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

Transcription

1 E-Journal of Advanced Maintenance Vol. 6 (214) Japan Society of Maintenology Hardness Prediction of HAZ in Temper Bead Welding Lina YU 1,*, Kazuyoshi SAIDA 1, Masahito MOCHIZUKI 1, Masashi KAMEYAMA 2, Naoki CHIGUSA 3 and Kazutoshi NISHIMOTO 4 1 Osaka University, 2-1 Yamadaoka, Suita, Osaka , Japan 2 Japan Nuclear Safety Institute, Shiba, Minato-ku, Tokyo 18-14, Japan 3 The Kansai Electric Power Co., Inc., 8 Yokota, 13 Goichi, Mihama-cho, Mikata-gun, Fukui , Japan 4 Fukui University of Technology, Gakuen 3-6-1, Fukui-shi, Fukui , 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. KEYWORDS Hardness, Temper bead welding, Consistent layer technique, Low-alloy steel, FEM ARTICLE INFORMATION Article history: Received 18 April 214 Accepted 15 July 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 retransformation line of the 2 nd layer welding within fusion zone of the 1 st layer welding, as a result, HAZ of the 1 st 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, * Corresponding author, yulina@mapse.osaka-u.ac.jp ISSN /1 21 JSM and the authors. All rights reserved. 34

2 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 69 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, T pi is the peak temperature of the i th pass thermal cycle, and CR i means the cooling rate from 8 C to 5 C of the i th 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 2 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 Inconel69 Material Chemical composition (mass %) C Si Mn P S Ni Cu Cr Mo Ti Al Fe Co A533B Bal. - Inconel Bal Table 2 Thermal cycle conditions for the simulated thermal cycles of CSL technique Thermal cycle 1 st 2 nd Temper cycle Peak Temperature, T pi ( ) ~135 67~135 ~65 Cooling rate, CR i ( /s) 3, 3,6,1 3, 3,6,1 5s~1hr (Holding time) Table 3 Thermal cycle conditions for the simulated thermal cycles of non-csl technique Thermal cycle 1 st 2 nd 3 rd 4 th Temper cycle Peak Temperature, ~135 67~135 67~135 67~135 ~65 T pi ( ) Cooling rate, CR i ( /s) 3, 3,6,1 3, 3,6,1 3, 3,6,1 3, 3,6,1 5s~1hr (Holding time) Fig. 1 Schematic illustration of multi-pass welding samples 35

3 Table 4 Welding conditions for 6 layer-163 pass welding No. Current Voltage Welding speed Heat input Number of [A] [V] [cm/min] [J/cm] weld beads 1 st layer nd layer rd layer th layer th layer th layer 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. (a) (b) 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 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 2 nd layer welding. However, in non-csl technique as presented in Fig. 2(b), the 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 (+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. 36

4 (a) (b) (c) (d) 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 (a) Temperature( ) Tp 1, CR 1 Tp 2, CR 2 Tp 3, CR 3 (b) (c) Temperature( ) Tp 1, CR 1 Tp 3, Tp 2, CR 2 CR 3 Tp 4, CR 4 (d) Times (s) Times (s) Fig. 4 Thermal cycles in temper bead welding produced by non-csl technique: (a) 3-cycle, (b) 3-cycle+temper, (c) and (d) +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: T p and CR [1,8]. The 2-cycle is more complicated, with four factors governing the hardness: T p1 and CR 1 of the first thermal cycle, and T p2 and CR 2 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: T p1, CR 1, T p2, CR 2 and TCTP. However, the construction of the experimental hardness database of the complicated thermal cycle (3-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 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 37

5 and the thermal cycle with the highest temperature were chosen as the representative thermal cycles. Temperature ( ) Tp 1 Tp 2 Tp 3 Tp 2 Time (s) Simplification Temperature ( ) Tp 3 Time (s) (a) Schematic illustration of Method 1 Temperature ( ) Tp 1 Tp 2 Tp 3 Time (s) Simplification Temperature ( ) Tp 1 Tp 2 Time (s) (b) Schematic illustration of Method 2 Temperature ( ) Tp 1 Tp 2 Tp 3 Time(s) Simplification Temperature ( ) Tp 1 Tp 3 Time (s) (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 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), 6 /s and 3 /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.88,.36 and.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 6 /s (shown in Fig. 7) and 3 /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. 38

6 Hardness of hte simplified TC (HV 1/2) 5 1 (a) Method 1 R=.88 Hardness of hte simplified TC (HV 1/2) 5 1 (b) Method 2 R=.36 Hardness of hte simplified TC (HV 1/2) 5 1 (c) Method 3 R= Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) (a) Method 1 (b) Method 2 (c) Method 3 Fig. 6 Hardness comparison of non-simplified and simplified CR=WQ Hardness of hte simplified TC (HV 1/2) 5 1 (a) Method 1 R=.91 Hardness of hte simplified TC (HV 1/2) 5 1 (b) Method 2 R=.74 Hardness of hte simplified TC (HV 1/2) 5 1 (c) Method 3 R= Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) (a) Method 1 (b) Method 2 (c) Method 3 Fig. 7 Hardness comparison of non-simplified and simplified CR=6 /s Hardness of hte simplified TC (HV 1/2) 5 1 (a) Method 1 R=.88 Hardness of hte simplified TC (HV 1/2) 5 1 (b) Method 2 R=.55 Hardness of hte simplified TC (HV 1/2) 5 1 (c) Method 3 R= Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) 1 5 Hardness without simplification (HV 1/2) (a) Method 1 (b) Method 2 (c) Method 3 Fig. 8 Hardness comparison of non-simplified and simplified CR=3 /s Table 5 Correlation coefficient of hardness between the original and simplified thermal cycles Cooling Rate Method 1 Method 2 Method 3 WQ /s /s 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 [8-1]. Therefore, the effect of thermal cycle on the prior austenite grain size has been investigated. The peak temperature of the 1 st thermal cycle (Tp 1 ) was varied as 135 (CG), 1 (FG) and 75 (IC), and base metal (BM) was also included for comparison. The cooling rate used was 6 /s. The relationship between austenite grain size and peak temperature of the 2 nd thermal cycle (Tp 2 ) is shown in Fig. 9. For all samples, when Tp 2 was changed from 7 to 1 (IC~FG range), the prior austenite grain size was about 4~8μm. While the grain size markedly 39

7 increased to about 12μm at Tp 2 of 135 (CG range). This indicates that the prior austenite grain size is mainly decided by the 2 nd thermal cycle, and the effect of the 1 st thermal cycle seems to be neglected in such conditions as considered. The microstructures of A533B heated by 3-cycle with different Tp 1, consistent Tp 2 and Tp 3 are presented in Fig. 1. When the Tp 2 and Tp 3 were same, the microstructures subjected to 3-cycle were similar to each other. Even in case of different Tp 1, Tp 1 seems have little effect on the microstructure, especially for the case with Tp 2 higher than Ac 3 (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 2 nd thermal cycle and prior austenite grain size of A533B Fig. 1 Microstructure of 3-cycle with different Tp 1, consistent Tp 2 and Tp 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 1 st thermal cycles: (a), (b), (c) and (d) represent BM, Tp 1 =IC, Tp 1 =FG and Tp 1 =CG, respectively. When the peak temperature of the 2 nd thermal cycle was higher than Ac 3, 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 1 st thermal cycle can be neglected for all cases. When Tp 2 was between and Ac 3, if the prior austenite grain size was 4

8 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 1 st 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 and Ac 3 of low-alloy steel A533B are very narrow (67 ~837 ), and when Tp 2 is in the range between and Ac 3, the temperature has little effect on the hardness of A533B (the hardness only varied from 24~28HV) [5]. Above all, for low-alloy steel A533B, the effect of the 1 st 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 being applied can be represented as that of the last 2-cycle, and the hardness of 3-cycle+temper and +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) Tp 1 =IC, (c) Tp 1 =FG and Tp 1 =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 41

9 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 C, the position is considered as BM. If any peak temperature is over 15 C, then the position is classified as WM. All other positions, with at least one peak temperature during the thermal cycles between C and 15 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 (T pi, CR i, 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: T pi, CR i 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: 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(x i ) of the network is thus (1) 42

10 where n is the number of neurons in the hidden layer, c j is the center vector for neuron j, and w j are the weights of the linear output neuron. The weights w j, c j, and r are determined in a manner that optimizes the fit between O(x i ) and the data. In the present study, the thermal cycle parameters (T pi, CR i and TCTP) are the input data, and the hardness is the output data. (2) 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 69 are illustrated in Table 6 and Fig. 15, respectively. Temperature ( ) Specific heat (J/kg ) Fig. 14 Mesh model for FEM analysis (6 layer-163 pass welding) Table 6 Temperature dependent material properties of A533B steel Thermal conductivity (W/mm ) Yield Strength (MPa) Young s modulus (GPa) Possion s ratio (-) Thermal expansion (1/ ) e e e e e e e-6 43

11 Fig. 15 Temperature dependent physical parameters of Alloy 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 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 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 Fig. 17 Comparison between (a) simulated peak temperature distribution after 3 layer-84 pass welding and (b) the experimental sample 44

12 Fig. 18 Comparison between (a) simulated peak temperature distribution after 6 layer-163 pass welding and (b) the experimental sample 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 35HV 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. (a) (b) (c) 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. 2. Near to the weld metal, the hardness is measured along both the horizontal and vertical lines, and the distance of the measurement is.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 , 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. 45

13 Hardness, HV 横 Fig. 2 Hardness measured position Mea. HV Cal. HV Distance from centre of WM (mm) Hardness, HV 縦 Mea. HV Cal. HV Distance from bead boundary (mm) (a) Horizontal direction (b) Vertical direction Fig. 21 Comparison of calculated and measured hardness (1 layer-29 pass welding) (a) Horizontal direction (b) Vertical direction Fig. 22 Comparison of calculated and measured hardness (3 layer-84 pass welding) (a) Horizontal direction (b) Vertical direction Fig. 23 Comparison of calculated and measured hardness (6 layer-163 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 35HV, the welding conditions should be modified. Thus, the appropriate welding 46

14 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. References [1] J. Liao, K. Ikeuchi, F. Matsuda: Toughness Investigation on Simulated Weld HAZs of SQV-2A Pressure Vessel Steel, Nuclear Engineering and Design, Vol.183, pp. 9-2 (1998). [2] Y. Nakao, H. Ohige, S. Noi, Y. Nishi: Distribution of Toughness in HAZ of Multi-Pass Welded High Strength Steel, Quarterly Journal of the Japan Welding Society, Vol.3, pp (1985). [3] N. Yurioka, Y. Horii: Recent developments in repair welding technologies in Japan, Science and Technology of Welding & Joining, Vol.11, pp (6). [4] R. Viswanathan, D. W. Gandy, S. J. Findlan: Temper Bead Welding of P-Nos. 4 and 5 Materials, EPRI TR , Final Report, December (1998). [5] L. Yu, Y. Nakabayashi, M. SaSa, S. Itoh, M. Kameyama, S. Hirano, N. Chigusa, K. Saida, M. Mochizuki and K. Nishimoto: Neural network prediction of hardness in HAZ of temper bead welding using the proposed thermal cycle tempering parameter (TCTP), ISIJ International, Vol.51, pp (211). [6] D. Deng, H. Murakawa, M. Shibahara: Investigations on welding distortion in an asymmetrical curved block by means of numerical simulation technology and experimental method, Computational Materials Science, Vol.48, pp (21). [7] L. Yu, M. SaSa, K. Ohnishi, M. Kameyama, S. Hirano, N. Chigusa, T. Sera, K. Saida, M. Mochizuki and K. Nishimoto: Neural network-based toughness prediction in HAZ of low-alloy steel produced by temper bead welding repair technology, Science and Technology of Welding and Joining, Vol.18, pp (213). [8] R. Mizuno, P. Brziak, M. Lomozik, F. Matsuda: Appropriate welding conditions of temper bead weld repair for SQV2A pressure vessel steel, Proceedings of the 3th MPA-Seminar in conjunction with the 9th German-Japanese Seminar, Stuttgart, Germany (4). [9] S. J. Lee, Y. K. Lee: Prediction of austenite grain growth during austenitization of low alloy steels, Materials and Design, Vol.29, pp (8). [1] 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 (7). [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 (212). [12] E. A. Metzbower, D. L. Olson, N. Yurioka: Neural network analysis of oxygen in weld metals, Science and Technology of Welding & Joining, Vol.14, pp (9). [13] D. Casasent, X. Chen: Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification, Neural Networks, Vol.16, pp (3). 47