process.

In [78] two SISO subsystems are developed to control a double-electrode GMAW process. The control structure is selected for convenient implementation and design. One system controls the welding current of the main torch by manipulating the wire feed speed. The other system controls the welding voltage of the bypass torch by manipulating the welding current. Two interval models have been obtained, based on experimental data from step-response experiments under different manufacturing conditions. These simple controllers show an acceptable

better control of the welding process.

*Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

a specific welding arrangement.

and depth, are within 3%.

**65**

Chin [28] developed a system for infrared image sense and PID control of the SAW process. Several tests were executed with diverse conditions in the control variables. Similarly, an infrared point sensor (pyrometer) is used in [36] to estimate the weld bead depth in GTAW and SAW processes. The penetration is controlled indirectly (using the infrared emission) by a PI regulator, which changes the welding current in the GTAW process and the welding voltage and welding speed in the SAW process. A satisfactory result is obtained under laboratory conditions.

Moon and Beattie [45] developed an adaptive fill control for multi-torch multipass SAW processes. The system measures the joint geometry with laser seam tracking and calculates the total area of the joint, computing by numerical integration based on the actual joint profile. With the area ratio of the joint, the welding current/voltage combination is obtained, and the welding speed is adjusted inversely in proportion to the area to be filled. The control significantly improves weld bead quality. This technology has been used in the manufacture of longitudinal and spiral pipes and pressure vessels.

In [66], two simultaneous but independent control loops are used. The first loop monitors the temperature with an infrared camera and controls the trajectory of the torch. The second loop monitors the geometric profile with the laser stripe and manipulates the welding speed and the wire feed speed. A variable delay Smith predictor is used for reducing the dead-time effect of laser strip sensor, as shown in **Figure 11**. The author tested a SISO closed loop with a PI controller and a MIMO GOSA. The last one obtains the best result.

An H-infinity robust control system in [1] is proposed to control the length and width of the weld pool, manipulating welding current and weld speed. The simulation shows an effective robust method to control processes with large uncertainties in the dynamics. However, the complexity of the H-infinity control algorithm can make the implementation of embedded devices difficult. Also, it needs an effective description of the uncertainties of the welding process dynamics, and it is difficult under conditions of the real processes.

A weld pool imaging system, with a LaserStrobe high shutter speed camera, is used in [58] to obtain contrasting images and eliminate the arc interference. Image processing algorithms, based on edge detection and connectivity analysis, extract information about the weld pool length and width online. A neuro-fuzzy controller, based on human experience and experimental results, manipulate the welding voltage and speed in real-time based on weld pool dimensions. A welding speed closedloop control is used to reach the set point of the weld pool geometry. The simulation results are satisfactory, but the response may be slow due to the time required for image processing and fuzzy calculation.

**Figure 11.** *SISO (a) and DIDO (b) control loops that employ infrared information. Adapted from [66].*

#### *Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

competitiveness of the neural network with three hidden layers. The simulation test

Chin [28] developed a system for infrared image sense and PID control of the SAW process. Several tests were executed with diverse conditions in the control variables. Similarly, an infrared point sensor (pyrometer) is used in [36] to estimate the weld bead depth in GTAW and SAW processes. The penetration is controlled indirectly (using the infrared emission) by a PI regulator, which changes the welding current in the GTAW process and the welding voltage and welding speed in the SAW process. A satisfactory result is obtained under laboratory conditions. Moon and Beattie [45] developed an adaptive fill control for multi-torch multi-

pass SAW processes. The system measures the joint geometry with laser seam tracking and calculates the total area of the joint, computing by numerical integration based on the actual joint profile. With the area ratio of the joint, the welding current/voltage combination is obtained, and the welding speed is adjusted inversely in proportion to the area to be filled. The control significantly improves weld bead quality. This technology has been used in the manufacture of longitudi-

In [66], two simultaneous but independent control loops are used. The first loop monitors the temperature with an infrared camera and controls the trajectory of the torch. The second loop monitors the geometric profile with the laser stripe and manipulates the welding speed and the wire feed speed. A variable delay Smith predictor is used for reducing the dead-time effect of laser strip sensor, as shown in **Figure 11**. The author tested a SISO closed loop with a PI controller and a MIMO

An H-infinity robust control system in [1] is proposed to control the length and width of the weld pool, manipulating welding current and weld speed. The simulation shows an effective robust method to control processes with large uncertainties in the dynamics. However, the complexity of the H-infinity control algorithm can make the implementation of embedded devices difficult. Also, it needs an effective description of the uncertainties of the welding process dynamics, and it is difficult

A weld pool imaging system, with a LaserStrobe high shutter speed camera, is used in [58] to obtain contrasting images and eliminate the arc interference. Image processing algorithms, based on edge detection and connectivity analysis, extract information about the weld pool length and width online. A neuro-fuzzy controller, based on human experience and experimental results, manipulate the welding voltage and speed in real-time based on weld pool dimensions. A welding speed closedloop control is used to reach the set point of the weld pool geometry. The simulation results are satisfactory, but the response may be slow due to the time required for

*SISO (a) and DIDO (b) control loops that employ infrared information. Adapted from [66].*

got promising behavior.

*Welding - Modern Topics*

nal and spiral pipes and pressure vessels.

GOSA. The last one obtains the best result.

under conditions of the real processes.

image processing and fuzzy calculation.

**Figure 11.**

**64**

In [31], the difficulty to tune the PI controller parameters to achieve the desired performance, across the entire range of the process operation, is shown. Therefore, the design and implementation of more complex controllers are required to obtain better control of the welding process.

A method developed by Casalino in [52] defines an automated methodology for selecting the weld process parameters based on artificial intelligence. While there are many methods available to improve the reliability of traditional open-loop control schemes, these can only be used with a particular welding power source and a specific welding arrangement.

Lu et al. [43] developed a non-transferred plasma charge sensor-based vision system to measure the depth of the weld pool surface in orbital GTAW processes. The sensor measures the welding voltage when the welding current is off and calculates the arc distance by an inversely linear relation. An insulated gate bipolar transistor (IGBT) power module is utilized to temporarily switch off the main power supply. During this period, the large arc pressure associated with the main arc is not present, the depth of the weld pool surface decreases, and the sensor output increases, obtaining the arc measurement. An adaptive interval algorithm controls the depth of the weld pool surface, regulating the main-arc-on period. Four experiments were executed under different conditions and show a satisfactory response.

Smith et al. [34] use two independent PIs to control a pulsed TIG process in a horizontal position. Both controllers use the control error of the top face weld pool width as input. The independent outputs are the welding current and the wire feed speed. The active adjustment of the welding current and the wire feed speed allows compensating variations on the weld pool size. A camera and image processing algorithm measures the top face weld pool width. The controllers and image processing algorithms run concurrently on a PC. The controllers use CAN serial communication protocol to adjust the outputs in two distributed actuator nodes, based on a microcontroller system. A step-change in plate thickness was used to test the controller system. The experimental results produced welds with a more consistent profile when faced with variations in process conditions.

In [63], a three-dimensional vision system is used for obtaining the geometrical parameters of the weld joint. A closed-loop neuronal-network controller is developed to control the width and depth of the weld joint, by regulating the welding voltage, welding speed, and wire feed speed. The neuronal model has two hidden layers with six and four neurons, respectively, as shown in **Figure 12**. The experimental results, using the neural network learning data and the error range of width and depth, are within 3%.

Fuzzy and PID controllers are employed in [65] to control the geometry stability in a GTAW process, by regulating the welding current (*ΔI*) and wire feed rate (*v*). A real-time image analysis algorithm was developed for seam tracking and seam gap measuring using passive vision. The control was applied in a teach and playback robotic welding system, which helps the robot track the seam with the gap variation. The quality of the products obtained reached the standard of first-order welding seam (Q J2698-95) in terms of dimensions and soundness, which was verified with an X-ray inspection. **Figure 13** shows the block diagram.

In [78] two SISO subsystems are developed to control a double-electrode GMAW process. The control structure is selected for convenient implementation and design. One system controls the welding current of the main torch by manipulating the wire feed speed. The other system controls the welding voltage of the bypass torch by manipulating the welding current. Two interval models have been obtained, based on experimental data from step-response experiments under different manufacturing conditions. These simple controllers show an acceptable behavior to control this relatively complex process.

Lü et al. [69] developed a MISO algorithm for the control of the width of the weld pool backside in the GTAW process. The vision sensing technology and model-free adaptive control (MFC) are used. The welding current and wire feed speed are selected as the manipulated variables, and the backside width of the weld pool is the controlled variable. The main difficulty was the availability of computational resources to maintain the control period and the image processing speed within acceptable values. It has the disadvantage of using complex optical systems

In [68] a space state model of the GMAW process is obtained to compare the behavior of three controllers: ARMarkov-PFC (based MPC controller), PI, and feedback linearization based on the PID (FL-PID). The controller outputs are the wire feed motor voltage and the welding voltage. The controlled variables are the welding current and voltage. The simulation results show that the ARMarkov-PFC outperformed other controllers from the viewpoints of the transient response, desired output tracking, and robustness against the process parameter uncertainties but require more computational resources. The FL-PID controller was sensitive to the process parameter variations, presence of noise and disturbance, and results in an improper performance. The PI controller produced an inappropriate transient response and inadequate interaction reduction despite good tracking performance,

for obtaining the image of the back- and the front side of the weld pool.

*Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

low sensitivity to parameter variations, and low computational resource

The main advantages of MPC over structured PID controllers are its ability to handle constraints, non-minimum phase processes, changes in system parameters (robust control), and its straightforward applicability to large, multivariable, or multiple-input multiple-output processes. Despite having many advantages, a noticeable drawback of an MPC is that it requires higher computation capability, as

The change in arc voltage during the peak current is used in [49] to estimate the weld penetration depth in the pulsed GMAW process. An adaptive interval model control system is implemented, but, contrary to the author's comment, the control

Lui et al. [60] developed a model-based predictive control for the orbital GTAW process. The control input is the backside weld bead width, and the outputs are the welding current and welding speed. A nonlinear neuro-fuzzy (ANFIS) model is utilized to estimate the backside weld bead width (related with weld depth penetration) using the front-side weld pool characteristic. **Figure 14** shows a block

In [67] the GMAW LAM process is modeled using the recursive least squares algorithm for system identification. An image processing algorithm is employed for obtaining the nozzle to top surface distance. An adaptive controller adjusts the deposition rate and keeps the nozzle to top surface distance constant. The precision

A segmented self-adaptive PID controller was developed in [30] for controlling the arc length and monitoring the arc sound signal in the pulsed GTAW process for the flat and arched plate. The experiments show that the controller has acceptable

Lui and Zhang [4] developed a machine-human cooperative control scheme to perform welder teleoperation experiments in the orbital GTAW process. They developed an ANFIS model of the human welder's adjustment on the welding speed. The welder sees the weld pool image overlaid with an assistant visual signal and moves the virtual welding torch accordingly. The robot follows the welder's movement and completes the welding task. The experimental data is used to obtain the model. Later, they transfer this model to the welding robot controller to perform

range of the control system is limited within 0.5 mm.

requirements.

is shown in [79].

accuracy is not good.

diagram of this approach.

accuracy.

**67**

**Figure 12.**

*Neural network to determine welding parameters. Adapted from [63].*

**Figure 13.**

*Block diagram of the geometry stability controller for GTAW process, developed in [65].*

Chen solved the full penetration detection, in orbital the GTAW process, with a vision system over the topside pool and a neural network model for estimating the backside weld bead width in [39]. The neural network has 17 neurons in the input layer, 40 in the hidden layer, and 1 in the output. An adaptive controller receives the backside weld bead width, estimated by the neural network, and regulates the peak current. A fuzzy controller received the gap state and controlled the wire feed speed. The experimental results, examined in X-ray, have shown a uniform backside of the weld bead.

An adaptive inverse control based on support vector machine-based fuzzy rules acquisition system is proposed by [64] for pulsed GTAW process. This method extracts the control rules automatically from the process data, using an adaptive learning algorithm and a support vector machine to adjust the fuzzy rules. The controlled variable is the backside weld width, and the manipulated variable is the peak current of the pulse. A double-side visual sensor system captures the topside and backside images of the weld pool simultaneously. The data for model identifying is obtained experimentally. The control is simulated and shows satisfactory results.

#### *Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

Lü et al. [69] developed a MISO algorithm for the control of the width of the weld pool backside in the GTAW process. The vision sensing technology and model-free adaptive control (MFC) are used. The welding current and wire feed speed are selected as the manipulated variables, and the backside width of the weld pool is the controlled variable. The main difficulty was the availability of computational resources to maintain the control period and the image processing speed within acceptable values. It has the disadvantage of using complex optical systems for obtaining the image of the back- and the front side of the weld pool.

In [68] a space state model of the GMAW process is obtained to compare the behavior of three controllers: ARMarkov-PFC (based MPC controller), PI, and feedback linearization based on the PID (FL-PID). The controller outputs are the wire feed motor voltage and the welding voltage. The controlled variables are the welding current and voltage. The simulation results show that the ARMarkov-PFC outperformed other controllers from the viewpoints of the transient response, desired output tracking, and robustness against the process parameter uncertainties but require more computational resources. The FL-PID controller was sensitive to the process parameter variations, presence of noise and disturbance, and results in an improper performance. The PI controller produced an inappropriate transient response and inadequate interaction reduction despite good tracking performance, low sensitivity to parameter variations, and low computational resource requirements.

The main advantages of MPC over structured PID controllers are its ability to handle constraints, non-minimum phase processes, changes in system parameters (robust control), and its straightforward applicability to large, multivariable, or multiple-input multiple-output processes. Despite having many advantages, a noticeable drawback of an MPC is that it requires higher computation capability, as is shown in [79].

The change in arc voltage during the peak current is used in [49] to estimate the weld penetration depth in the pulsed GMAW process. An adaptive interval model control system is implemented, but, contrary to the author's comment, the control accuracy is not good.

Lui et al. [60] developed a model-based predictive control for the orbital GTAW process. The control input is the backside weld bead width, and the outputs are the welding current and welding speed. A nonlinear neuro-fuzzy (ANFIS) model is utilized to estimate the backside weld bead width (related with weld depth penetration) using the front-side weld pool characteristic. **Figure 14** shows a block diagram of this approach.

In [67] the GMAW LAM process is modeled using the recursive least squares algorithm for system identification. An image processing algorithm is employed for obtaining the nozzle to top surface distance. An adaptive controller adjusts the deposition rate and keeps the nozzle to top surface distance constant. The precision range of the control system is limited within 0.5 mm.

A segmented self-adaptive PID controller was developed in [30] for controlling the arc length and monitoring the arc sound signal in the pulsed GTAW process for the flat and arched plate. The experiments show that the controller has acceptable accuracy.

Lui and Zhang [4] developed a machine-human cooperative control scheme to perform welder teleoperation experiments in the orbital GTAW process. They developed an ANFIS model of the human welder's adjustment on the welding speed. The welder sees the weld pool image overlaid with an assistant visual signal and moves the virtual welding torch accordingly. The robot follows the welder's movement and completes the welding task. The experimental data is used to obtain the model. Later, they transfer this model to the welding robot controller to perform

Chen solved the full penetration detection, in orbital the GTAW process, with a vision system over the topside pool and a neural network model for estimating the backside weld bead width in [39]. The neural network has 17 neurons in the input layer, 40 in the hidden layer, and 1 in the output. An adaptive controller receives the backside weld bead width, estimated by the neural network, and regulates the peak current. A fuzzy controller received the gap state and controlled the wire feed

An adaptive inverse control based on support vector machine-based fuzzy rules acquisition system is proposed by [64] for pulsed GTAW process. This method extracts the control rules automatically from the process data, using an adaptive learning algorithm and a support vector machine to adjust the fuzzy rules. The controlled variable is the backside weld width, and the manipulated variable is the peak current of the pulse. A double-side visual sensor system captures the topside and backside images of the weld pool simultaneously. The data for model identifying is obtained experimentally. The control is simulated and shows satisfactory

speed. The experimental results, examined in X-ray, have shown a uniform

*Block diagram of the geometry stability controller for GTAW process, developed in [65].*

*Neural network to determine welding parameters. Adapted from [63].*

backside of the weld bead.

results.

**66**

**Figure 13.**

**Figure 12.**

*Welding - Modern Topics*

and large random access memory (RAM) capacity, where you can run a standard operating system interconnected with the FPGA. These features and the small size, low power consumption, low heat dissipation, and reconfigurable architecture make it an ideal tool for monitoring and control systems with real-time requirements. For all these good reasons, we must pay special attention to these devices. The modern welding power sources are controlled by embedded systems. These systems provide communication, data acquisition, and control functions for different welding processes, but their most important specialization is the control of the electric arc, laser signal, and other methods to transfer the energy to the base material. This specialization permitted the substitution of big transformer and switches to select the welding parameters such as the voltage, current, and inductance, by a smaller transformer and high-frequency switching semiconductors governed by a microcontroller. With these changes it was possible to generate various types of waveforms on the output of the welding power source, improving the conventional processes and creating new processes and new control algorithms. In these systems, the embedded controller emulates the impedance of the old transformer and keeps the set points of welding voltage and current with more accuracy. The configuration of welding parameters, data acquisition, and set point changes can be made using a communication protocol, defined by the manufacturer. The integration with a supervisory or higher control system is possible using

*Graphical summary of the applications of embedded devices in welding processes found in the literature review.*

The literature review does not show an extensive application of FPGA to bead geometry control in the arc welding process. But other works have shown applications related with wire feed control [80], defect detection [81–83], and arc signal

Based on the literature review and the experimental experience about the control of the welding bead geometry, it is possible to observe the great complexity of the welding process and the many efforts to control it. The proposed solutions range from simple open-loop controllers to complex intelligent control algorithms, highlighting the legendary PID combined with other techniques, adaptive methods,

Despite the arduous research efforts, few of these algorithms are being applied in the industry, in some cases due to its complexity but others due to commercial interests and its cost of implementation. For these reasons, the control of welding

monitoring [84, 85]. A graphical summary is shown in **Figure 15**.

and the neural network and fuzzy algorithms.

this communication protocol.

**4. Conclusions**

**69**

**Figure 15.**

*Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

**Figure 14.** *Model-based predictive control to orbital GTAW process. Adapted from [60].*

automated welding. The controller receives the three-dimensional weld pool characteristic parameters (weld pool length, width, and convexity) and changes the welding speed.

In other similar work [3], a human intelligence model based on a neuro-fuzzy algorithm is proposed to implement an intelligent controller to maintain a full penetration manipulating the welding current. These works establish a method to rapidly transform human welder intelligence into welding robots by using threedimensional weld pool surface sense, fitting the human welder response to the information through a neuro-fuzzy model, and using the neuro-fuzzy model as a replacement for human intelligence in automatic systems. In previous works [56, 57], the skilled human welder response to the fluctuating three-dimensional weld pool surface is correlated and compared with a novice welder.

#### **3.2 Embedded devices in welding process control**

Embedded systems, especially the FPGA and system on chip (SoC), are used in a multitude of technological processes in various industries, covering hazardous areas such as medical, aerospace, and military or even the most common household appliances. With the increase in processing capabilities of these systems, based on microcontrollers and new processor generation, it is possible to obtain remarkably improved measurement and control systems with the use of advanced algorithms for processing information provided by the sensors. The parallel processing capabilities of the FPGA (into the SoC) allow lower execution times than in processors or microcontrollers. These capabilities are important to estimators based on neural networks (parallel execution) and to control systems in real-time that need to attend several sensors and actuators.

The FPGA has numerous digital inputs and outputs, with the possibility of adding several analogs and other peripherals. Many of them have a hard processor, with one or more cores and various peripherals for communication, video, sound,

*Automatic Control of the Weld Bead Geometry DOI: http://dx.doi.org/10.5772/intechopen.91914*

**Figure 15.** *Graphical summary of the applications of embedded devices in welding processes found in the literature review.*

and large random access memory (RAM) capacity, where you can run a standard operating system interconnected with the FPGA. These features and the small size, low power consumption, low heat dissipation, and reconfigurable architecture make it an ideal tool for monitoring and control systems with real-time requirements. For all these good reasons, we must pay special attention to these devices.

The modern welding power sources are controlled by embedded systems. These systems provide communication, data acquisition, and control functions for different welding processes, but their most important specialization is the control of the electric arc, laser signal, and other methods to transfer the energy to the base material. This specialization permitted the substitution of big transformer and switches to select the welding parameters such as the voltage, current, and inductance, by a smaller transformer and high-frequency switching semiconductors governed by a microcontroller. With these changes it was possible to generate various types of waveforms on the output of the welding power source, improving the conventional processes and creating new processes and new control algorithms.

In these systems, the embedded controller emulates the impedance of the old transformer and keeps the set points of welding voltage and current with more accuracy. The configuration of welding parameters, data acquisition, and set point changes can be made using a communication protocol, defined by the manufacturer. The integration with a supervisory or higher control system is possible using this communication protocol.

The literature review does not show an extensive application of FPGA to bead geometry control in the arc welding process. But other works have shown applications related with wire feed control [80], defect detection [81–83], and arc signal monitoring [84, 85]. A graphical summary is shown in **Figure 15**.
