Introduction
Background
Motivation
Objective
Outline of the thesis
Literature Survey
Process monitoring for laser welding
- In-process monitoring
- Post-process monitoring
Sibillano et al. (Sibillano et al., 2005) investigated the dynamics of the plasma plume through an optical spectroscopic approach. Zhang et al., 1996) also measured the geometric appearance of the weld pool using the vision camera system.
Weld quality monitoring using sensor signals
- Univariate approaches
- Multi-sensor approaches
- Uncertainty consideration
Park et al. (Park et al., 2002) developed a real-time evaluation system for laser welding quality. A slopeCoeff controls and adjusts the proposed probability assignment function by changing the slope of the graph. A simple comparison of the probability assignment function with respect to the different slope coefficients is illustrated in Figure 4-8.
Based on the thresholds and basic probability assignment functions, the probability of the normal state is determined with the test data. In the case of the weld defect detection regime, the proportion of actual states (normal and abnormal) is greatly distorted. The reflection signal was closely related to the temperature of the laser source.
The result implies that the fusion of the reliable sensors increased the accuracy of defect detection. We defined the problem or challenging problems on the defect detection of the multisensory system.
Laser Welding Quality
Mechanical Properties
Ma et al. (Ma et al., 2014) investigated the tensile strength of the hardened zone (fusion zone and the grain-coarsened heat-affected zone (HAZ)), the softened zone (subcritical HAZ) and the base material under the condition of a high strength dual phase ( DP) 980 steel lap joint. They compared the experimental result of the numerical prediction of von Mises equivalent strain concentrations with failure modes, and the comparison shows reasonable agreement. Chen et al. (Chen et al., 2009) evaluated the welding of the lap joint configuration of hot-dip galvanized steel sheets by means of an optical scanning and electron microscopy with the tensile/hardness tests.
They found that the small increase in hardness in the “connection hole” and the “vent hole” is due to the absence of gas porosity. The welds produced using the “vent” configuration also have a higher tensile strength than the regular joint gap configuration. Mei et al. (Mei et al., 2013) compared two types of laser systems in terms of hardness and tensile strength, as well as surface formation and cross-sectional weld shape.
Mei et al. (Mei et al., 2009) studied the effects of process parameters such as laser power, welding. The experimental results showed that both the tensile strength and the microhardness of the welded joints are higher than that of the base metal under the selected conditions.
Visual Inspection
- Destructive test
- Non-destructive test
From the research, the variation of weld seam could be used as an indirect measure for the estimation of the tensile strength. Furthermore, Eriksson and Kaplan (Eriksson and Kaplan, 2009) investigated the comparison of the surface defect and its radiation signal.
The quality measure of laser welding
It illustrates how the coefficient adjusts and controls the slope of the probability assignment functions. The weighted summation of the two probability information (the estimated probability density function and the proposed probability assignment function) was used to assign the probability of the normal state. In this case, precision, recall and F-score are suitable indices for the performance of the detection method (Patcha and Park, 2007).
Recall, on the other hand, is a measure of how much of the normal state is correctly perceived. Figure 4-21 shows the response surface of the highest F-scores by weight and confidence interval. In the case of the 0.6 and 0.7 confidence interval, the F-score increased as the weight increased to 0.7.
Furthermore, the response surface of the highest F-score with respect to weight and confidence interval was constructed. For the production environment, we developed another version of the software, called LaserWel, as shown in Figure 5-8.
Weld Defect Detection via Sensor Fusion under Uncertainty
Problem statement
To improve the accuracy of defect detection performance, laser weld monitoring systems usually use rich information from multiple sensors, such as plasma, temperature and back-reflection signals. We need to specify the sources of uncertainty, especially regarding the commercially available monitoring system. First, the final decision or classification of the target status cannot be achieved simply because the monitoring system consists of multiple sensors, each of which can indicate a different welding quality status.
Second, all sensor signals are also within the tolerance range, and thus, the interpretation of each sensor decision is simple to classify or set it as the same result, the actual fault may have occurred in a practical environment. The temperature signal has bounded the lower threshold, which means that all temperature sensor data points are within the thresholds. As shown in Figure 4-3, specific patterns can be derived from the various sensor signals.
How we can divide a signal into several pieces of segments, codify the segmented pieces and interpret the row of the codified segment will be the approaches to the solution of this problem. In this research, we focused on the first and second problems by applying evidence theory and modified probability assignment function to detect the welding defect from the multiple sensory information.
Estimation of a probability distribution function for sensor data
After the estimation of the probability distribution function for each sensor was performed, the statistical thresholds were generated by the distribution.
Proposed probability assignment
- The Dempster-Shafer theory
- The thresholds adaptive probability assignment function
Note that the empty set (∅) must be the probability of 0, which implies that one of the conclusions must be true. It is understood to be a measure of confidence devoted precisely to hypothesis A. As a result, uncertainty, more specifically ignorance, regarding A is represented by the difference of the two measures; 𝑃𝑙𝑠(𝐴) − 𝐵𝑒𝑙(𝐴).
We defined a modified version of the double sigmoid function (equations (8) and (9)) to control the slope of the probability function for step changes around thresholds. As the difference between the upper and lower control limits increases, the slopeCoeff becomes larger, resulting in a steeper slope of the probability function near the upper and lower control limits. A graphical illustration of the probability assignment function with thresholds (in this case of the plasma signal, upper control limit: 23.4 V, lower control limit: 11.7 V and midpoint: 17.3 V) is shown in Figure 4-9 (b) .
We further improved the proposed probability assignment function by adding the information about the estimated probability density function from Chapter 4.3.1. By applying the weight, the probability assignment function contains richer information regarding bias of the original training data.
Multi-sensor evidence fusion by the Dempster’s combination rule
In this case of Θ = {𝐻, ¬𝐻}, the entire power set of the discrimination framework, namely {∅, 𝐻, ¬𝐻, {𝐻, ¬𝐻}}, is mapped in the form of two binary digits for the bitwise conjunctive operation (AND) calculation for the combination procedure. A set of all possible subsets of assigned basic probability (mass function) is converted into a vector form to calculate the vector inner product. The status of the current weld result is classified by testing whether the combined belief is greater than a certain threshold (user selected).
A whole procedure for the defect detection system including the combination of each evidence (basic probability mass value) is illustrated as follows in Figure 4-12. The normal state data from three sources is preprocessed and trained as a form of a threshold. The probability vectors come from the different information sources, are combined by Dempster's combination rule.
Problem sets
- Experimental setup
- Experimental result and discussion
This characteristic of data set makes it difficult to determine the performance of the proposed detection method. The detection threshold for the final decision and classification is also one of the essential parameters. First, we assembled the prototype of the sensor system using the products of Hamamatsu optics.
The performance of the developed detection method is evaluated by statistical comparison of visual inspection result (actual defect) and detection result. Kaplan, “Challenges for interpreting the electromagnetic feedback from laser welding,” Optics and Lasers in Engineering, vol. Yoo, “A study on the digital filter and wavelet transformation of monitoring for laser welding”, Journal of the Korean Society for Precision Engineering, vol.
Ceglarek, "Correlation analysis of the variation of weld seam and tensile strength in laser welding of galvanized steel", Optics and Lasers in Engineering, vol. Kim, “A fault detection method of laser welding based on PDF estimation and Dempster-Shafer theory”, Proceedings of the Society of CAD/CAM Engineers Conference, 2014, pp.
LaserWel: A Defect Detection and Monitoring System for Laser Welding
Optical system
The system consists of a 2.5-axis gantry-based automatic welding system with a laser beam of IPG YLS-2000 fiber laser as a laser source. The laser has a maximum output discharge of 2 kW in the TEM01 mode of laser radiation. The light signal is collected next to the laser source called coaxial acquisition system.
Based on the first prototype, we configured the improved version of the sensor system using Thorlab products. The intensity of the input light source was reduced to 25% of the original light source to avoid clipping effects. The IR range including the reflected laser beam can pass through the mirror, while the light from UV ranges reflects at an angle of 45 degrees.
The reflected laser beam (1064nm), which is the same as the wavelength of the fiber laser source, should be removed to obtain accurate information about the weld pool temperature and fluctuations. To do that, we installed the long-pass filter of 1100nm, which can block the wavelengths of back-reflected laser (1064nm) in front of the IR photodiode sensor.
Software development
For this purpose, attention should be focused on defect detection in the quality estimation of laser welding. The defect detection method based on Dempster-Shafer theory can be advantageously used to overcome the limitations in monitoring laser welding process. Finally, the laser welding defect detection module, constructed response surface and online part gap assessment module will be integrated as a closed-loop laser welding process controller.
Ceglarek, "Effects of laser welding direction on joint quality for non-uniform piece-to-piece gaps," Metals, vol. Hashmi, “Effect of laser welding parameters on heat input and weld bead profile,” Journal of Materials Processing Technology, vol. Perán, “Detection of laser welding defects in the automotive industry based on radiation and spectroscopic measurements,” The International Journal of Advanced Manufacturing Technology, vol.
Zhou, “Application of artificial neural network in laser welding defect diagnosis,” Journal of Materials Processing Technology, vol. Zhou, “Real-time monitoring of laser welding based on multiple sensors,” Control and Decision Conference, pp.
Conclusion and Future Research
Experiments for the part-to-part gap size
Software registration
