Microsoft Word - 1-V8N1(2023)-AITI#10658(01-11).docx Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 Optimization of Weld Parameters in Wire and Arc-Based Directed Energy Deposition of High Strength Low Alloy Steels Van Thao Le1,*, Dinh Si Mai1, Van Thuc Dang1, Duc Manh Dinh1, Thi Hong Cao2, Van Anh Nguyen3 1Advanced Technology Center, Le Quy Don Technical University, Hanoi, Vietnam 2Institute for Tropical Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam 3Welding Engineering and Laser Processing Centre, Cranfield University, Bedford, UK Received 10 August 2022; received in revised form 27 September 2022; accepted 30 September 2022 DOI: https://doi.org/10.46604/aiti.2023.10658 Abstract This paper aims to investigate the fabrication of high strength low alloy (HSLA) steels by wire and arc-based directed energy deposition (WADED). Firstly, the relationship between the process variables (including the travel speed-V, the current-C, and the voltage-U) and the geometrical characteristics of weld beads (including the bead height (BH), bead width (BW), and melting pool length (MPL)) was investigated. Secondly, the optimal process variables were identified using the desirability approach. The results indicate that voltage-U has the highest impact on BW and MPL, meanwhile the travel speed-V is the most impacting factor on BH. The optimal variables for the WADED process of HSAL steels are V = 0.3 m/min, C = 160 A, and U = 19 V. The component fabricated with the optimal variables is fully dense without spatters and defects, confirming the efficiency of the WADED process for HSLA steels. Keywords: WADED, HSLA steel, weld bead, optimal variables 1. Introduction Emerged since the 1980s, additive manufacturing (AM) technologies, especially metallic AM, are strongly developed, and they are becoming the key technology in the Industry 4.0 era [1]. Because of the layer-by-layer manufacturing principle, AM technologies can fabricate very complex structures from various materials, including metallic alloys that are very difficult to machine by conventional processes such as milling and turning [2]. The metallic AM technologies can be categorized based on the energy source and feedstock form or the fabrication methods. According to the feedstock form and the fabrication methods, there are two main groups of metallic AM, i.e., powder bed fusion (PBF) and directed energy deposition (DED) [3]. Compared to PBF-AM processes, DED-AM can produce metallic components with larger dimensions and can be applied effectively for repairing and remanufacturing applications [4-7]. The DED-AM processes include two technologies: (i) laser and powder-based DED (LPDED) using a laser source to melt metal powder (Fig. 1(a)), and (ii) wire and arc-based DED (WADED) utilizing an arc source to melt the metal wire (Fig. 1(b)). The nozzle in the WADED is a welding torch, and the wire feeding method depends on the used welding source, for example, gas tungsten arc welding (GTAW), plasma arc welding (PAW), and gas metal arc welding (GMAW) [8]. Compared to other metal AM technologies, WADED has a superior rate of material deposition (from 3 to 8 kg/h), high efficiency of material utilization, low costs of investing systems and devices, and easy implementation [9]. * Corresponding author. E-mail address: vtle@lqdtu.edu.vn Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 2 The metal wire available in the welding market can be used for WADED processes. They are also much cheaper than metal powder used in PBF-AM and LPDED. Therefore, WADED is becoming a potential solution for manufacturing components with large and wide dimensions [10]. (a) The LPDED process [11] (b) The WADED process Fig. 1 Schemas of LPDED and WADED processes Among metals used in WADED, including steels, aluminum, titanium, and nickel-based alloys, steels are the most investigated materials because of their relatively low costs, wide applications, and availability in the welding market. In the literature, many steel grades have been investigated and fabricated with the WADED processes, for example, low-carbon steels (e.g., ER70S-6) [12-14], austenite stainless steels (e.g., 304, 308L, 308LSi, 309L, and 316L) [15], and high- strength low alloy (HSLA) steels (e.g., ER110S-G and ER120S-G) [16-18]. The HSLA steels are largely utilized for manufacturing lightweight structures and high strength components in many sectors, for example, automotive, shipbuilding, tools and die industries. These steel grades have high strength, toughness, weldability, and low costs [19]. Recently, many researchers have investigated and fabricated components from HSLA steels by the WADED processes. Sun et al. [20] produced thin-wall HSLA steel components by WADED and examined the anisotropy in mechanical characteristics. They stated that the strengths of the as-fabricated component in the horizontal direction were lower than those in the vertical direction. Rodrigues et al. [16] studied the impact of the heat input on the microstructures and mechanical properties of the WADEDed thin-wall HSLA steel part. They observed that there were no significant differences in microstructure between the specimens fabricated with low and high levels of heat input. Dirisu et al. [19] analyzed the toughness properties of HSLA steels produced by WADED. They found that the refinement in grains and the increase in density of grain boundaries resulted in high resistance to failure of the samples in the vertical direction. Fang et al. [21] also fabricated HSLA steel parts by WADED. The researcher observed that the part had a superior balance in strength and ductility. The variation in microhardness was due to the difference of thermal histories in different regions of the component. Although certain authors have investigated HSLA steels fabricated by the WADED processes, as mentioned above, they mainly analyzed microstructures and mechanical characterization of the as-built material, as well as the influence of process variables on the quality of the part. Until now, the studies on predicting and optimizing process variables in WADED of HSLA steel to obtain the expected geometrical properties of weld beads are still limited. Most of the previously mentioned studies have selected the process variables (e.g., the arc voltage, welding current, and wire-feed speed) based on the suggestion of the wire manufacturers for conventional welding or based on several tests, while the WADED process is very different from welding. In WADED processes, the quality and geometry of weld beads (e.g., stable and smooth shape and less spatter) notably affect the process stability and the appearance of as-fabricated components [22-25]. Therefore, this study aims to explore the relationship between the process variables {C, U, and V} and the geometrical characteristics of weld beads {BW, BH, and MPL} and identify optimal process parameters that produce weld beads with expected geometrical properties. Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 3 2. Materials and Methods 2.1. Materials and the WADED system In this investigation, the HSLA steel wire (SM-110) with a 1.2-mm diameter supplied by Hyundai Welding and the low-carbon steel substrates with a size of 200 × 200 × 10 mm had been utilized. The chemical elements of the wire are 1.95%Ni, 1.90%Mo, 0.34%Cr, 0.58%Mo, 0.80%Si, 0.089%C, 0.010%P, 0.004%S, and %Fe in balance (wt.%). A WADED system (Fig. 2(a)) consists of a GMAW source, a welding torch, a welding wire feeder, a 6-axis robot, and a shielding gas feeder was used to fabricate the samples (e.g., single weld tracks, Fig. 2(b) and Fig. 2(c)). The relationships between the wire feed speed and welding current in this GMAW-AM system is approximately increasing linearly. A mixed gas of argon (80%) and CO2 (20%) with 16 L/min in flow speed was applied during the WADED process. (a) The welding robot (b) Weld bead samples (c) WB’s characteristics Fig. 2 The WADED system, the WB samples with the measurement area, and the response description 2.2. Experimental procedure To achieve the goal of the study, the research procedure shown in Fig. 3 was performed. The research procedure includes the following steps: (i) identification of the process variables and their ranges, (ii) design of experiment, (iii) fabrication of single weld beads by the WADED process, (iv) measurement of the responses (i.e., BH, BW, and MPL), development of regression models for the responses and determining the impact of process variables on the responses through the analysis of variance (ANOVA), and (v) the optimization of process variables. To observe the relationship between the process variables and the responses, the Taguchi L9 orthogonal array was adopted to design the experimental plan. Three input variables, including the current-C, the travel speed of the weld torch-V, and the voltage-U were selected for the investigation because they directly influence the shape and dimensions of the weld beads. Three levels for each variable were used (Table 1). As a result, there were nine experiment runs. Table 1 Input variables and their levels for the DoE Item Levels Voltage-U (V) 18 20 22 Current-C (A) 120 140 160 Travel speed-V (m/min) 0.3 0.4 0.5 Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 4 Fig. 3 The study flowchart After the fabrication of weld bead samples, the responses of weld beads, including BH, BW, and MPL were measured. These characteristics of weld beads play important roles in the WADED process. BH and BW are related to the layer width and height, whereas the MPL is related to the planning of deposition paths. The measurements were executed in the stable regions of welding beads (Fig. 2(b)) utilizing a digital Mitutoyo caliper with 0.01 mm in resolution and ± 0.02 mm in accuracy. The BW and BH were measured five times at five locations in the middle zone of weld beads (Fig. 2(b)). Finally, the average value of the five measurements of BW and BH was taken for analysis. On the other hand, the measurement of MPL was repeated three times to ensure the reliability of the measurement results. The experiment runs and measured results were presented in Table 2. Table 2 Experiment runs and the response measurement Experiment run Input variables Responses Current-C (A) Voltage-U (V) Travel speed-V (m/min) BW (mm) BH (mm) MPL (mm) 1 120 18 0.3 5.39 3.09 11.26 2 120 20 0.4 5.86 2.54 11.64 3 120 22 0.5 5.82 1.82 12.39 4 140 18 0.4 4.58 2.92 10.49 5 140 20 0.5 5.41 2.33 11.34 6 140 22 0.3 7.51 2.81 13.52 7 160 18 0.5 4.18 2.77 9.79 8 160 20 0.3 6.87 3.00 11.95 9 160 22 0.4 7.82 2.37 13.24 2.3. Analysis and optimization methods To evaluate the impact of input variables on the responses and identify the impact contribution of each input, the ANOVA and the Minitab software was adopted. The ANOVA was executed with a 5% significance level and a 95% confidence level. In the WADED, BH, and BW are expected to be maximal while the MPL is minimal to enhance productivity. As a result, the optimization problem was formulated as Eq. (1). { } { } { } { } , , , 120 160 , 18 22 , 0.3 0.5 / Find C U V To maximize BH BW and minimize MPL Subject to C A U V V m min≤ ≤ ≤ ≤ ≤ ≤      (1) Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 5 The optimal input variables were estimated by the desirability function (DF) method [26]. Moreover, the weight for each response (i.e., BW, BH, and MPL) was identified by the CRITIC method [27]. The steps of the CRITIC method are described as follows: Step 1: Construct the decision matrix (DM) of k experimental runs and p evaluation attributes: DM = [��� ] ×� . Step 2: Normalize the DM using Eq. (2): ˆ worst ij j ij best worst j j m m m m m − = − (2) where �� �� is the normalized value of the �� alternative for ��� attribute, �� ���� and �� ����� are the best and worst values of ��� attribute. Step 3: Calculate the standard deviation of each normalized attribute using Eq. (3): 2 1 ˆ( ) k ij j i j m m k σ = − =  (3) where k is the number of experimental runs and ��� is the average value of � �� normalized attribute. Step 4: Construct the symmetric matrix [���� ] × with the linear-correlation coefficient ���� between the attributes. ���� is calculated by Eq. (4): 1 2 2 1 1 ˆ ˆ( )( ) ˆ ˆ( ) ( ) k li i lj j l ij k k li i lj j l l m m m m cc m m m m = = = − − = − −    (4) Step 5: Calculate the attribute information ��� by Eq. (5): 1 (1 ) k j j jl l AI ccσ = = − (5) Step 6: Calculate the weight �� for each attribute (i.e., response): 1 j j p j j AI w AI = =  (6) 3. Results and Discussion 3.1. Regression models The regressive models of the responses BW, BH, and MPL are shown in Tables 3, 4, and 5, respectively. They were developed with the help of Minitab software. In the case of BW, the p-values of U and V in Table 3, are smaller than 0.05, while the p-value of C is bigger than 0.05, meaning that U and V are the significant terms of the BW model. The values of R-sq, R-sq(adj), and R-sq(pred) are 95.67%, 93.07%, and 83.83%, respectively. It indicates that the BW model has acceptable accuracy and can terms be used to predict the response in the entire design space. For the BH model, all the p-values of C, U, and V in Table 4 are smaller than 0.05. Therefore, all the model terms C, U, and V are significant. The values of the determination coefficients R-sq, R-sq(adj), and R-sq(pred) are 96.51%, 94.72%, and Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 6 86.18%, respectively, indicating a reasonable accuracy of the BH model. This model can also be used to predict the response in the entire design space. For the developed model of MPL, the p-values of U and V in Table 5 are smaller than 0.05, while the p-value of C is bigger than 0.05, indicating that U and V are the significant term of the MPL model. The values of R-sq, R-sq(adj), and R-sq(pred) are 97.73%, 96.36%, and 91.15%, respectively. Therefore, the MPL model has an acceptable accuracy, and it can be used to predict the responses (i.e., BW, BH, and MPL) in the whole design space. Table 3 ANOVA of BW Source DF Seq SS Contribution Adj SS Adj MS F-Value P-Value Regression 3 11.8791 95.67% 11.8791 3.9597 36.81 0.001 C 1 0.5424 4.37% 0.5424 0.5424 5.04 0.075 U 1 8.1713 65.81% 8.1713 8.1713 75.97 0.000 V 1 3.1654 25.49% 3.1654 3.1654 29.43 0.003 Error 5 0.5378 4.33% 0.5378 0.1076 - - Total 8 12.4169 100.00% - - - - Regressive model �� = �4.93 # 0.01503 × ' # 0.5835 × ) � 7.26 × - R-sq = 95.67% R-sq(adj) = 93.07% R-sq(pred) = 83.83% Table 4 ANOVA of BH Source DF Seq SS Contribution Adj SS Adj MS F-Value P-Value Regression 3 1.26082 96.51% 1.26082 0.420272 46.14 0.000 C 1 0.07935 6.07% 0.07935 0.079350 8.71 0.032 U 1 0.52807 40.42% 0.52807 0.528067 57.98 0.001 V 1 0.65340 50.02% 0.65340 0.653400 71.74 0.000 Error 5 0.04554 3.49% 0.04554 0.009108 - - Total 8 1.30636 100.00% - - - - Regressive model �. = 6.109 # 0.00575 × ' � 0.1483 × ) � 3.300 × - R-sq = 96.51% R-sq(adj) = 94.42% R-sq(pred) = 86.18% Table 5 ANOVA of MPL Source DF Seq SS Contribution Adj SS Adj MS F-Value P-Value Regression 3 11.3854 97.73% 11.3854 3.79513 71.65 0.000 C 1 0.0160 0.14% 0.0160 0.01602 0.30 0.606 U 1 9.6520 82.85% 9.6520 9.65202 182.22 0.000 V 1 1.7173 14.74% 1.7173 1.71735 32.42 0.002 Error 5 0.2648 2.27% 0.2648 0.05297 - - Total 8 11.6502 100.00% - - - - Regressive model /01 = 1.55 � 0.00258 × ' # 0.6342 × ) � 5.350 × - R-sq = 97.73% R-sq(adj) = 96.36% R-sq(pred) = 91.15% 3.2. Relationship between the input variables and the responses As shown in Fig. 4(a), it is indicated that the BW increases when U and C increment. Meanwhile, V shows an opposite impact tendency. The BW decreases with the augmentation in V. Based on the ANOVA results (Table 3), the voltage-U has the most impact contribution to the BW with 65.81%, followed by the travel speed (25.49%), and the current (4.37%), respectively. The influence of the input variables on the BW can be explained as follows. An increase in voltage also makes an increase in the length and spreading of the arc. Therefore, BW becomes larger with a higher level of voltage [28]. An increase in welding current leads to an increase in wire feed speed and material deposition, resulting in an augmentation in melting pool size and in Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 7 the width of weld beads (BW) [23]. Oppositely, an increase in travel speed causes a reduction in material deposition quantity per length unit. Therefore, the BW becomes narrow as the travel speed increases [28]. The influence of the input variables on the BH is shown in Fig. 4(b). It is shown that the increase in both the voltage-U and the travel speed-V causes a decrease in the BH. On the other hand, the BH increases when the current-C increase. However, the BH sightly increases when C increases from 140 A to 160 A. In this case, the travel speed-V reveals the highest impact on the BH with a contribution of 50.02%, followed by the voltage-U (40.42%), and the current-C (6.02%), respectively (Table 4). Indeed, when increasing the travel speed, the quantity of deposited materials per length unit is reduced. Hence, BH is reduced [28-29]. The spreading area of the arc is larger when the voltage increases, leading to flatter weld beads [30]. As a result, BH reveals a reducing trend with an increment in voltage. As C increases, the wire feeding speed augments. Hence, the volume of materials deposited increases, leading to an increase in BH [28]. For the MPL, as depicted in Fig. 4(c), it strongly increases when the voltage increases, meanwhile the MPL slightly increases with the increase in the current-C. The MPL, on the other hand, increases as V augments from 0.3 m/min to 0.4 m/min, after that it decreases. These findings can be explained by the ANOVA results (Table 5). It is shown that the voltage-C has the most influential contribution of 82.85% to MPL, while the current has the lowest impact contribution of 0.14% to MPL. (a) Effects of process variables on BW (b) Effects of process variables on BH (c) Effects of process variables on MPL Fig. 4 Influences of the input variables on the responses 3.3. Optimization results As mentioned previously, the weights for the responses were calculated by the CRITIC method. The weight values of BW, BH, and MPL were 0.42, 0.23, and 0.34, respectively. The solution of the multi-attribute optimization problem (Eq. (1)) was shown in Fig. 5. It indicated that the optimal input variables are {C = 160 A, V = 0.3 m/min, and U = 19 V}. This set of input parameters is corresponding to the DF value of 0.8651 and the predicted responses BW = 6.39 mm, BH = 3.22 mm, and MPL = 11.59 mm. Compared to the worst case, where the BH was the smallest (the experiment run #3), the optimal input parameters allow enhancing the BH and BW by 77% and 10%, respectively, while reducing the MPL by 6%. Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 8 Fig. 5 Optimization solution To confirm the efficiency of the optimal input parameters, they were tested to fabricate a three-bead multi-layer-cylindric wall (Fig. 6). It is shown that the optimal parameters enable the production of smooth weld beads without spatters and major defects. There is only a minor defect in shape on the top surface at the stopping point of the weld path. In this investigation, as the starting point and the ending point of the weld path were not identical, there was an overlap between the arc-striking and the arc-extinguishing regions of the arc (Fig. 7(a)). Therefore, the difference in height between the arc-striking and the arc-extinguishing regions was compensated. On the other hand, when the starting and the ending points are identical, there is a space in the circle weld bead between the starting and the ending points (Fig. 7(b). As the number of layers increases, the gap depth increases, resulting in major defects in the shape of the part. Fig. 6 Multi-bead multi-layer cylindric part (a) Not identical (b) Identical Fig. 7 Programming of a circle weld path: the starting point and the ending point Advances in Technology Innovation, vol. 8, no. 1, 2023, pp. 01-11 9 Moreover, the as-built material is fully dense without defects such as pores, cracks, and lack of fusion. The microstructure mainly consists of α-ferrite phases in acicular and granular morphologies (Fig. 8(a)). The EDX analysis results also indicate that the as-built material has similar chemical elements as the wire material (Fig. 8(b)). The melted metal was perfectly protected by the shielding gas during the WADED process. (a) Microstructure (b) Chemical elements Fig. 8 Microstructure and chemical elements of the as-built material 4. Conclusions In this study, HSLA steel was utilized as the raw material in the WADED process. The study aims to predict the connection between the main process variables {C, U, and V} and the geometrical characteristics of weld beads {BW, BH, and MPL}. Furthermore, finding the optimal process variables is also the study’s aim. The main findings of this investigation are highlighted as follows. (1) The voltage-U has the highest impact on BW and MPL, meanwhile the travel speed-V is the most impacting factor on BH. An increase in U leads to an increase in BW and MPL and a decrease in BH. On the other hand, an increase in V causes a decrease in both BW and BH. (2) All the predictive models of BW, BH, and MPL have an acceptable accuracy with the determination values R-sq = 95.67%, 96.51%, and 97.73%, respectively. They can be applied to predict the responses in the entire design space. (3) The optimal process variables for the WADED process of SM-110 HSLA steel are V = 0.3 m/min, C = 160 A, and U = 19 V. The component fabricated with the optimal variables has a smooth top surface. Moreover, spatters and defects don’t present in the fabrication. The as-built material is fully dense without defects such as pores, cracks, and lack of fusion. (4) The microstructure of the as-built material is mainly composed of ferrite phases with acicular and granular morphologies. Its chemical elements also have the comparable weight percentage to those of the wire material. 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