Ion GROZAV, Cristian TURC, Felicia BANCIU
THE OPTIMIZATION OF THE SOLDERING PROCESS THROUGH EXPERIMENT DESIGN Keywords: experiment design, optimization, soldering, control factors Abstract The purpose of this paper is to improve the wave soldering process of electronic component, which usually results in a great number of defects, such as insufficient soldering and short-circuits. The wave solder machine has several control factors (parameters), including solder temperature, preheat temperature, conveyor speed, flux type, flux pressure, solder wave depth, and conveyor board angle on conveyor. In addition, however, there are several other factors (noises factors) that cannot be controlled. Designing an experiment with so many control factors implies a big number of the runs. To decrease the number of runs, the authors have identified from previous experience the factors that bear the greatest influence on soldering defects: flux-pressure, conveyor speed, and board angle on conveyor. In the first stage of the experiments, a screening experiment 23 with 3 replicates was designed. After analyzing the data, a control factor was eliminated and in the second stage a central composite design with 2 control factors was designed. Analysis of the experiment data showed that the wave soldering process was optimized by minimizing the soldering defects .
1. LITERATURE REVIEW An industrial innovation comes about as the result of conducting investigations through a sequence of experiments. Research and development is a dynamic process of learning and adaptation [1]. The means by which the objective can be reached is discovered through the investigation process, each subset of experimental runs supplying a basis for deciding what the next experimet would be. Also, the objective itself can change as new knowledge is brought to light. In an investigation sequence of experiments, different experimenters can take different routs and begin from different starting point, however, they always arrive at similar solutions and scientific iterations tend to be self-correcting [5], [6]. Experiments are performed in many fields, usually to discover something about a particular process or system. An experiment is a test or series of tests in which
Ion GROZAV, Cristian TURC, Felicia BANCIU
purposeful changes are made to the input variables so that the reasons for the changes observed in output response can be observed and identified [6], [7]. In engineering, experimentation plays an important role, especial in new product design, manufacturing process development and process improvement. The objective in many cases may be to develop a robust process [2], that is, a process affected minimally by external sources of variability. In any experiment, the results and conclusions that can be draw on the manner in which the data were collect. That is the reaFig.1. Model of the process/product son for a very careful design of the experiments. The experiments are use to study the performance of processes and systems. The process or system can be representing like in figure 1. The process is a combination of the machines, methods, people and other resources that transform the input (often material, energy and information) into an output y that can be one or more observable responses. The process can be manipulated using process variables x1, x2, …,xp, called controllable factors, whereas other variable z1, z2, …,zq, are uncontrollable factors (although they may be controllable for purposes of the test) [4], [5], [6]. The objectives of the experiment can be: − Determining which variable are the most influential on the response y; − Finding where to set the controllable factors xi so that the response y is always near the desired value; − Establishing the setting values for x’s so that variability in the response y is small; − Determining where to set the controllable factors x so that the effect of the uncontrollable factors z are minimal.
2. CHARACTERIZATION AND OPTIMIZATION OF THE WAVE SOLDERING PROCESS 2.1. Characterization the process using first order model An example of application of factorial design is give in the case of wave soldering machine. This machine is use in the manufacturing process for printed circuits board. The board is carry with a conveyor through a wave of smelt soldering
Optimization of Soldering Process Using Design of Experiments
and the electronically components are join on the printed board. The clamping device for a panel with 10 boards on it can be seeing in figure 2. This clamping device can carry the panel in 2 position given by angle zero or 150. The angle is one of the controllable factors. The control factors for wave soldering process are preheat temperature, conveyor speed, conveyor angle, board angle on conveyor, flux Fig. 2. Clamping Device type, flux specific gravity, solder temperature, solder wave depth, etc. A great number of controllable will give a great number of the runs in experiment and this consumes a lot of resource. Using the experience for this process has been take only three controllable factors: A-conveyor speed, B-flux pressure and C-angle of the panel with the direction of the conveyor. Responses for this soldering process are insufficient soldering and the shortcircuit. There are several others uncontrollable factors that can act during the soldering process. They are: types of components on the board, layout of the components on Pareto Chart of the Standardized Effects
Pareto Chart of the Standardized Effects
(response is Short, Alpha = .10)
(response is Ins_sold, Alpha = .10)
1.746
1.746 F actor A B C
B C
Name conv _speed flux_pres angle
B A Term
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AC 0.5
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Fig. 3. Pareto Chart for Insufficient Soldering and Short-Circuit
Main Effects Plot (data means) for Ins_sold conv_speed
Main Effects Plot (data means) for Short
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Fig. 4. The Factors Main Effect
-1
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Name conv _speed flux_pres angle
Ion GROZAV, Cristian TURC, Felicia BANCIU
the board, operator, thickness of the printed circuit board, production rate, etc. All these factors will affect the responses of the process. In order to find the influence grad of the controllable factors, first was made a factorial experiment 23 with 3 replicates. The design of this experiment has bee made using a statistical software program MINITAB. The Pareto diagram with the influence rank of the factors is show in figure 3. Pareto chart show that for insufficient soldering the order of the influence is factor B and C with significance influence, and for short –circuit the order is C, B and A factor. The main effects of the controllable factors are present in figure 4. Interaction Plot (data means) for Ins_sold -1
1
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Interaction Plot (data means) for Short
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Fig. 5. The Interactions of the Control Factors
The angle magnitude of the main effect gives information about how greater is the effect of the factor. The effect can be positive, like of the conveyor speed, or negative, like of the flux pressure and the angle. The interactions for both cases of the insufficient soldering and short-circuit are present in figure 5. In these graphs can be seeing that some line are not perfect Surface Plots of Ins_sold
Contour Plots of Ins_sold Hold Values
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Fig. 6. Surface and Contour Plots
parallel. That implies some interactions among factors, but the Pareto chart show that these interactions are not significance. The statistical software gives a mathematical model of the soldering process. This model can be use to plot the surface or contour for response of the process function of the two controllable factors. Figure 6 show all possible graphic representation of these surfaces or contours. Usually factorial design is the case of
Optimization of Soldering Process Using Design of Experiments
screening design, but in some cases are possible some non-linearity. Using this graphic representation can observe the Overlaid Contour Plot of Ins_sold, Short region in which can results the desired responses. Similar can be get the graphical representations for the case of short-circuit. For the experimenter, in this stage of experimentation, is important to find the region with minimum number of insufficient soldering and short-circuits. The statistical software gives the possibility to overlay the surface contours for the Fig.7. Overlaid of the Contour Plot case of insufficient soldering and shortcircuit. The goal is to find the setting of principal controllable factors for minimization of the responses. In figure 7 is present this overlay. In this figure can be see that for minimizing the insufficient soldering and shorts is necessary to set minimum values for conveyor speed and maximum values for flux pressure. Other possibility to find the desired Fig. 8. Optimal Values for Factor Setting values for all controllable factors is by optimization of the process, using the mathematical model given by the statistical software. The result of optimization is shows in figure 8. The desire values of the responses need to set conveyor speed to the minimum value in selected region and the maximum value for flux pressure and the angle. In the same figure can be seeing that the model of the process is lineal. In practice majority of the processes are non-linear and may be the optimum values given for the previous optimization are not so good. Using this first experimental design was possible to characterize the process by selecting the most important controllable factors and to establish the region in which will be obtain some improving of the process responses. 1.0
Ins_sold 0 1 Short 0 1
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2.2. Optimizing the process using second order model For a better improving of the responses needs a second non-linear design experiment. In these cases are use so called Response Surface Methodology (RSM) [2], [4], [6], [7]. This method is a collection of mathematical and statistical techniques that are useful for modeling and analyzing the cases in which responses are
Ion GROZAV, Cristian TURC, Felicia BANCIU
influence by several variables in non-linear manner and the objective is to optimize
Fig. 9. A Response Surface and Contour Plot
Fig. 10. The Sequential RSM
the response. The objective is to lead the process rapid and efficiently along the path of imSurface Plot of Ins_sold vs flux press., conv speed
Surface Plot of Short vs flux press., conv speed
6
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Fig.11. Surface Plot
provement near the vicinity of the optimum. When the region of the optimum is found with a more elaborated model, a non-linear one such a second order model, an analyses of the response surface can be done as a “climbing a hill”, the top of the hill represent the maximum response ( see figure 9 and 10) . If the objective is to minimum the response then the direction of moving is “descending into a valContour Plot of Ins_sold vs flux press., conv speed
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1.2 2.4 3.6 >
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Short < -0.05 -0.05 0.15 0.15 0.35 0.35 0.55 > 0.55
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flux press.
0.5 flux press.
Contour Plot of Short vs flux press., conv speed Ins_sold < 0.0 0.0 - 1.2
-1.0
-0.5
0.0 0.5 conv speed
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Fig.12. The Contour Plot for Second Order Model
Optimization of Soldering Process Using Design of Experiments
ley”. In the case of wave soldering process, using factorial design, in the first stage, has been find a region where result quite desired responses for insufficient soldering and short-circuit. The first optimum values were present in figure 8. For a better optimization in the second stage of the experiment was using a central composite design [6]. The surfaces plot for insufficient soldering and short are present in figure 11 and the contour plot in the same cases are present in figure 12. Figures 11 and 12 are showing that in the region of the optimum value the soldering process is non-linear. Indeed the second order experiment was necessary to optimize the process. Fig. 13. The Overlaid Contour Plot By overlay the contour plot in the for Two Responses case of insufficient soldering and for short-circuits is possible to find the region where there are minimum defects. This overlay of these contour plots is present in figure 13. In the figure the white region, give the setting value for control factors that assure the minimum defects on the board in the wave soldering process. There are two regions with minimum defects. One is locate in the area with maximum values of control factors and the other with minimum Fig.14. Optimal Values of setting values for controllable factors. the Factors. The statistical software helps us to optimize the process in the case of multiple responses. In figure 14 the optimal values for conveyor speed and flux pressure are presented in code units. So can protect the company where the experiments have been making. Because all desirability function di =1 means that process present an global optimal. The overall desirability is D = 1, that show us again that the soldering process can be best optimized. Overlaid Contour Plot of Ins_sold, Short
Ins_sold -0.1 0.1
1.0
Short -0.1 0.1
flux press.
0.5
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3. CONCLUSIONS In practice is important to find the most sensitive control factors that have a great influence upon responses (quality indicators) of the process / product. These
Ion GROZAV, Cristian TURC, Felicia BANCIU
control factors are using for control the process behavior and for a economic optimization of the process / product. In this paper was presented a case of wave soldering process that was characterized in the first stage using factorial design (lineal modeling) and in the second stage the process was optimized using a central composite design. This research was made in the framework of international program FP6, VRLKCiP lab UPT from POLITEHNICA University of Timisoara. REFERENCES [1] Box G.E.P.; Liu P.Y.T., Statistics as a Catalyst to Learning by Scientific Method, Part I, Journal of Quality Technology, vol.1, No. 1, January 1999, p. 1-15. [2] Fowlkes, W.Y., Creveling, C.M., Engineering Methods for Robust Design, Using Taguchi Methods in Technology and Product Development, ISBN 0-201-63367-1, Addison-Wesley, 1995. [3] Grama, L., Programarea experimentelor in constructia de masini (Design of Experiments in Machine Building), Ed. VERITAS, Tg. Mures, 2000. [4] Grozav, I., Optimization of the Ultrasonic Welding Parameters Using Design of Experiments, Annals of MTeM for 2003&Proceedings of the 6th International Conference” Modern Technologies in Manufacturing”, 2-4 Oct., Cluj-Napoca, 2003, p 211215. [5] Miron, M., Grozav, I., Todea, C., Notiuni fundamentale de Metodologia cercetarii stiintifice medicale (Basic knowledge for research methodology in medical science), ISBN 973-631-189-9, Ed. Marineasa, Timisoara 2004. [6] Montgomery, D.C., Design and Analyses of Experiment 5th ed., ISBN 0-471-31694, John Wily & Son Inc., New York, 2001. [7] Vardeman, S.B., Statistics for Engineering Problem Solving, ISBN 0-534-92871-4, PWS Publishing Company, Boston, 1994.
Authors: Assoc.Prof. dr ing. Ion GROZAV , POLITEHNICA University of TIMISOARA, Manufacturing Engineering Department. 300222 TIMISOARA-Romania,Bd.M. Viteazul No. 1. e-mail:
[email protected], tel.: +40726416635 Lecturer. dr ing. Cristian TURC POLITEHNICA University of TIMISOARA, Manufacturing Engineering Department. 300222 TIMISOARA-Romania,Bd.M. Viteazul No. 1. e-mail:
[email protected], tel.: +40728043170 Assist. ing. Felicia BANCIU POLITEHNICA University of TIMISOARA, Manufacturing Engineering Department. 300222 TIMISOARA-Romania,Bd.M. Viteazul No. 1. e-mail:
[email protected], tel.: +40723152935
