Hardness Prediction of HAZ in Temper Bead Welding by · PDF fileL. YU, et al./ Hardness Prediction of HAZ in Temper Bead Welding by Non-Consistent Layer Technique 35 Half Bead technique,

34 ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

E-Journal of Advanced Maintenance Vol. 6 (2014) 34-47 Japan Society of Maintenology

Hardness Prediction of HAZ in Temper Bead Welding by Non-Consistent Layer Technique

Lina YU1,*, Kazuyoshi SAIDA1, Masahito MOCHIZUKI1, Masashi KAMEYAMA2, Naoki CHIGUSA3 and Kazutoshi NISHIMOTO4 1 Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan 2 Japan Nuclear Safety Institute, 5-36-7 Shiba, Minato-ku, Tokyo 108-0014, Japan 3 The Kansai Electric Power Co., Inc., 8 Yokota, 13 Goichi, Mihama-cho, Mikata-gun, Fukui 919-1141, Japan 4 Fukui University of Technology, Gakuen 3-6-1, Fukui-shi, Fukui 910-8505, Japan ABSTRACT Based on the experimentally obtained hardness database, the neural network-based hardness prediction system of heat affect zone (HAZ) in temper bead welding by Consistent Layer (CSL) technique has been constructed by the authors. However in practical operation, CSL technique is sometimes difficult to perform because of difficulty of the precise heat input controlling, and in such case non-CSL techniques are mainly used in the actual repair process. Therefore in the present study, the neural network-based hardness prediction system of HAZ in temper bead welding by non-CSL techniques has been constructed through thermal cycle simplification, from the view of engineering. The hardness distribution in HAZ with non-CSL techniques was calculated based on the thermal cycles numerically obtained by finite element method. The experimental result has shown that the predicted hardness is in good accordance with the measured ones. It follows that the currently proposed method is effective for estimating the tempering effect during temper bead welding by non-CSL techniques.

* Corresponding author, E-mail: [email protected]

KEYWORDS

Hardness, Temper bead welding, Consistent layer technique, Low-alloy steel, FEM

ARTICLE INFORMATION

Article history: Received 18 April 2014 Accepted 15 July 2014

1. Introduction

Low alloy steel ASTM A533B possessing superior low-temperature toughness and weldability is typically used as the material for pressurized water reactor vessels in nuclear power plants. The aged nuclear power plants are needed to be repaired or maintained to assure the safety and extend their lives. After repair welding, post weld heat treatment (PWHT) is normally required to eliminate the residual stress and decrease the hardness. However, PWHT is sometimes difficult to perform in operation in the case of repairing for large-scale structures. In practice, the temper bead welding technique is an effective repair welding method instead of PWHT [1-3].

Temper bead welding is a kind of multi-pass welding, in which the tempering effect is caused by heat-arising from the subsequent multi-layer weld thermal cycles. Several kinds of temper bead welding techniques have been developed in the past twenty years: (a) Half Bead Technique, (b) Consistent Layer Technique, (c) Alternate Temper Bead Technique, (d) Controlled Deposition Technique, and (e) Weld Toe Tempering Technique [4]. Consistent Layer (CSL) technique is the theoretically most authoritative method among these temper bead welding techniques. It is because that this technique places the Ac1 retransformation line of the 2nd layer welding within fusion zone of the 1st layer welding, as a result, HAZ of the 1st layer is only tempered but not retransformed by the subsequent layer. The authors have previously reported the hardness prediction system of the CSL technique using neural network based on the experimentally obtained hardness database [5].

However in practical operation, CSL technique is hardly to perform because of difficulty of the precise heat input controlling, and in such case non-CSL techniques (Controlled Deposition technique,

L. YU, et al./ Hardness Prediction of HAZ in Temper Bead Welding by Non-Consistent Layer Technique

35

Half Bead technique, etc) are often applied in the actual repair process. Therefore in the present study, the hardness prediction system of non-CSL technique has been investigated, from the view of engineering. The proposed hardness prediction system was constructed with simplifying the complicated thermal cycles of non-CSL technique into 4 typical patterns of thermal cycles of CSL technique. The proposed hardness prediction system of non-CSL technique has been verified with comparing the predicted hardness with the measured ones in HAZ of low alloy steel A533B when temper bead welding of non-CSL technique was applied.

2. Materials and experimental proceedings

The chemical compositions of the low-alloy steel A533B and filler material Inconel 690 used are shown in Table 1. Samples of low-alloy steel (5×5×5 mm) were heated by a high frequency induction heating device to synthesize as-welded and temper-processed HAZs. The thermal cycle conditions for the simulated thermal cycles of CSL technique and non-CSL technique are shown in Tables 2 and 3, respectively. Here, Tpi is the peak temperature of the ith pass thermal cycle, and CRi means the cooling rate from 800°C to 500°C of the ith pass thermal cycle. The Vickers hardness was measured in the cross sectional area of the specimens after polishing and etching with 3% nital solution. The Vickers hardness measurement was performed at a load of 9.8N for 20 second, and the average values were taken after excluding the maximum and minimum values from each multiple measurement.

In order to simulate the actual repair welding, the 6 layer-163 pass welded samples (shown in Fig. 1) were produced by TIG welding with the welding conditions shown in Table 4. The temper bead welding was performed using non-CSL technique. The sectioned surfaces of 1-layer, 3-layer and 6-layer weldments were cut from the multi-pass welded sample. The thermal cycles in 6 layer-163 pass temper bead welding were calculated using finite element method (FEM) software developed for welding simulation [6].

Table 1 Chemical composition of A533B and Inconel690

Material Chemical composition (mass %) C Si Mn P S Ni Cu Cr Mo Ti Al Fe Co

A533B 0.12 0.26 1.43 0.006 0.002 0.53 0.02 0.01 0.51 - 0.038 Bal. - Inconel690 0.02 0.15 0.27 0.0021 0.0009 Bal. - 29.59 0.02 0.406 0.216 10.13 0.05

Table 2 Thermal cycle conditions for the simulated thermal cycles of CSL technique

Thermal cycle 1st 2nd Temper cycle

Peak Temperature, Tpi (℃) 400~1350 670~1350 400~650 Cooling rate, CRi (℃/s) 3, 30,60,100 3, 30,60,100 5s~1hr (Holding time)

Table 3 Thermal cycle conditions for the simulated thermal cycles of non-CSL technique

Thermal cycle 1st 2nd 3rd 4th Temper cycle

Peak Temperature, Tpi (℃)

400~1350 670~1350 670~1350 670~1350 400~650

Cooling rate, CRi (℃/s)

3, 30,60,100 3, 30,60,100 3, 30,60,100 3, 30,60,100 5s~1hr (Holding time)

Fig. 1 Schematic illustration of multi-pass welding samples


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Table 4 Welding conditions for 6 layer-163 pass welding

No. Current ［A］

Voltage ［V］

Welding speed ［cm/min］

Heat input ［J/cm］

Number of weld beads

1st layer 210 9.5 12 6400 29 2nd layer 236 9.5 12 7500 28 3rd layer 240 9.5 12 7800 27 4th layer 240 9.5 12 7800 27 5th layer 240 9.5 12 7800 25 6th layer 240 9.5 12 7800 27

3. Thermal cycle variety of non-Consistent Layer technique

The HAZ is that portion of the parent metal adjacent to the weld that has not been melted but

whose microstructure and hence mechanical properties have been altered by the heat of welding. During welding there can be up to four sub-zones within the HAZ created according to the maximum temperature reached and the duration of time at that temperature. As illustrated in Fig. 2, these 4 sub-zones are: coarse grained HAZ (CGHAZ), fine grained HAZ (FGHAZ), inter-critical HAZ (ICHAZ) and sub-critical HAZ (SCHAZ). Among these, CGHAZ always has poor mechanical properties due to the increase in hardness and loss of toughness [1-3]. Therefore tempering is required to improve the mechanical properties. And the temper bead welding technique is an effective repair welding method.

Fig. 2 schematic illustration of comparison between CSL technique and non-CSL technique: (a) CSL technique and (b) Non-CSL technique

Fig. 2 presents a schematic illustration of comparison between CSL technique and non-CSL

technique. In CSL technique as shown in Fig. 2(a), the HAZ of the first layer is only tempered but has not retransformed by the subsequent thermal cycles of layers, because this technique places the Ac1 retransformation line of the second layer welding within the fusion zone produced by the first layer welding. Therefore, there are only tempering thermal cycles from the 2nd layer welding. However, in non-CSL technique as presented in Fig. 2(b), the Ac1 retransformation line of the second layer welding exceeds the fusion zone produced by the first layer welding. Therefore, the HAZ of the first layer welding has been retransformed by the subsequent layer.

The possible thermal cycles in the first layer welding are single cycle and double cycle. Because there are only tempering thermal cycle from the second layer welding in CSL technique, the four types of thermal cycle patterns in HAZ of temper bead welding by CSL technique are illustrated in Fig. 3 [5,7]. Fig. 3(a) shows the type of single thermal cycle, marked as 1-cycle. Fig. 3(b) presents the type of double thermal cycle, which is marked as 2-cycle. Figs. 3(c) and (d) illustrate the types of 1-cycle+temper and 2-cycle+temper, respectively.

However in non-CSL technique, the HAZ of the first layer welding has been retransformed by the subsequent layer, as a result, the possible thermal cycles subjected to HAZ of non-CSL technique are 3-cycle (+temper) or 4-cycle (+temper), as illustrated in Fig. 4. It means that the thermal cycles in HAZ of non-CSL technique are actually more complicated than that in HAZ of CSL technique.

(a) (b)


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Fig. 3 Four types of thermal cycles in temper bead welding produced by CSL technique: (a) 1-cycle, (b) 2-cycle, (c) 1-cycle+temper and (d) 2-cycle+temper

Ac1

Times (s)

Tem

pera

ture

(℃)

Tp1, CR1

Tp2,CR2

Tp3,CR3

Ac1

Times (s)

Tem

pera

ture

(℃)

Tp1,CR1

Tp3,CR3

Tp4,CR4

Tp2,CR2

Fig. 4 Thermal cycles in temper bead welding produced by non-CSL technique: (a) 3-cycle, (b) 3-cycle+temper, (c) 4-cycle and (d) 4-cycle+temper

The authors have constructed the hardness prediction system of HAZ in temper bead welding by

CSL technique using neural network (NN), based on the experimentally obtained hardness database of the 4 types of thermal cycle patterns: 1-cycle, 2-cycle, 1-cycle+temper and 2-cycle+temper [5]. For the 1-cycle, the hardness is mainly dependent on two factors: Tp and CR [1,8]. The 2-cycle is more complicated, with four factors governing the hardness: Tp1 and CR1 of the first thermal cycle, and Tp2 and CR2 of the second thermal cycle. Besides of these factors, the temper cycles are also expressed by TCTP (thermal cycle temper parameter) [5,7], which was proposed by the authors by extending the Larson-Miller parameter (LMP) to non-isothermal heat treatment. To apply LMP to temper thermal cycle process, the thermal cycle was divided into small increments where each increment could be regarded as an isothermal heat treatment with a “short” holding time. The overall tempering effect during the thermal cycle process is considered as the sum of the “short” isothermal heat treatments. The proposed TCTP has been proved can be applied to evaluate quantitatively the tempering effect and the hardness change during both thermal cycle tempering processes and multi-pass isothermal heat treatment [5]. Therefore, the 4 types of thermal cycle patterns in CSL technique are determined by the following 5 parameters: Tp1, CR1, Tp2, CR2 and TCTP.

However, the construction of the experimental hardness database of the complicated thermal cycle (3-cycle, 4-cycle+temper, etc) is quite difficult, because there are too many thermal cycle parameters to change which needs substantial experimental results. Therefore, extending the present hardness prediction system of CSL technique to non-CSL technique is considered through simplification of the weld thermal cycle patterns as discussed in the following section.

4. Simplification method of the complicated thermal cycle 4.1 Thermal cycle simplification method and hardness comparison

In general, mechanical properties (hardness, toughness et al.) after multi-pass thermal cycle are greatly affected by the later thermal cycle among the multi-pass thermal cycles [1,8]. The thermal cycle with the higher peak temperature is also considered have great effect on the hardness. Based on the assumption, the hardness subjected to the 3-cycle or 4-cycle is considered to be represented by that of the critical 2-cycle, by neglecting the other cycle with little effect on hardness.

As the result, three simplification methods were proposed as illustrated in Fig. 5. In simplification method 1, as shown in Fig. 5(a), the last 2-cycle were chosen instead of the multi-pass thermal cycle by neglecting the former thermal cycles. As presented in Fig. 5(b), in simplification method 2, the 2-cycle with the highest peak temperature were chosen instead of the multi-pass thermal cycle. While in simplification in method 3, as illustrated in Fig. 5(c), the last thermal cycle

(b) (c) (d) (a)

(d) (c) (b) (a)


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and the thermal cycle with the highest temperature were chosen as the representative thermal cycles.

Tp2Tp1

Ac1

Time (s)

Tem

pera

ture

(℃)

Tp3

Tp2

Ac1

Time (s)

Tem

pera

ture

(℃)

Tp3Simplification

(a) Schematic illustration of Method 1

Tp2Tp1

Ac1

Time (s)

Tem

pera

ture

(℃)

Tp3 SimplificationTp2

Tp1

Ac1

Time (s)

Tem

pera

ture

(℃)

(b) Schematic illustration of Method 2

Tp2Tp1

Ac1

Time(s)

Tem

pera

ture

(℃)

Tp3 Simplification Tp3

Tp1

Ac1

Time (s)

Tem

pera

ture

(℃)

(c) Schematic illustration of Method 3

Fig. 5 Schematic illustration of simplified methods of 3-cycle

The hardness subjected to the original 3-cycle and 4-cycle heated using high frequency induction heating device has been compared with that heated by the simplified 2-cycle using the above 3 proposed methods. The hardness comparison results are as shown in Figs. 6-8, with different cooling rates varied as water quenching (WQ), 60℃/s and 30℃/s. The peak temperatures of the last cycle are classified into three categories as different temperature ranges of CGHAZ, FGHAZ and ICHAZ. Fig. 6 shows the hardness comparison between the original complicated thermal cycle and the simplified 2-cycle with the cooling condition of WQ. The correlation coefficients between the hardness of original 3-cycle and the hardness of simplified 2-cycle for the Method 1, 2 and 3 are 0.88, 0.36 and 0.83, respectively. Among the 3 methods, the correlation coefficient for the Method 1 was the highest. Similarly, when the cooling rate was changed to be 60℃/s (shown in Fig. 7) and 30℃/s (shown in Fig. 8), the correlation coefficient for the Method 1 was also the highest. The correlation coefficients for the Method 1, 2 and 3 were summarized in Table 5. The result suggested that the simplification method 1 was the most reasonable one to predict HAZ hardness of low-alloy steel A533B for non-consistent layer technique.


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0

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(a) Method 1 (b) Method 2 (c) Method 3

Fig. 6 Hardness comparison of non-simplified and simplified CR=WQ

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Fig. 7 Hardness comparison of non-simplified and simplified CR=60℃/s

0

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C (H

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Fig. 8 Hardness comparison of non-simplified and simplified CR=30℃/s

Table 5 Correlation coefficient of hardness between the original and simplified thermal cycles

Cooling Rate Method 1 Method 2 Method 3 WQ 0.88 0.36 0.83

60℃/s 0.91 0.74 0.91 30℃/s 0.88 0.55 0.80

4.2 Effect of the first cycle on hardness and the austenite grain size

It is well known that the hardness of low-alloy steel is greatly dependent on the prior austenite

grain size [8-10]. Therefore, the effect of thermal cycle on the prior austenite grain size has been investigated. The peak temperature of the 1st thermal cycle (Tp1) was varied as 1350℃(CG), 1000℃(FG) and 750℃(IC) , and base metal (BM) was also included for comparison. The cooling rate used was 60℃/s. The relationship between austenite grain size and peak temperature of the 2nd thermal cycle (Tp2) is shown in Fig. 9. For all samples, when Tp2 was changed from 700℃ to 1000℃ (IC~FG range), the prior austenite grain size was about 4～8μm. While the grain size markedly

(a) Method 1 R=0.88

(b) Method 2 R=0.36

(c) Method 3 R=0.83

(a) Method 1 R=0.91

(b) Method 2 R=0.74

(c) Method 3 R=0.91

(a) Method 1 R=0.88

(b) Method 2 R=0.55

(c) Method 3 R=0.80


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increased to about 120μm at Tp2 of 1350℃(CG range). This indicates that the prior austenite grain size is mainly decided by the 2nd thermal cycle, and the effect of the 1st thermal cycle seems to be neglected in such conditions as considered.

The microstructures of A533B heated by 3-cycle with different Tp1, consistent Tp2 and Tp3 are presented in Fig. 10. When the Tp2 and Tp3 were same, the microstructures subjected to 3-cycle were similar to each other. Even in case of different Tp1, Tp1 seems have little effect on the microstructure, especially for the case with Tp2 higher than Ac3 (837℃). It also ensured the simplification method of thermal cycle by neglecting the first thermal cycle is reasonable.

Fig. 9 Relationship between the peak temperature of the 2nd thermal cycle and prior austenite grain size of A533B

Fig. 10 Microstructure of 3-cycle with different Tp1, consistent Tp2 and Tp3 4.3 Microstructure analysis for the validity of the thermal cycle simplification

Fig. 11 shows the schematic illustration of α-γ retransformation after double thermal cycle being

applied with the different 1st thermal cycles: (a), (b), (c) and (d) represent BM, Tp1=IC, Tp1=FG and Tp1=CG, respectively. When the peak temperature of the 2nd thermal cycle was higher than Ac3, both the grain size and the microstructure were similar because the phase transformation of α→γ has finished during heating at the peak temperatures. Therefore, the effect of the 1st thermal cycle can be neglected for all cases. When Tp2 was between Ac1 and Ac3, if the prior austenite grain size was


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similar, as shown in Figs. 11(a), (b) and (c), the newly generated austenite grain size was also similar to each other, while the phase transformation ratio of the austenite was little different for the case of different 1st thermal cycle. On the other hand, if the prior austenite grain size was very large as presented in Fig. 11(d), the austenite grain could grow without colliding with the other grains, as a result, both the grain size and the phase transformation ratio of the austenite were a little different. The temperature range between Ac1 and Ac3 of low-alloy steel A533B are very narrow (670℃~837℃), and when Tp2 is in the range between Ac1 and Ac3, the temperature has little effect on the hardness of A533B (the hardness only varied from 240～280HV) [5]. Above all, for low-alloy steel A533B, the effect of the 1st thermal cycle has little effect on the hardness of HAZ for 3-cycle being applied, and the hardness are almost determined by the last 2-cycle. Therefore, the thermal cycle simplification method by using the last 2-cycle is possible for the hardness prediction of HAZ for 3-cycle being applied, from the view of engineering.

Through the above simplification method, the hardness of HAZ for 3-cycle and 4-cycle being applied can be represented as that of the last 2-cycle, and the hardness of 3-cycle+temper and 4-cycle+temper can be deputized as that of the last 2-cycle+temper. As a result, the hardness in HAZ of temper bead welding with non-CSL technique also could be expressed as the hardness subjected to 1-cycle, 2-cycle, 1-cycle+temper and 2-cycle+temper.

Fig. 11 Schematic illustration of α-γ retransformation after double thermal cycle: (a) BM, (b) Tp1=IC, (c) Tp1=FG and Tp1=CG

5. Basic concept of hardness prediction of HAZ in temper bead welding with

non-Consistent Layer technique

5.1 Hardness prediction system for temper bead welding of non-Consistent Layer technique Fig. 12 shows the flow of hardness prediction system of HAZ in temper bead welding with

non-CSL technique, which is similar to that with CSL technique, except for the addition of “Simplification of thermal cycle” subsystem.

It includes following subsystems. Firstly, the thermal cycles in HAZ of temper bead welding using non-CSL technique are simulated by FEM. Secondly, the base metal (BM), HAZ and the weld


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metal (WM) are judged according to the peak temperatures of the simulated multi-pass thermal cycles. If all the peak temperatures of the multi-pass thermal cycle are lower than 400 °C, the position is considered as BM. If any peak temperature is over 1500 °C, then the position is classified as WM. All other positions, with at least one peak temperature during the thermal cycles between 400 °C and 1500 °C, are considered to be HAZ, which is the target region to be calculated. Thirdly, within the data of HAZ, according the thermal history, the complicated thermal cycles were simplified using the proposed simplification method expressed in chapter 4. Then, the thermal cycles at the grid nodes of the mesh in the samples for calculation are classified into four types: 1-cycle, 1-cycle+temper, 2-cycle, and 2-cycle+temper. Fourthly, the thermal cycle parameters (Tpi, CRi, and TCTP) will be fed into the proposed neural network (NN) based hardness prediction subsystem of CSL technique constructed by the authors [5], and the hardness at every grid node is calculated. According the calculated hardness at all grid nodes, the visual hardness distribution in HAZ of temper bead welding can be shown as color charts. Finally, the predicted hardness is compared with the experimentally measured one to verify the effectiveness of the hardness prediction system.

Fig. 12 Hardness prediction system for HAZ of temper bead welding by non-CSL technique 5.2 RBF-Neural Network used in present system

Because the hardness in HAZ subjected to various kinds of thermal cycles is determined by the

parameters: Tpi, CRi and TCTP [5], the prediction systems of hardness are constructed with using a Neural Network (NN). The NN method is a powerful tool which can process such complex data as involved in the present research.

NN [11,12] is a mathematical model or computational model that simulates the structure and/or functional aspects of biological neural networks. In most cases, a NN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks have become useful modeling tools for non-linear statistical data. They are usually used to model complex relationships between inputs and outputs or to find patterns in data.

The radial basis function (RBF) [13] is a powerful technique for interpolation of multidimensional space in a NN. Fig. 13 illustrates the RBF-NN model. RBF networks typically have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer. The hidden layer can be described by a Gaussian basis function:

(1)

where x is the input data, c is the center vector, and r is the Euclidean distance. In the basic form all inputs are connected to each hidden neuron.

The output O(xi) of the network is thus


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(2)

where n is the number of neurons in the hidden layer, cj is the center vector for neuron j, and wj are the weights of the linear output neuron. The weights wj, cj, and r are determined in a manner that optimizes the fit between O(xi) and the data. In the present study, the thermal cycle parameters (Tpi, CRi and TCTP) are the input data, and the hardness is the output data.

Fig. 13 Radial basis function neural network model 5.3 Temperature analysis in HAZ of temper bead welding by FEM

The temperature distributions produced by multi-pass thermal cycles in welds during temper

bead welding were calculated using three-dimensional finite element analysis code, developed specifically for welding simulation [6]. The mesh model is the same size with the experimental welding sample as shown in Fig. 14. The welding conditions were the same as the experimental conditions shown in Table 4, which followed the non-CSL technique. The temperature dependent physical properties of A533B and Alloy 690 are illustrated in Table 6 and Fig. 15, respectively.

Fig. 14 Mesh model for FEM analysis (6 layer-163 pass welding)

Table 6 Temperature dependent material properties of A533B steel

Temperature (℃)

Specific heat

(J/kg℃)

Thermal conductivity (W/mm℃)

Yield Strength (MPa)

Young’s modulus

(GPa)

Possion’s ratio (－)

Thermal expansion

(1/℃) 20 445 0.039 478 210 0.3 12.0e-6

200 517 0.0389 455 202 0.3 12.7e-6 400 592 0.036 405 188 0.3 13.9e-6 600 723 0.0317 238 160 0.3 13.8e-6 800 812 0.0378 75 115 0.3 12.6e-6

1000 658 0.0309 17 93 0.3 12.6e-6 1300 721 0.0365 5 10 0.3 14.5e-6


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Fig. 15 Temperature dependent physical parameters of Alloy 690.

6. Prediction result of hardness in HAZ of temper bead welding with non-Consistent Layer technique using neural network

6.1 FEM simulation result of temper bead welding

Figs. 16-18 present the comparison between the calculated peak temperature distribution in the

middle section and the actual welded sample section after 1 layer-29 pass welding, 3 layer-84 pass welding and 6 layer-163 pass welding, respectively. Peak temperatures are presented in different color scale. Figs. 16-18 indicate that the simulated temperature distributions are similar to the experimental results, which suggests the FEM simulation results are effective.

Fig. 16 Comparison between (a) simulated peak temperature distribution after 1 layer-29 pass welding and (b) the experimental sample



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6.2 Predicted hardness distribution of temper bead welding

Based on the calculated thermal history at all grid nodes of the mesh, the hardness at the grid

nodes was calculated. Thus calculated hardness distribution in the HAZ is visualized as color chart maps in Fig. 19. Fig. 19(a) illustrates the hardness distribution in HAZ of 1 layer-29 pass welding. WM and BM are shown in pink and grey respectively in Fig. 19. The hardness in HAZ is shown with rainbow colors depending on the hardness levels. It can be seen that there are hardened ranges in HAZ with the hardness higher than 350HV in the HAZ of 1-layer welding. Compared with it, the hardness was greatly decreased after 3-layer welding and 6-layer welding, as shown in Figs. 19(b) and (c). Especially after 6-layer welding, the hardened range has completely disappeared. This illustrates the effectiveness of temper bead welding on decreasing hardness.

Fig. 19 Calculated hardness distribution after temper bead welding: (a) 1 layer-29 pass welding, (c) 3 layer-84 pass welding and (b) 6 layer-163 pass welding.

6.3 Validity of hardness prediction system of non-CSL technique

The position of measuring hardness is shown in Fig. 20. Near to the weld metal, the hardness is

measured along both the horizontal and vertical lines, and the distance of the measurement is 0.25 mm. The predicted hardness and the experimentally measured results for the HAZ of A533B low-alloy steel after 1-layer, 3-layer and 6-layer welding are shown in Figs. 21-23, respectively. The blue points are the experimentally measured hardness, and the red points are the calculated hardness using the currently proposed hardness prediction system, based on the FEM simulated thermal history. The predicted hardness was in good accordance with the experimentally measured hardness. It follows that the proposed hardness prediction system is useful and effective for estimating the tempering effect in temper bead welding of non-CSL technique.

(a) (b)

(c)


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Fig. 20 Hardness measured position

050

100150200250300350400450

0 1 2 3 4

Har

dnes

s, H

VDistance from bead boundary (mm)

縦

Mea. HVCal. HV

050

100150200250300350400450500

-6 -4 -2 0 2 4 6

Har

dnes

s, H

V

Distance from centre of WM (mm)

横

Mea. HVCal. HV

(a) Horizontal direction (b) Vertical direction Fig. 21 Comparison of calculated and measured hardness (1 layer-29 pass welding)



6.4 Benefits of the proposed method

Above all, through this method, the hardness can be predicted before the temper bead welding is

actually conducted. That is, if the calculated hardness in HAZ is higher than the critical value, for instance more than 350HV, the welding conditions should be modified. Thus, the appropriate welding


47

conditions can be selected before the actual welding. Therefore, the presently proposed method is useful for assessment of tempering effect of temper bead welding techniques. 7. Conclusions

A neural network-based prediction system for the hardness in HAZ when temper bead welding of

non-CSL technique is applied has been investigated. The following conclusions can be drawn: (1) The thermal cycles in heat affect zone (HAZ) of non-CSL technique are more complicated than

those of CSL techniques. The hardness of HAZ after the 3-cycle being applied can be represented as that of the simplified last 2-cycle, of which validity was also ensured by the microstructure analysis.

(2) Through simplifying the complicated thermal cycles in non-CSL technique to 4 typical patterns of thermal cycles, the neural network-based hardness prediction system for temper bead welding of non-CSL technique has been constructed.

(3) The hardness distribution in HAZ of non-CSL technique was calculated based on the thermal cycles numerically obtained by FEM. The predicted hardness was in good accordance with the experimental results. It follows that the currently proposed methods are effective for estimating the tempering effect during temper bead welding of non-CSL techniques.

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Vessel Steel”, Nuclear Engineering and Design, Vol.183, pp. 9-20 (1998). [2] Y. Nakao, H. Ohige, S. Noi, Y. Nishi: “Distribution of Toughness in HAZ of Multi-Pass Welded High

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[9] S. J. Lee, Y. K. Lee: “Prediction of austenite grain growth during austenitization of low alloy steels”, Materials and Design, Vol.29, pp. 1840-1844 (2008).

[10] K. Poorhaydari, D. G. Ivey: “Application of carbon extraction replicas in grain-size measurements of high-strength steels using TEM”, Materials Characterization, Vol.58, pp.544-554 (2007).

[11] V. D. Manvatkar, A. Arora, A. De, T. DebRoy: “Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding”, Science and Technology of Welding & Joining, Vol.17 , pp. 460-466 (2012).

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