0　Introduction

Manufacturing quality has become a vital characteristic. Welded joint quality mainly depends on mechanical properties of the weld metal and heat affected zone (HAZ), which in turn is influenced by metallurgical characteristics and chemical composition of the welded joint. These mechanical-metallurgical features of the welded joint depend on weld bead geometry, which is directly related to welding process parameters [1-3]. Therefore, it is very essential to select appropriate welding process parameters to obtaining optimal weld bead geometry [4].

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The formation of uniform or stable back beads in the first layer weld during one side multilayer welding is important to achieve high quality and productivity welded metal joints [5]. In conventional welding, metal or backing ceramic back plates are attached to the back side of the joint part before welding. However, when metal plates are used, a notch may occur that causes cracks [6], and in either case, the back plates must be removed by the operators after the welding, which is incompatible with automated welding processes. Developing welding methods that do not employ backing plates will enhance welding efficiency and reduce manufacturing costs [7-8]. Yamane et al. [9] applied a switch back welding process, a special single MAG to control back bead geometry in the V groove without a back plate. However, welding efficiency was low, with average welding speed = 1.7 mm/s. Yang et al.[10] proposed a double side arc welding method to realize no back chipping for thick plate welding. However, the weld conditions were confined to vertical and horizontal positions.

Some parameters interact in a complex manner, which directly or indirectly affects the back bead geometry, mechanical properties, melting features, and joint chemistry. Hence, an optimal process condition is required, capable of producing the desired quality weld. This optimization should simultaneously fulfill all the objectives, i.e., multi-response optimization [11].

Common approaches to tackle welding optimization include response surface methodology, multiple regression analysis, genetic algorithm modeling, and Taguchi method [12-15]. In most cases, optimization has only considered a single objective function. For a multi-response process, improvement of one response may cause changes in other responses, beyond the acceptable limit. Thus, to solve multi-criteria optimization problems, it is convenient to convert all the objectives into an equivalent single objective function to represent all the product quality characteristics.

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Grey relational analysis based on grey system theory can be used to effectively solve complicated inter-relationships among multiple performance characteristics [18]. A grey relational grade is obtained, and optimization of the complicated multiple performance characteristics can be converted into optimization of a single grey relational grade. Saurav et al. [19] used a grey based Taguchi method to optimize bead geometry in submerged arc bead-on-plate welding. Bead geometry related objective functions were selected, i.e., bead width, bead reinforcement, penetration depth, and HAZ depth. Lin et al. [20] used a grey based Taguchi method to optimize the weld bead geometry in activated GMA welding process. Thus, experimental results have shown that optimal process parameters can be effectively determined to improve multiple weld qualities through this approach.

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Fig.2 shows a typical PMAG-TIG double-arc hybrid welding setup. A pulsed DC MAG and DC TIG welder with rated output current of 500 A were used. The hybrid welding system was achieved by fixing the MAG torch on the welding robot, as shown in Fig.3. The MAG arc was leading, with a specified distance to the trailing TIG arc. The electrode distance can be adjusted by the TIG welding torch position. The PMAG arc was ignited first, and the TIG arc was ignited once a weld pool was formed on the root of the base metal groove.

The Taguchi method is a systematic application of design and experiment analysis for the purpose of improving product quality [16]. The method utilizes the well-balanced orthogonal array experimental design, and the signal to noise ratio (SNR) serves as the objective function to be optimized within the experimental domain. The Taguchi method has become a powerful tool for improving productivity during research and development, facilitating high quality products to be produced quickly and at low cost [17]. However, the Taguchi method optimizes a single performance characteristic, and is difficult to apply to multi-response optimization. Therefore grey relational analysis was adopted in this study.

Fig.1　Schematic diagram of experimental procedure

1　Materials and methods

1.1　Equipment setup

The current study integrated the Taguchi method with grey relational analysis to optimize PMAG-TIG twin arc root weld quality. A grey relational grade was obtained to evaluate the multiple quality characteristics, converting optimization of multiple quality characteristics into optimization of a single grey relational grade. Then, the grey grade was used to optimize weld bead geometry and analyze the effect of welding parameters on thick plate butt-joint welds in the PMAG-TIG hybrid root welding process. Fig.1 outlines the proposed approach.

Fig.2　Schematic map of the PMAG-TIG twin-arc hybrid welding system

Fig.3　Welding equipments of the PMAG-TIG twin arc hybrid welding

1.2　Materials and welding conditions

Table 1 shows the chemical compositions of the mild plate steel and solid wire. Specimen size was 16 mm×150 mm×300 mm (thickness×width×length) (Fig.4). The butt-joint type was a V groove, with fixed root opening = 1.0 mm, 20° bevel angle and 2.0 mm root face. The edge was prepared to normal welding conditions by a milling machine. Specimen edges and surfaces were cleaned with acetone to remove grease and residue before welding. Table 2 shows the PMAG-TIG double-arc welding conditions.

Fig.4　The size of specimen and groove shaped used in this work(mm)

Table 1　Chemical compositions of the base mild steel and wire used in the experiments

Table 2　Welding parameters used in the experiment

Welding parameters, including average PMAG and TIG welding currents, welding speed, distance between the wire and tungsten, affect back bead characteristics. For example, back bead width to root reinforcement ratio (BWRR), and height of deposited metal (HDM). Therefore, proper selection of the welding parameters can produce better welding performance. The initial welding parameters were PMAG welding average current = 300 A, TIG welding current = 100 A, welding speed = 600 mm/min, and electrode-tungsten tip separation = 22 mm. Welding experiments were performed to determine optimal welding parameters by setting the PMAG welding average current 270-330 A, TIG welding current 80-120 A, travel speed 600-720 mm/min, and wire-tungsten tip separation 18-26 mm, as shown in Table 3.

Table 3　Welding parameters used for the Taguchi orthogonal array

1.3　Welding performance evaluation

The grey relational coefficient was calculated to express the relationship between ideal and normalized experimental results:

All specimens were prepared by mechanical lapping, grinding and polishing, followed by etching with 3% nitric acid. An optical microscope was used to measure BWRR and HDM, as shown in Fig.5.

2　Results and discussion

2.1　Orthogonal array experiment

The fundamental principle of robust design is to improve quality by minimizing the effect of variation. Thus, it is important to identify significant noise factors [21], which requires Engineering experience and judgment. The PMAG-TIG twin arc hybrid root welding process is very difficult to control uniformity and stability of the back bead geometry.

Therefore, the PMAG welding average current, electrode-tungsten tip separation, travel speed of welding torch, and TIG welding current were selected as control factors; and the position of metallographic specimen on each specimen was selected as the noise factor (as shown in Fig.5a).

An L9 (34) orthogonal array was employed to assess the control effects (Table 4). There were nine test conditions, and two repetitions were conducted for each case.

2.2　Signal to noise ratio

The signal to noise ratio for the selected quality characteristics (BWRR and HDM) depend on the characteristic being evaluated [1]. The equation for calculating the signal-to-noise ratio (S/N) for larger is better performance characteristic,

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(1)

where n = 2 is the number of repetitions regardless of noise level, and Ni is the quality characteristic for the ith specimen. Table 5 shows S/N results for the experiment.

2.3　Grey relational analysis

We adopted grey relational analysis to solve the problem of multiple quality characteristics. The BWRR and HDM results were normalized over [0,1], for grey relational generation,

(2)

Fig.5　The position of N1 and N2 in specimen and weld bead geometry (a) Position of N1 and N2 in specimen (b) Weld bead geometry parameters

Table 4　Orthogonal array L9 (34) and experimental results

Table 5　Signal to noise ratios for measuredbead geometry features

Table 9 reveals that control factor D (welding speed) has a smaller effect on the multiple quality characteristics of the PMAG-TIG twin arc welding process. To prevent an over-estimate [22], control factor D is not considered and the estimated S/N ratio ηopt is computed as

Table 6 shows the normalized SNR for BWRR and HDM.

The weld back bead geometry was measured to evaluate PMAG-TIG hybrid weld quality. Increased HDM and larger BWRR are indicators of improved welding performance [19], Hence we employed these metrics to describe the weld bead geometry.

(3)

where is the ideal normalized result for the ith quality characteristic, and ζ is the distinguishing coefficient, defined on [0,1]. A weighting method is used to integrate the grey relational coefficients to the grey relational grade

(4)

where γj is the grey relational grade (GRG) for the jth experiment, ωi is the weighting factor for the ithquality characteristic (in this case, we assume ω1 = 0.7 and ω2 = 0.3), and m is the number of quality characteristics. Table 7 shows the grey relational coefficients and grades calculated from Table 6. Larger grey relational grade represents closer to ideal. Thus, optimization of the complicated multiple quality characteristics can be achieved by optimizing the grey relational grade.

Table 6　Normalized signal to noise ratio

Table 7　Weighted quality parameters and grey relational grades (GRGs)

2.4　Optimization

The effect of each welding process parameter on grey relational grade at different levels can be assumed to be independent because the design employed the L9 orthogonal array. Table 8 summarizes the mean GRG for each level of welding parameters and Fig.6 shows GRG relationships for multiple quality characteristics. Since larger GRG implies better performance, the optimal welding combination is PMAG welding average current = 300 A, TIG welding current = 80 A, wire to electrode distance = 18 mm, and welding speed = 660 mm/min.

Fig.6　Grey relational grade graph for the multiple quality characteristics of PMAG-TIG twin arc hybrid root welds (a)PMAG welding current level (b)TIG welding current level(c) Wire to electrode distance level (d)Welding speed level

Table 8　Grey relational grade (GRG) response table for parameter levels define in Table 3

We employed an analysis of variance (ANOVA) to investigate which welding process parameters significantly affected the quality characteristics, as shown in Table 9. The sum of squares ranks the importance of factor for its impact on GRG, and hence weld quality. Thus, factors B (TIG welding current), A (PMAG average welding current), and C (wire-tungsten tip separation) have significant effects on welding quality.

Table 9　Analysis of variance for the factors of Table 8 contribution to weld quality

2.5　Confirmation and validation

where yij is the original value for the ith result in the jth experiment, and xij is the normalized value.

(5)

where , and are the average grey relational grade of the optimal level of the factors A, B and C as shown in Table 8. The proposed approach yielded the welding condition that optimized the multiple quality characteristics of the PMAG-TIG hybrid welding specimen: pulse MAG welding average current of 300 A, TIG welding current of 80 A, travel speed of welding torch of 660 mm/min and the distance between PMAG wire and TIG tip of 18 mm. Table 10 presents the experimental results obtained using these optimal welding parameters. Comparing the initial conditions with the proposed approach reveals that the improvement of the grey relational grade when the initial conditions were changed to the optimal parameters is 0.615. The experimental results as shown in Table 10 confirm that the optimization of the PMAG-TIG twin arc welding parameters via the grey-based Taguchi method was achieved. Comparison of cross-section of PMAG-TIG twin arc hybrid welds as shown in Fig.7 reveals that the weld bead geometry of the optimal welding parameters via the proposed approach is better than that of initial conditions.

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Table 10　Welding performance enhancement using the proposal approach

Fig.7　Cross-sections of PMAG-TIG twin arc hybrid welds for validation (a) Initial welding parameter (b) Optimal welding parameter

3　Conclusions

Through the above analysis, the following conclusions are obtained.

(1) The optimum levels of design parameters are that the PMAG welding average current of 300 A, TIG welding current of 80 A, the wire-tungsten tip separation of 18 mm, and welding speed of 660 mm/min. Under these parameters, a continuous, uniform and stable back-bead geometry is realized.

(2) The degree of influence of the design parameters on the back-bead geometry was in the order of TIG welding current > PMAG welding average current > wire-tungsten tip separation > welding speed.

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(3) The back-bead geometry obtained with the optimum level of the design parameter is as follow: the back-bead width is 3.70 mm; the root reinforcement is 0.58 mm; the back-bead width to root reinforcement ratio is 6.379; the deposited metal height is 8.44 mm. The result is an efficient root weld back bead.

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