*Online Measurements in Welding Processes DOI: http://dx.doi.org/10.5772/intechopen.91771*

since 1971, but it is not until the 1980s that a greater interest in this subject was observed. Recently, in the last decade, the number of publications is increased.

The technological development and cost reduction of sensors and the need for welding process control in an industrial environment (robotic welding) stimulated

An example is the development of infrared sensors that since the 1970s were available but in the 1980s were given classified contracts by the US Department of Defense to Honeywell and Texas Instruments to develop uncooled infrared sensor technology. In 1992, the US Government de-classified this technology for commercial products, allowing the sale of their devices to foreign countries, but kept close the manufacturing technologies. In the next decade, several countries developed uncooled imaging systems [25] with a drastic reduction of uncooled array cost.

*Evolution of the number of publications about measurement and estimation of the weld bead geometry (adapted*

**Figure 20** shows this growing interest.

*Welding - Modern Topics*

**Figure 21.**

**94**

**Figure 20.**

*from [7]).*

*Evolution of techniques used to measure the weld bead geometry [7].*

the number of scientific works about these topics.

This evolution can be observed in **Figure 21** with a significant increase in scientific publications in this decade.

The estimation of geometry weld bead using acoustic signal and vision techniques shows more activity in the last 20 years because of the great development of cameras, audio systems, and digital signal processor devices with high speed and quality, little size, and low cost.

In **Table 1**, some commonly used methods to obtain measurements of welding processes online are compared. The comparison criteria are the cost of implementation and the accuracy of the method. Both criteria are evaluated as low, medium, or high. In the table, a blank cell indicates a method that is not used to measure a specific variable. Some methods are used in conjunction with other measurement methods to obtain or estimate the value of the variable. Various measurement methods can be combined using a sensor fusion technique.

The analysis techniques most used in the last century were regression models, least mean square algorithms, Kalman filter, and other statistical methods. In the last few years, the principal techniques used are the artificial neural network, fuzzy logic, and neuro-fuzzy. The intense development of image processing algorithms was observed in the last 20 years.

### **4. Conclusions**

The correct selection of measuring techniques and the use of sensor fusion algorithms, combined with indirect measurement techniques, can help to reduce the cost of welding production and increase productivity by the detection or prediction of many welding defects or set point deviations. These measurements allow online adjusting of the welding power source and robot parameters in a closed control loop. Online estimation of variables that cannot be measured improves control systems and reduces the number of parts rejected in the final quality inspection.

To take advantage of a modern welding power source, it is important to equip the monitoring and control system with serial communication capabilities. The modeling of estimators is a critical step to obtain an accurate measuring system, and dynamic models have a better representation of the welding process than static models due to thermal inertia of the process. Vision and thermographic measuring techniques, image processing, and neural network algorithms, despite consuming more computing resources, are the most used to estimate the weld bead geometry, and excellent results have been observed.

The research on sensor fusion algorithms is grown. Following this trend, in this work a novel modeling method that uses arc welding measurements and thermographic information to create a dynamic model to estimate the weld bead penetration is presented. This new approach obtains information about the amount and spatial distribution of the energy in the workpiece and uses only addition operations, simplifies calculations, and improves model accuracy. A satisfactory solution was shown to be applied in welding automatic control using computers or embedded devices.

**Author details**

**97**

Guillermo Alvarez Bestard

University of Brasilia, Brasilia, Brazil

*Online Measurements in Welding Processes DOI: http://dx.doi.org/10.5772/intechopen.91771*

provided the original work is properly cited.

\*Address all correspondence to: guillermo@unb.br

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Online Measurements in Welding Processes DOI: http://dx.doi.org/10.5772/intechopen.91771*

This evolution can be observed in **Figure 21** with a significant increase in scientific

The estimation of geometry weld bead using acoustic signal and vision techniques shows more activity in the last 20 years because of the great development of cameras, audio systems, and digital signal processor devices with high speed and

In **Table 1**, some commonly used methods to obtain measurements of welding processes online are compared. The comparison criteria are the cost of implementation and the accuracy of the method. Both criteria are evaluated as low, medium, or high. In the table, a blank cell indicates a method that is not used to measure a specific variable. Some methods are used in conjunction with other measurement methods to obtain or estimate the value of the variable. Various measurement

The analysis techniques most used in the last century were regression models, least mean square algorithms, Kalman filter, and other statistical methods. In the last few years, the principal techniques used are the artificial neural network, fuzzy logic, and neuro-fuzzy. The intense development of image processing algorithms

The correct selection of measuring techniques and the use of sensor fusion algorithms, combined with indirect measurement techniques, can help to reduce the cost of welding production and increase productivity by the detection or prediction of many welding defects or set point deviations. These measurements allow online adjusting of the welding power source and robot parameters in a closed control loop. Online estimation of variables that cannot be measured improves control systems and reduces the number of parts rejected in the final quality

To take advantage of a modern welding power source, it is important to equip the monitoring and control system with serial communication capabilities. The modeling of estimators is a critical step to obtain an accurate measuring system, and dynamic models have a better representation of the welding process than static models due to thermal inertia of the process. Vision and thermographic measuring techniques, image processing, and neural network algorithms, despite consuming more computing resources, are the most used to estimate the weld bead geometry,

The research on sensor fusion algorithms is grown. Following this trend, in this work a novel modeling method that uses arc welding measurements and thermographic information to create a dynamic model to estimate the weld bead penetration is presented. This new approach obtains information about the amount and spatial distribution of the energy in the workpiece and uses only addition operations, simplifies calculations, and improves model accuracy. A satisfactory solution was shown to be applied in welding automatic control using computers or embed-

methods can be combined using a sensor fusion technique.

publications in this decade.

*Welding - Modern Topics*

quality, little size, and low cost.

was observed in the last 20 years.

and excellent results have been observed.

**4. Conclusions**

inspection.

ded devices.

**96**
