Introduction
Background
Motivation
Objectives
Outline of the thesis
Literature Survey
Laser welding
- Laser
- Laser welding defects
- Tensile strength
- Part-to-part gap
- Laser power and welding speed
- Shielding gas
- Laser beam quality
Facilitate gas escape (ple part-to-p section information -Proper laser welding -Proper slope beam of ing. A report published by NIPPON Steel shows the correlation between weight loss and tensile shear strength of the laser welding of galvanized steel (Report also suggested a strong relationship between weight losses and surface area of porosity In general, the threshold value of part-to-part gap should be within ten percent of the thickness of upper part (Havrilla 2012).
In general, the hump effect facilitates the generation of depressions in one of the galvanized steels in a lap joint configuration of laser welding. Before starting the laser welding process, removing zinc coating from the weld area is also a possible solution to prevent part-to-part gaps (Graham et al. 1996). In the overlap joint configuration with 2.5 KW pulse CO2, laser welding of galvanized steel produces good weld quality without part-to-part gap, first claimed by Heyden et al. 1989) also consistent with good quality of laser welding can be achieved using pulsed Nd:YAG laser in galvanized steel without part-to-part gaps.
But the main disadvantage of lower power pulsed laser welding is that the welding speed cannot be more than 2.4mm/s, which is not possible in mass production for any industry. General Motors first proposed in 1987 vertical placement of metal parts for CO2 continuous wave laser welding of galvanized steel (Delle Piane et al. 1987) and with this technique there is no need to maintain parts partial gap. Changing the joint geometry is a special technique that provides channels for the escape of zinc vapor during laser welding of galvanized steel (Milberg and Trautmann 2009).
But this technique requires extra setup of laser welding system, which increases the cost as well as degrades the efficiency of the laser welding system. Roller creates pressure on the part-to-part gap, which provided favorable conditions to escape the zinc vapor during laser welding. For a given workpiece specification, there are certain combinations of laser power, welding speed, focal point, and part-to-part gap that can produce optimal penetration depth during laser welding of galvanized steel (Balasubramanian et al. 2008).
In general, the shielding gas performs two tasks in laser welding (Hügel and Graf 2009, Zaeh et al. 2010). Regarding the physical properties of shielding gases, especially the thermal conductivity and density of the shielding gas are more important factors for laser welding (Seto et al. 2000). By using Ar as a side impact shielding gas during laser welding of galvanized steel, we can find relatively good mechanical properties of the joint, thin crystal grains in the heat affected zone and a visually good weld seam (Mei et al. 2009). ).
Monitoring system of laser welding process
- Plasma
- Back reflection
- Temperature
- Depth of penetration
- Part-to-part gap
- Keyhole or conduction mode
- Undercut, blowhole, and root sagging
- Why we need multi sensor fusion techniques?
They also observed experimentally that the changes in the internal structure of the sample, the release of the back reflection and the release of metal vapor on the surface of the sample are responsible for creating the stress wave. In other words, we can say that from acoustic emission or from the stress wave signal we can monitor the mechanical properties of the laser welded seam. 1989) demonstrate the monitoring of weld depth using acoustic emission. Eriksson and Kaplan (2009) experimentally observed that based on empirical value, bulge is easy to detect from the signal, but spatter is difficult to detect from signals (temp, back reflection and plasma) for the case of laser welding of galvanized steel. 2009b) argue that some of the laser welding defects such as blow hole, undercut and root subsidence are difficult to detect from the sensor signal using limit checking methodology.
Based on correlation coefficient graphs, Sibillano et al. 2007a) suggested a range of welding speed and within a range the quality of weld is acceptable. Here PCA is used for dimensional reduction and elimination of redundancy for plasma signals. 2007) attempted to compare the quality characteristics of laser welding (penetration depth and seam weld width) and the thermal properties of the plasma plume. The strength of the back-reflection from the weld zone mainly depends on the surface geometry of the weld pool and not on the temperature of the weld pool or the vapor cloud during the LW process (Olsson et al. 2011).
Zhang (2008b) found that the strength of the return reflection signal is a function of the distance of the workpiece from the focal position and the spacing between the parts. The high noise level and high return signal strength is a key feature of the absence of a lock. Therefore, the rebound clearly shows us the formation of a lock. 2012) confirmed that there is a strong correlation between laser light back reflection and part gap in a laser welding control system.
They experimentally observed that maximum penetration depth is achieved when the surface temperature is just below the vaporization point of the workpiece. The detection of the laser welding quality through acoustic emission is also confirmed by (Li et al. 1992). Based on acoustic emission, Farson et al. 1990) proposed Neuro-fuzzy feedback control system for monitoring penetration depth for laser welding.
Plasma electron temperature can be a promising technique for estimating the penetration depth (Sebestova et al. 2012, Sibillano et al. 2012). The lack of spatial resolution is a major disadvantage of the photodiode, but the camera allows for spatial resolution (Norman et al. 2009b). Detecting the transition from wire state to keyhole state provides sufficient information about the quality of the weld in terms of weld seam width, penetration depth.
Classification techniques for fault detection in laser welding
- Support vector machines
- Neural network
2007) identified the fault and no fault classification of laser welding based on the quality of the weld seam. When laser welding aluminum sheet, the presence of an antioxidant coating deteriorates the quality of the weld seam. Classification Wang and Gao (2013) Optical sensor Width of the weld seam Classification Timm et al. based on the geometry of the weld seam.
With the help of NN, Cook et al. 1995) estimated both the top and bottom widths of the weld seam in plasma arc welding. 2013) proposed an NN-based model for predicting weld seam geometry (weld width and penetration depth). They used laser power, welding speed and spot diameter as process variables for predicting weld seam geometry.
Chokkalingham (2012) suggested a NN-based model for predicting the penetration depth and weld seam width from infrared thermal images of the weld seam. To investigate the correlation analysis of weld top seam width variation and tensile strength in laser welding of galvanized steel. To investigate the correlation of weld seam variation and tensile strength over uniform top weld width in laser welding of galvanized steel.
Similarly, the regression model shown in Figure 3.9 shows that there is no correlation between the variance of the top weld and the ultimate tensile strength. Table 3.7: Analysis of average top weld width results Example 1: Uneven top width. In the case of a non-uniform seam boundary of the top weld, there is a positive correlation between the average logarithmically transformed width of the top weld and the maximum tensile strength.
However, welds with a uniform seam boundary do not have sufficient evidence for a correlation between the average width of the top weld seam and the maximum tensile strength. With a non-uniform seam boundary of the upper weld seam, there is a negative correlation between the variance of the upper weld seam and the maximum tensile strength. Correlation analysis of the variation of weld seam and tensile strength in laser welding of galvanized steel.
Variation of weld seam
Correlation analysis of the variation of weld seam verses tensile strength
This chapter points to some research questions, open for a thorough investigation in the field of laser welding. A powerful signal processing system is needed to improve the accuracy rate for spot detection in laser welding of galvanized steel. The use of multi-sensor fusion is necessary to assess the tensile strength of welded parts in an indirect non-destructive way in laser welding of galvanized steel.
Effects of piece-to-piece gap and weld direction on laser weld quality. Real-time fast transformation analysis of acoustic emission during CO2 laser welding of materials. Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm.
Prediction Algorithm of Molten Pool Width Based on Support Vector Machine during High Power Disc Laser Welding.
Correlation analysis of the variation and average width of weld seam verses tensile strength
Conclusions
Future researches
