Materials and Design

Transcription

1 Materials and Design 34 (2012) Contents lists available at ScienceDirect Materials and Design journal homepage: The modeling and process analysis of resistance spot welding on galvanized steel sheets used in car body manufacturing S.M. Hamidinejad a,, F. Kolahan b, A.H. Kokabi c a Department of Mechanical Engineering, Islamic Azad University, Eghlid Branch, Eghlid, Iran b Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran c Materials Science and Engineering Department, Sharif University of Technology, Tehran, Iran article info abstract Article history: Received 11 April 2011 Accepted 28 June 2011 Available online 7 July 2011 Keywords: A. Ferrous metals and alloys D. Welding G. Destructive testing In this study, the resistance spot welding (RSW) process of the galvanized interstitial free (IF) steel sheets and galvanized bake hardenable (BH) steel sheets, used in the manufacturing of car bodies, has been modeled and optimized. The quality measure of a resistance spot welding joint is estimated from the tensile shear strength. Furthermore, four important process parameters, namely welding current (WC), welding time (WT), electrode force (EF), and holding time (HT) are considered as the factors influencing the quality of the joints. In order to develop an accurate relationship between the process inputs (4-component vectors) and the response output (tensile shears strength) at first a linear regression model was utilized but the residuals analysis revealed a non-linear behavior. Therefore, an artificial neural network (ANN) was proposed because the ANNs are capable of mapping the non-linear systems. A back propagation neural network model was developed to analyze RSW process and the interaction effects of the parameters. In the second phase of this research, Genetic Algorithm with the fitness function based on an ANN model was employed as an optimization procedure for determining a set of process parameters; as a result, the maximum joint strength was obtained. Optimization results showed high compatibility with the actual experimental data. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Resistance spot welding (RSW) is an efficient joining process widely used for the fabrication of sheet metal assemblies. RSW has excellent techno-economic benefits such as low cost, high speed and suitability for automation which make it an attractive choice for auto-body assemblies, truck cabins, rail vehicles and home appliances [1]. Due to the large number of spot welds in a particular application (for example in a car body), the process parameters of RSW need to be fine tuned [2]. Moreover, the weldability of galvanized steel sheet is more demanding than that of ordinary steel sheets for the existence of spatter generating and electrode pollution during the spot welding. This limits the application of galvanized steel sheets and the large-scale automatic fabrication of automotive products [3]. Like any other welding process, the quality of the joint in RSW is directly influenced by welding input parameters. A common problem faced by manufacturer is the control the process input parameters to obtain a well welded joint with required strength [4]. Thus, finding the relationships between the strength of spot weld and process parameters is of great interest in related Corresponding author. Tel.: ; fax: addresses: hamidinejad@iaueghlid.ac.ir, hamidi.mahdi@gmail.com (S.M. Hamidinejad). industrial applications. Structures employing RSW joints are usually designed so that these joints are loaded in shear even if the parts are exposed to tension or compression loading [5]. Therefore, the tensile shear strength of spot weld is an important index to welding quality [6]. Static tensile shear test is the most common laboratory test used to determine weld strength because of its simplicity [7]. In recent years, analytical and numerical methods have been employed to model welding processes and to establish the relationships between different weld quality indicators and process parameters. Specifically, several research works are reported on using artificial neural networks (ANNs) to model various welding techniques. ANNs are mathematical models that imitate the behavior of the biological nervous system. They have parallel, distributed and adaptive processing capable of mapping non-linear and complex systems in which the regression methods have their limitations [8,9]. Ates [10] presented a technique based on artificial neural networks (ANNs) to model gas metal arc welding parameters. The proposed ANN predicts mechanical properties of the weldment such as tensile strength, impact strength, elongation and weld metal hardness. Fratini et al. [11] linked ANN to a finite element model (FEM) to estimate average grain size values in the friction stir welding (FSW) process. Based on experimental results, Cevika et al. [12] proposed an ANN to determine the ultimate capacity of arc spot welding. The ultimate capacity of arc spot /$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.matdes

2 760 S.M. Hamidinejad et al. / Materials and Design 34 (2012) welding is modeled in terms of weld strength, average welding thickness and diameter. Martín et al. [2] developed an ANN to interpret ultrasonic oscillograms and to classify the respective spot weld in a certain quality level. In another study, Martín et al. [13] proposed an ANN to predict the tensile shear strength of the 304 austenitic stainless steel RSW welded joints. They investigated the effect of three process parameters namely welding time, welding current and electrode force, on the tensile shear strength. With regard to the points mentioned, in this study at first a linear regression model has been proposed, but the residuals analysis revealed the inherent nonlinearity behavior of the RSW process. Therefore, an artificial neural network (ANN) is developed to predict spot weld quality measure (tensile shear strength). The present study differ from the existing research in the sense that we have considered two different materials; namely galvanized interstitial free (IF) and galvanized bake hardenable (BF) steel sheets. Moreover, the interaction effects of RSW parameters are analyzed. The important process parameters considered here include welding current, welding time, electrode force and holding time. Next, a Genetic Algorithmic (GA) procedure has been employed to determine optimal process parameter values for desired tensile shear strength. The optimization results are then verified against actual experimental data which revealed that they are satisfactory. 2. Experimental procedure 2.1. Materials and equipment The materials used in experiments are commercially available galvanized steel sheet widely used in car fabrication. Interstitial free (IF) and bake hardenable (BH) steel sheets are lately developed materials with superior properties. Due to the presence of alloying elements such as Mn and Si, they have excellent formability and mechanical properties. They are appropriate for galvanizing and annealing to produce specialized sheets of steel, which is required for automotive body manufacturing [14]. The chemical composition of the IF and BH sheets is shown in Tables 1 and 2. The mechanical properties of the IF and BH sheets is shown in Table 3. The sheets thickness is 0.67 mm. IF and BH sheets are welded in a single-phase AC 50 Hz equipment by using water cooled type B (Dome) RWMA electrodes [15] with 7 mm face diameter RSW process parameters Experimental data were gathered using Design of Experiment (DOE) approach. The full factorial and central composite design tables were combined together which resulted in 124 different welding tests. To determine the feasible working limits of welding conditions, several trial tests were carried out. Different combinations of RSW parameters were used in the trial runs. The weld penetration and nugget appearance were inspected to identify the appropriate ranges of the welding parameters. In this regard, it was observed that if the welding times are less than 8 cycles, there would be lack of fusion and incomplete penetration. Actually, such cycles produced very small nuggets. On the other hand, welding times greater than 12 cycles resulted in weld splash and spatter as well as penetration of the electrodes into the workpiece and workpiece crushing. Also, the welding currents less than 10 ka resulted in incomplete penetration and lack of fusion. For currents greater than 12 ka, weld splash and spatter would occur. The limits for electrode force and holding time were determined in similar fashion. The considered process parameters and their ranges are shown in Table 4. Table 4 Experimental variable levels. Experimental variables Low level ( 1) High level (+1) WC Welding current (ka) EF Electrode force (kgf) WT Welding time (cycle(1/50 s)) HT Holding time (cycle(1/50 s)) Table 1 Chemical composition of the interstitial free (IF) steel sheets (wt%). C Si S P Mn Ni Cr Mo V Cu Al Nb Zn Ti Table 2 Chemical composition of the bake hardenable (BH) steel sheets (wt%). C Si S P Mn Ni Cr Mo V Cu Al Nb Zn Ti Table 3 Mechanical properties of the IF and BH steel sheets. Yield strength (Mpa) Ultimate strength (Mpa) Before baking After baking Before baking After baking IF BH Fig. 1. The tensile shear strength definition and calculation from laboratory test. D is eccentricity and d is thickness.

3 S.M. Hamidinejad et al. / Materials and Design 34 (2012) Fig. 2. Dimensions of spot welded tensile shear test specimens Tensile shear test Tensile shear strength is an important measure of welding quality in RAW [3]. Therefore, in this research tensile shear strength has been selected to describe the mechanical properties of spot weld. Referring to Fig. 1, there is an eccentricity D between two tensile axes of the overlap joints. The tensile stress and shear stress are both playing a role during tension test to spot weld because of the eccentricity [3]. A tensile shear test specimen was spot welded for each of the 124 welding conditions mentioned above. The specimens were prepared according to ISO [16] (Fig. 2). In order to increase the accuracy and the confidence level, the experiment related to each of the parameters combinations was carried out three times and their average value reported as the strength related to this parameters combination. The tensile shear tests were carried out at a crosshead of 20 mm/min with a Zwick (Z050) universal testing machine. During the tests, three types of breaking failure were observed: (1) separation; (2) knotting; (3) tearing. Samples of them are shown in Fig Results and discussion 3.1. Linear regression model Table 5 Model summery. S R square Adjusted R square % 69.00% Table 6 ANOVA. df Sum of squares Mean of square F Significant Regression 4 4,728,340 1,182, Residual Error 119 2,021,045 16,984 Total 123 6,749,385 Table 7 Coefficients. Coefficients SE coefficients T Significant Constant EF WC WT HT Fig. 4. Residual plot. Fig. 3. Breaking types observed in tensile shear test; (A): separation type breaking; (B): knotting type breaking; (C): tearing type breaking. A linear regression model was developed to relate the tensile shear strength of the RSW joints to the four welding parameters; WT, WC, EF and HT. For regression analysis Minitab was used. Tables 5 7 illustrate the results of the regression analysis. Table 7 shows that the regression is significant at the 95% confidence level (p = 0.000). Table 6 shows that EF (p = 0.089) and HT (p = 0.266) are not significant. Nevertheless, the coefficient of determination (R 2 = 70%) and the residuals analysis (Fig. 4) for this model reveal non-linear behavior of the process. Hence, in the next section an ANN model is proposed.

4 762 S.M. Hamidinejad et al. / Materials and Design 34 (2012) The proposed ANN model Traditional modeling methods are mostly relied on assumptions for model simplifications, and consequently may lead to inaccurate results. The ANN captures the underlying trend of the data set presented to it, in the form of a complex non-linear relationship between the input parameters and the output variable [9]. The characteristics of the ANNs make them suitable for modeling the strength of a resistance spot welding joint, and therefore it was used as the modeling tool in this research The artificial neural network architecture In this paper, a multilayer back propagation feed forward ANN, implemented and trained using the Neural Network Toolbox in the MATLAB Ò 7.4 package, has been used. While BPNs can have many layers, but all pattern recognition and classification tasks can be performed with a three-layer BPN [17].The Bayesian regularization algorithm (called trainbr in MATLAB Ò ) was used for training the Table 8 Assessment of the ability to generalize of the ANN. Input EF (kgf) WC (ka) WT (cycle) HT (cycle) Output Experiment value Predicted value ANN. Bayesian regularization is a network training function that updates the weight and bias values according to Levenberg Marquardt optimization. It minimizes a combination of squared errors and weights, and then determines the correct combination so as to produce a network that generalizes well. An ANN learns by means of training the same as a biological nervous system does. In this research, a supervised learning mechanism was utilized in training of the ANN; thus each input should come with its respective desired output. The inputs are 4-component vectors, a component for each of the welding parameters, WT, WC, EF and HT. Each target is the tensile shear strength of the RSW joint obtained with the respective input. The overfitting phenomenon may occur in the training when the ANN memorizes the training data instead of building an input output mapping for the problem in question. Consequently, the overfitting problem results in drop of the ability to generalize the ANN. The data were organized in input/output pairs. The total data set had 124 pairs and was randomly divided into two subsets [18 20]: Training subset: With 104 input/target pairs for training the ANN. In the training, the synaptic weights are repetitively updated to decrease an error function. Fig. 5. Plot of ANN outputs vs. experimental outputs. Fig. 6. Interaction between welding current and welding time, electrode force is 225 kgf and holding time is 12 cycles.

5 S.M. Hamidinejad et al. / Materials and Design 34 (2012) Validation subset: With 20 input/target pairs for evading overfitting and attaining good generalization by means of cross validation. Training stops if the error with regards to validation subset starts to increase (early stopping). The performance of the ANNs depends on the number of hidden layers and number of neurons in them. So, many trials need to be made to find the optimum structure for the neural network by changing the number of hidden layers and also the number of neurons in each of them. The proper neural network structure for predicting the tensile shear strength of the RSW joint was chosen by trial-and-error method. In this paper, the number of neurons in the input and output layers of the ANN are four and one respectively. There is a hidden layer with 10 neurons. The transfer function for the hidden layer is the Log-sigmoid transfer function, called logsig in MATLAB Ò ; the transfer function for the output layer is the identity function, called purelin in MATLAB Ò. The 20 input/target pairs used for cross validation were also employed as well for estimating the ability to generalize the previously trained ANN. The 20 inputs were presented to the ANN and an experimental output was attained for each input, Table 8. As Fig. 5 depicts, the network outputs (ANN-output) are plotted versus the experimental outputs (E-output) as open circles. The best linear fit is indicated by a dashed line. The perfect fit (network outputs equal to experimental outputs) is indicated by the solid line. It is difficult to distinguish the best linear fit line from the perfect one because the fit is so good. In this model, the correlation coefficient (R-value), which is a measure of how well the variation in the network output is explained by the experimental output, is The result indicates that the ANN has a high performance, and it can accurately map the relationship between the tensile shear strength of the RSW joint and the process parameters Analysis of process parameters A major advantage of ANN is that it can take into account the interaction effects of process parameters. Due to the complicated effects of interactions, it is important to pay more attention to the marching of process variables during the welding process design [3]. In this regard, the ANN model has high capability to predict the tensile shear strength in RSW joints and hence is employed in this study to evaluate the interaction effects of parameters. For this purpose, the mathematical function developed by the ANN was extracted and the effect of the process parameters and their interactions on the tensile shear strength was illustrated by 3D surfaces and their contours. The effects of six interactions were shown in Figs The surfaces and their contours reveal Fig. 7. Interaction between welding current and electrode force, welding time is 12 cycles and holding time is 12 cycles. Fig. 8. Interaction between welding current and electrode force, welding time is 8 cycles and holding time is 12 cycles.

6 764 S.M. Hamidinejad et al. / Materials and Design 34 (2012) the variations in the tensile shear strength by the actions of two variables (two out of the four parameters remained constant). The physical weld attributes such as fusion zone size (FZS), weld penetration and electrode indentation are the most important parameters governing the mechanical performance of resistance spot welds. It has been shown that welding current and welding time significantly affect these characteristics [21]. For instance, volume of melted metal is a function of heat input which is governed by the welding parameters including welding current and welding time. Fig. 6 demonstrates the interaction effect of welding current and welding time (EF is 225 kgf and HT is 12 cycles). As illustrated, within the range of 8 10 cycles, by increasing the welding current the strength increases at a sharp ascending rate and then remains constant in the ka welding current range. On the other hand, at higher welding times (10 12 cycles), by increasing the welding current to 11 ka the strength increases and then decreases. This happens due to the fact that more resistance heat is generated by increasing the welding current and time. Moreover, the liquid metal during nugget forming is crushed to turn into a spatter under the action of the excessive resistance heat generated by the large welding current and electrode force, thereby decreasing the nugget size. In fact, the input heat melts and softens the welding sheets and, if the electrode force remains constant, a deeper imprint is created. A spike in electrical current results in a large imprint and a potential burn-through effect on thin metal plates [22]. Also, the appearance of the welds is important, particularly on the high condition where the spatter is excessive [23]. Furthermore, because of significant resistance heat the overheating microstructure is generated in the heat affected zone (HAZ) and then the mechanical properties of the spot weld decreases as a result [3]. Fig. 7 demonstrates the interaction effect of welding current and electrode force (WT and HT are 12 cycles). The role of the electrode force in forming nuggets in the RSW process is crucial, especially regarding the galvanized steel sheets. As can be seen, in general, by increasing the welding current, at first the strength increases and after reaching a maximum amount, it decreases. At higher electrode force there is a further decrease in strength because of excessive electrode force leading to zinc coat with low melting point to melt and to accumulate around the electrode to enlarge the contact area between workpieces and electrodes. Thus, the resistant heat decreases because of lower current density with this phenomenon, and then the nugget size decreases with this effect, which is also disadvantageous to the quality of RSW because of the decreasing of effective area to load and mechanical properties of spot weld. Another important reason for the increase in Fig. 9. Interaction between welding time and electrode force, welding current is 12 ka and holding time is 12 cycles. Fig. 10. Interaction between welding current and holding time, welding time is 12 cycles and electrode force is 225 kgf.

7 S.M. Hamidinejad et al. / Materials and Design 34 (2012) Fig. 11. Interaction between electrode force and holding time, welding current is 10 ka and welding time is 12 cycles. strength at higher welding currents is the excessive resistance heat generated and the liquid metal spattering. Under these circumstances, due to the penetration of the electrodes into the workpiece and the workpiece crushing, the strength decreases. In order to investigate the interaction effect of EF and WC more accurately, Fig. 9 is presented. By comparing Fig. 7 with Fig. 8, it can be concluded that the effect of the electrode force at higher welding currents depends greatly on the welding time because at the higher welding time (12 cycles) the increase in the electrode force contributes to a decrease in strength. This is also due to the penetration of the electrodes into the workpiece and the workpiece crushing. However, by decreasing the welding time (8 cycles- Fig. 8) the decrease in strength is not observed any more. In other words, by decreasing the welding time the electrode force parameter causes much fewer variations in the strength. Fig. 9, which illustrates the interaction effect between WT and EF (WC is 12 ka and WT is 12 cycles), also confirms this fact. Fig. 10 demonstrates the interaction effect of welding current and holding time (EF is 225 kgf and WT is 12 cycles). As can be seen, by increasing the current from the lowest level, the strength increases and after reaching a maximum amount, it decreases. This is because of the overheating microstructure which is generated in the heat affected zone (HAZ). However, as can be seen in this figure, the holding time has little impact on the quality index. Since the alloys used in this research are low-carbon steels, the holding Fig. 12. Interaction between welding time and holding time, welding current is 12 ka and electrode force is 210 kgf. time, which is regarded as heat treatment, does not make noticeable changes to the microstructure of the nuggets and HAZ. Therefore, lack of considerable dependence of strength on the holding time is reasonable. Figs. 11 and 12 show the interaction effect of electrode force and holding time (WC is 10 ka and WT is 12 cycles) and the interaction effect of welding time and holding time (EF is 210 kgf and welding current is 12 ka), respectively. They also exhibit lack of intense dependence of mechanical properties on the holding time. In spite of the analyses and investigations conducted on the proposed ANN, it is still impossible to find out how to adjust the process parameters to reach the highest strength level. In other words, due to the existence of various parameters and their interaction effects in the defined boundary restrictions, the estimation of the optimized combination of the process parameters to achieve the highest strength level of the RSW joints is a demanding task. Thus, it is essential that a capable tool for obtaining the optimized combination of the process parameters be utilized. For this purpose, in this research a Genetic Algorithm (GA) approach has been devised to obtain the optimum combination of the process parameters to reach the highest strength level.

8 766 S.M. Hamidinejad et al. / Materials and Design 34 (2012) Table 9 GA parameters. GA parameters No. of generations for evolution 200 Population size 50 Type of selection Stochastic uniform Probability of cross over 0.95 Table 10 Predicted tensile shear strength of the RSW joint under optimum process parameters and experiment value. EF (kgf) WC (ka) 3.4. Process parameters optimization using combined ANN/GA method Genetic Algorithm is a capable, general-purpose optimization tool which is widely used for solving optimization problems in the mathematics, engineering, etc. In this research, Genetic Algorithm is employed to optimize the RSW process parameters to obtain a set of desired values for tensile shear strength during RSW welding experiments. The aim of the process optimization is to find the optimal control variables in RSW under certain given constraints, in order to obtain the best RSW joint quality. The fitness function used in the optimization procedure is based on the proposed ANN model. In this research, the ANN model has been implemented as the objective function of the optimization problem. The process window for each variable, as given below, was used as the boundary constraints. 10 ka 6 Welding Current (WC) 6 12 ka 195 kgf 6 Electrode Force (EF) kgf 8 cycle 6 Welding Time (WT) 6 12 cycle 8 cycle 6 Holding Time (WT) 6 12 cycle Table 9 lists GA parameters used to optimize the process parameters. The objective functions were the maximum values of the strength of the RSW joints; therefore, the reciprocal of the objective functions were used as the fitness functions. The proposed ANN model presented in Section was optimized by GA code. The neural network prediction of the tensile shear strength of the RSW joint, under the optimized process conditions, was N. This value is higher than all of the train and test samples. This indicates that the optimization procedure performs satisfactory. In order to evaluate the correctitude of the value predicted by the proposed GA, an actual experiment was carried out based on the optimized process parameters. The obtained experimental value was then compared with that predicted by the Genetic Algorithm. The results given in Table 10, show that the modeling approach presented in this study can accurately predict the strength values of RSW joints. Additionally, the developed optimization approach had a desired performance in determining the optimal set of process parameters. 4. Conclusions WT (cycle) HT (cycle) Predicted value by GA Experiment value From the present work the following conclusions can be drawn: (1) The regression analysis reveals that there is a non-linear relationship between the welding parameters and the tensile shear strength of the RSW joints. (2) The proposed ANN, was an effective tool for modeling the complex relationship between the process parameters and the quality index (the tensile shear strength) of the RSW. (3) The effects of welding parameters and their interactions on the tensile shear strength were analyzed on the basis of the ANN model. This can provide a beneficial reference for the RSW process of galvanized interstitial free (IF) steel sheets and galvanized bake hardenable (BH) steel sheets. (4) The combined ANN / GA optimization procedure proposed in this paper provides reasonable results for the optimization of the RSW process. The optimized results, obtained by GA, were successfully verified against the actual experimental data. Acknowledgements The authors would like to express their gratitude to Sapco Company for providing foundations for this research. They would also like to thank Iran Khodro Company (Iran s largest car manufacturer) for providing spot welding machine for this research. The authors would also like to acknowledge Mr. Mohammad Hossein Hasannia, for his help and considerable support in experimental work. References [1] Aslanlar S, Ogur A, Ozsarac U, Ilhan E. Welding time effect on mechanical properties of automotive sheets in electrical resistance spot welding. J Mater Des 2008;29: [2] Marti9n Óscar, López Manuel, Marti9n Fernando. Artificial neural networks for quality control by ultrasonic testing in resistance spot welding. J Mater Process Technol 2007;183: [3] Yi L, Jinhe L, Huibin X, Chengzhi X, Lin L. Regression modeling and process analysis of resistance spot welding on galvanized steel sheet. J Mater Des 2009;30: [4] Kolahan Farhad, Heidari Mehdi. Modeling and optimization of MAG welding for gas pipelines using regression analysis and simulated annealing algorithm. J Sci Ind R 2010;69: [5] Özyürek. An effect of weld current and weld atmosphere on the resistance spot weldability of 304L austenitic stainless steel. J Mater Des 2008;29: [6] Zhou M, Zhang H, Hu SJ. Relationships between quality and attributes of spot welds. Weld J 2003;4:72s 7s. [7] Zhou M, SJ Hu, Zhang H. Critical specimen sizes for tensile shear testing of steel sheets. Weld J 1999;78(9):305s 13s. [8] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. J Neural Network 1989;2: [9] Mathew MD, Kim DW, Ryu WS. A neural network model to predict low cycle fatigue life of nitrogen-alloyed 316L stainless steel. J Mater Sci Eng, A 2008;474: [10] Ates Hakan. Prediction of gas metal arc welding parameters based on artificial neural networks. J Mater Des 2007;28: [11] Fratini Livan, Buffa Gianluca, Palmeri Dina. Using a neural network for predicting the average grain size in friction stir welding processes. J Comput Struct 2009;87: [12] Abdulkadir Cevika M, Kutuk Akif, Erklig Ahmet, Guzelbey Ibrahim H. Neural network modeling of arc spot welding. J Mater Process Technol 2008;202: [13] Martín Óscar, Tiedra PilarDe, López Manuel, San-Juan Manuel, García Cristina, Martín Fernando, et al. Quality prediction of resistance spot welding joints of 304 austenitic stainless steel. J Mater Des 2009;30: [14] Feliu Jr S, Perez-Revenga ML. Effect of alloying elements (Ti, Nb, Mn and P) and the water vapor content in the annealing atmosphere on the surface composition of interstitial free steels at the galvanizing temperature. Appl Surf Sci 2004;229: [15] Gerken JM, Brown RS, DeAntonio DA, Dickinson DW, Foxall RH, Oehler IA, et al. Resistance spot welding. In: Mills K, Davis JR, Sanders BR, editors. Welding. Brazing and soldering, metals handbook, vol. 6. Metals Park, (OH): American Society for Metals; p [16] ISO Specimen dimensions and procedure for shear testing resistance spot, seam and embossed projection welds; [17] Woll SLB, Cooper. Pattern-based closed-loop quality control for the injection molding process. J Polym Eng Sci 1997;37: [18] Bishop. Neural networks for pattern recognition. New York: Oxford University Press; [19] Haykin. Neural networks: a comprehensive foundation. 2nd ed. Upper Saddle River, (NJ): Prentice-Hall; 1999.

9 S.M. Hamidinejad et al. / Materials and Design 34 (2012) [20] Guessasma Coddet. Microstructure of APS alumina titania coatings analysed using artificial neural network. J Acta Mater 2004;52: [21] Goodarzi M, Marashi SPH, Pouranvari M. Dependence of overload performance on weld attributes for resistance spot welded galvanized low carbon steel. J Mater Process Technol 2009;209: [22] Florea RS, Solanki KN, Bammann DJ, Baird JC, Jordon JB, Castanier MP. Resistance spot welding of 6061-T6 aluminum: failure loads and deformation. Mater Des 2011;34: [23] Weiss KE. Paint and coatings: a mature industry in transition. J Prog Polym Sci 1994;22: