تجزیه و تحلیل حساسیت پارامترهای فرآیند جوشکاری قوس الکتریکی غوطه ور

Selection of process parameters has great influence on the quality of a welded connection. Mathematical modelling can be utilized in the optimization and control procedure of parameters. Rather than the well-known effects of main process parameters, this study focuses on the sensitivity analysis of parameters and fine tuning requirements of the parameters for optimum weld bead geometry. Changeable process parameters such as welding current, welding voltage and welding speed are used as design variables. The objective function is formed using width, height and penetration of the weld bead. Experimental part of the study is based on three level factorial design of three process parameters. In order to investigate the effects of input (process) parameters on output parameters, which determine the weld bead geometry, a mathematical model is constructed by using multiple curvilinear regression analysis. After carrying out a sensitivity analysis using developed empirical equations, relative effects of input parameters on output parameters are obtained. Effects of all three design parameters on the bead width and bead height show that even small changes in these parameters play an important role in the quality of welding operation. The results also reveal that the penetration is almost non-sensitive to the variations in voltage and speed.

Submerged Arc Welding (SAW) is a high quality welding process with a very high deposition rate. It is commonly used to join thick sections in the flat position. SAW is usually operated either as fully mechanized or automatically processed. However, it can be used semi-automatically as well. During SAW process, operator cannot observe the weld pool and not directly interfere with the welding process. As the automation in the SAW process increases, direct effect of the operator decreases and the precise setting of parameters become much more important than manual welding processes. In order to obtain high quality welds in automated welding processes, selection of optimum parameters should be performed according to engineering facts. Generally, welding parameters are determined by trial and error, based on handbook values, and manufacturers’ recommendations. However, this selection may not yield optimal or in the vicinity of optimal welding performance. Furthermore, it may cause additional energy and material consumption resulting in low quality welding. Besides, in the industrial welding robots, even smaller changes in the welding process parameters may cause unexpected welding performance. Therefore, it is important to study stability of welding parameters to achieve high quality welding. Optimum process parameters selection has been investigated by some significant studies via establishing a mathematical model correlating welding parameters with quality characteristics using different approaches. In principle, weld bead geometry (weld bead characteristic) is one of the major quality properties mainly due to its influence on energy and electrode consumption. Determination of SAW process parameters to achieve desired weld bead geometry and the prediction of weld bead characteristics, such as bead width, bead height and penetration for the given input parameters, have been accomplished (Tarng et al., 2000, Chandel and Bala, 1988, Gupta and Parmar, 1989, Chan et al., 1994, Gunaraj and Murugan, 1999a, Gunaraj and Murugan, 2000a, Kim et al., 2003 and Tarng et al., 2002). Additionally, the area of Heat Affected Zone (HAZ) has also been used as a performance characteristic for parameter optimizations in SAW (Gunaraj and Murugan, 1999b, Gunaraj and Murugan, 2002 and Lee et al., 2000). Predicting the effects of small changes in design parameters provide very important information in engineering design. Therefore, by using a mathematically modeled prediction system, effect of any changes in the parameters on the overall design objective can be determined. This kind of analysis is known as Design Sensitivity Analysis (DSA). Basically, Sensitivity Analysis (SA) yields information about the increment and decrement tendency of design objective function with respect to design parameters. There are few studies performed sensitivity analysis using mathematical model for different welding methods. For example, Kim et al. (2003) conducted a sensitivity analysis in order to compare relative impact of process parameters on bead geometry of Gas Metal Arc (GMA) welding using a mathematical model. They found that width and height of weld bead are more sensitive to changes in process parameters relative to penetration. Gunaraj and Murugan (2000b) carried out a different procedure to optimize bead volume formed by SAW process. They used sensitivity analysis as a post optimization procedure to calculate variations in the objective function due to the small changes from the optimum values of constraints. In addition to these investigations, there is still need for optimization studies in all welding processes, especially for automated welding systems. Because of its simplicity in implementing to all mathematical models and inclusion of cross tendency information between process parameters, sensitivity analysis is a very useful tool. In this study, mathematical relations (empirical equations) between SAW process parameters and weld bead characteristics (SAW mathematical models) were constructed based upon the experimental data obtained by three parameters-three levels factorial analysis. The empirical equations, simulating the SAW process approximately, were carried out by Multiple Regression Analysis (MRA) and sensitivity equations were derived from these basic models. An analysis generally requires a definition of an objective function and design parameters. In this study, the objective function (quality function) was chosen as weld bead characteristics (the width, height and penetration of the weld bead) whereas process parameters (arc current, voltage and welding speed) were selected as the design variables. The methodology used in this paper for SAW is similar to that used by Kim et al. (2003) for GMA welding. The present study mainly focuses on the determination of sensitivity characteristics of design parameters and the prediction of fine-tuning requirements of these parameters in SAW process. The results revealed considerable information about process parameter tendencies and optimum welding conditions. Similar process parameter behaviors were obtained for GMA welding by Kim et al. (2003). However, our study does not only provide valuable results for SAW like for GMA welding (Kim et al., 2003), but also aims to present a sensitivity characteristic map for SAW process.

In the first part of this study, mathematical modelling using curvilinear regression equations were developed from experimental data. Then, sensitivity analysis of weld bead parameters such as bead width, bead height, and penetration to variations in current, voltage, and speed in submerged arc welding process were performed. Welding process parameters, required for desired weld bead geometry, can effectively be predicted using mathematical models developed in this study. These mathematical models can also be used to optimize processes and to develop automatic control systems for welding power sources. Following conclusions can be drawn from this sensitivity analysis: • Bead width is very sensitive to all process parameters. Current, voltage and speed are the determining parameters for bead width. • Bead width is more sensitive to voltage and speed variations than that of bead height and penetration. • All three process parameters have effects in determining the bead height. • In order to decrease the bead height, higher values of voltage and speed can be considered. • Since bead width and penetration are more sensitive to current variations than bead height, arc current can be used effectively for any adjustment in bead width and penetration. • Current is the most important parameter in determining the penetration. Penetration is almost non-sensitive to variations in voltage and speed. Therefore, voltage and speed cannot be efectively used to control penetration. • At maximum heat input level (higher levels of current and voltage, and lower level of welding speed), current sensitivity of penetration, and speed sensitivity of bead width reach their maximum values.