**4. Conclusions**

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, and the neural network and fuzzy algorithms.

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

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

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

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

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,

weld pool surface is correlated and compared with a novice welder.

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

**3.2 Embedded devices in welding process control**

attend several sensors and actuators.

**68**

welding speed.

**Figure 14.**

*Welding - Modern Topics*

processes is an open topic for research and especially for the development of feasible solutions to be used in the industry.

Scientific research and the slow but continuous application of its results in the welding industry show a tendency for modeling and control of these processes to be carried out using methods of artificial intelligence. These methods, in addition to including classic artificial intelligence techniques, are incorporating bioinspired algorithms, deep learning techniques, big data and data mining for the analysis of the measurements, the adjustment of the controllers, and even the implementation of the controller itself.

**References**

1971. p. 7

[1] Jou M. A study on development of an H-infinity robust control system for arc welding. Journal of Manufacturing Systems. 2002;**21**(2):140-150

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

> [11] Ko CN, Wu CJ. A PSO-tuning method for design of fuzzy PID controllers. Journal of Vibration and

[12] Kumar V, Nakra BC, Mittal AP. A review on classical and fuzzy pid controllers. International Journal of Intelligent Control Systems. 2011;**16**(3):

[13] Bennett S. Development of the PID controller. IEEE Control Systems. 1993;

[14] Jantzen J. Tuning of fuzzy PID controllers. Fuzzy Information and Engineering. 1998;**871**(98-H):1-22

[15] Karasakal O, Guzelkaya M, Eksin I, Yesil E, Kumbasar T. Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration. Engineering Applications of Artificial Intelligence. 2013;**26**(1):184-197

[16] Kazemian HB. Comparative study of a learning fuzzy PID controller and a self-tuning controller. ISA Transactions.

[17] Liu WH, Xie Z. Design and simulation test of advanced secondary cooling control system of continuous casting based on fuzzy self-adaptive PID. Journal of Iron and Steel Research

International. 2011;**18**(1):26-30

[19] Wu Y, Jiang H, Zou M. The research on fuzzy PID control of the permanent magnet linear synchronous motor. Physics Procedia. 2012;**24**:

[18] Soyguder S, Karakose M, Alli H. Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system. Expert Systems with Applications. 2009;**36**(3 PART 1):

2001;**40**(3):245-253

4566-4573

1311-1318

Control. 2008;**14**(3):375-395

170-181

**13**(6):58-62

[2] Iceland WF, O'Dor ME. Weld Penetration Control. United States: North American Rockwell Corporation;

[4] Liu Y, Zhang Y. Iterative local ANFIS-based human welder intelligence modeling and control in pipe GTAW process: A data-driven approach. IEEE/ ASME Transactions on Mechatronics.

[5] Bestard GA. Sensor fusion and embedded devices to estimate and control the depth and width of the weld

bead in real time [PhD thesis]. Universidade de Brasília; 2017

[6] Smith CA, Corripio AB. Principles and Practice of Automatic Process Control. 2nd ed. New York: John Wiley;

[7] Scotty A, Ponomarev V. Soldagem MIG-MAG: melhor entendimento, melhor desempenho. São Paulo: Artibler

[8] You DY, Gao XD, Katayama S. Review of laser welding monitoring. Science and Technology of Welding and

[9] Bristol EH. On a new measure of interaction for multivariable process control. IEEE Transactions on

Automatic Control. 1966;**11**(1):133-134

[10] Astrom KJ, Eykhoff P. System identification—A survey. Automatica.

Joining. 2014;**19**(3):181-201

2015;**20**(3):1079-1088

Editora Ltda.; 2008

1971;**7**(2):123-162

**71**

1997

[3] Liu Y, Zhang W, Zhang Y. Dynamic neuro-fuzzy-based human intelligence modeling and control in GTAW. IEEE Transactions on Automation Science and Engineering. 2015;**12**(1):324-335

Undoubtedly, the current development of embedded systems and the small and smart sensors is allowing the implementation of many algorithms proposed decades ago and new algorithms that make extensive use of the calculation capabilities of these systems. The use of multivariable control and dynamic models of the process will be possible and will allow a notable improvement in the quality of the welds and the number of parts rejected in the production process.

But the advantages of these technologies will not be accepted and exploited efficiently without adequate training of the technical staff that directs and operates the industries. Many of these modeling and control techniques are still unknown or their advantages are poorly disclosed. This is a problem when it is compared in terms of ease of use and productivity against classical techniques with decades of use in the industry. In this sense, we try to contribute to the dissemination of this knowledge throughout this chapter.
