GUI Based Mamdani Fuzzy Inference System Modeling

International Journal of Electrical & Computer Sciences IJECS Vol: 9 No: 9 - 281 -

1957091 IJECS-IJENS @ International Journals of Engineering and Sciences IJENS

GUI Based Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in Laser Machining

Sivarao, Peter Brevern, N.S.M. El-Tayeb, V.C. Vengkatesh

Abstract— The world of manufacturing has shifted its level to the era of space age machining. The purpose of this investigation is to develop Fuzzy based Graphical User Interface (GUI) for modeling of laser machining conditions. The developed fuzzy based GUI is expected to overcome the major problems faced by most of the manufacturing industries nowadays with the increased number controllable parameters and the lack of expertise to operate the machine. Investigations were carried out by screening for the significant parameters before the explicit GUI is designed. Next, the GUI for Fuzzy based modeling has been developed using GUIDE and Fuzzy Toolbox in MATLAB. The fuzzy variables were also analyzed before finalizing the significant of its variables. The developed GUI has been programmed to interact with fuzzy variables in order to model the laser processing cut quality of two different thicknesses, 2.5 and 5.0 mm. The models were then compared for their statistical validation by Root Mean Square Error (RMSE). Few models with best and optimized variables were taken as prediction models, where their respective outputs were analyzed and compared based on percentage error for 128 data sets to validate the models. The best developed model was then recommended to the pressure vessel manufacturing industry to further reduce the production cost and improve cut quality of its end product. Index Terms— Mamdani Fuzzy modeling, Laser Cutting, laser cut quality evaluation, GUI based modeling.

I. INTRODUCTION

The recent trend of manufacturing industries in achieving larger quantities with good quality product is embarked by employing non traditional machine tools in order to obtain tight tolerances and accurate dimensions in shortest time possible to make their products timely in the market. One of the ways to achieve these instant manufacturing practices is by simulating the processes to its actual conditions before they are put onto the actual production floor. High number of simulation tools are being employed for this reasons as the method is seen to be more reliable as compared to the traditional trial and error methods.

Ir. Sivarao is a Professional Engineer (P.Eng.) in the field of

Mechanical Engineering who currently serves as a lecturer and researcher in the Faculty of Manufacturing engineering, Universiti Teknikal Malaysia Melaka (UTeM). He is the corresponding author. (phone: 6063316505, Fax: 6063316411 & email: [email protected] or [email protected]).

Dr. Peter Brevern, Dr. N.S.M. El-Tayeb and Prof. V.C. Vengkatesh are the expatriates from Germany, Iran and India respectively. They are attached to Faculty of Eng. and Tech. (FET),

Multimedia University, Malaysia (email: [email protected], [email protected], [email protected]).

There are several Fuzzy based model that has been developed to determine machining parameters and responses. Wong and Hamouda [1] developed Fuzzy Expert System for machinability data sets on the web, where the results produce by the system was compared with the data from machining handbook. The error was about 0.25 to 2.41 percent. Suleyman et al. compared experimental results with the consistent fuzzy rule based model estimated values for cutting forces in turning operation [2]. In this experiment, three inputs; cutting speed, feed rate and depth of cut were used to investigate the response. The model with 27 rules has obtained the prediction accuracy up to 99.6 percent. Tansel in year 2006 employed fuzzy logic controller for auto detection of chatter in turning operation using S-transformation technique [3]. Fuzzy logic approach was also used for optimizing the machining parameters of an injection molding to produce thin shells which were then applied as mobile phone casings [4]. Fuzzy model was successfully used to select the best silicon crystal slicing techniques by Doraid and Omar [5]. The application of Fuzzy Logic and modeling techniques are not only limited for machining processes, parameter selection and control, but the advancements have taken place beyond expectation such as in the field of environment management where a method to capture the view of multiple stakeholders is developed using fuzzy set theory and Fuzzy Logic [6]. This method was successfully applied for flood management of Red River Basin, Minatoba, Canada. Fuzzy model was also successfully used in selecting the best logs using porosity and permeability data sets at Korea offshore [7]. Fuzzy logic hand writing recognizing system has been developed and used widely [8]. Diesel spray penetration using Fuzzy Logic modeling has been carried out by [9], where the model has gain the accuracy reaching almost 98 percent with the experimental data sets.

Laser cutting machine is very well known for its non-linear characteristics and the difficulties in controlling the process parameters. The influences of the machining parameters on machine are not always precisely known and hence, it becomes difficult to recommend the optimum machinability data for machining process. Therefore, a Fuzzy based model has been developed by referring to Seven significant designed parameters namely; cutting speed, duty cycle, frequency,



power, focal distance, gas pressure and stand of distance. The desired response is surface roughness which considered as most critical elements in sustaining the laser cut quality and dimensional accuracy.

II. FUZZY LOGIC

Fuzzy logic (FL) is a common element of Expert System with an increasing rate of interest and widely used over the past few years due to its successful applications in many control and prediction systems. It is widely used due to its ability in representing the vagueness and imprecise information. It suits very well in defining the relationship between inputs and desired outputs of a system, where its extra ordinary controlling and reasoning capability made its way to the application of many complex industrial systems since can be precisely modeled under various assumptions and approximations. Fuzzy system consists few inputs, output(s), set of predefined rules and a defuzzification method with respect to the selected fuzzy inference system.

A. Mamdani Fuzzy Inference System (FIS) Mamdani FIS is the most known or used in developing fuzzy models. The output of the system is generally defuzzified resulting fuzzy sets are combined using aggregation operator from the consequent of each rule of the input. A single if-then rule is written as; IF “X” is A, THEN “Y” is B

or in a mathematical form;

1{ ( ) ( ) } Ni i iI F p r e m i s e T H E N c o n s e q u e n t =

Where, A and B are linguistic values defined by fuzzy sets on the ranges; X and Y, respectively. The if-part of the rule “x is A” is called the antecedent or premise, while the then-part of the rule “Y is B” is called the consequent or conclusion. Fig. 1 shows the defuzzification method applied onto a fuzzy model based on three different conditions.

Fig. 1. Defuzzification using Mamdani FIS

Depending on the system, it may not be necessary to evaluate every possible input combination since some

may rarely or never occur. By making this type of evaluation which is usually done by an experienced operator, fewer rules can be evaluated, thus simplifying the processing logic and perhaps even improving the fuzzy logic system performance [10].

In this project, the input membership function was divided into two linguistic values, where each input denoted as low and high respectively. The determination of the membership function is done by using the help of ANFIS Toolbox in MATLAB. This technique enabled excellent model development for non-linear process in which the rules were automatically generated under ANFIS environment.

Fig. 2: Input MF for fuzzy model

The membership function and set of rules were fed into the system in determining the response. Each rule in the system is considered very important and critical to generate the predictions in numeric form. The snapshot of the membership function plot and rules fed into the system are shown in Fig. 2 and Fig. 3 respectively. Knowing the non-linear behavior of laser processing, therefore 128 rules were set to ensure the gaining desired outputs are reliable and satisfactory.

Fig. 3: Part of the 128 rules fed into Fuzzy System

III. Laser Machining

Laser cutting is used in precision industries as it has the ability to cut complex profiles featuring extra ordinary shapes, corners, slots, and holes with high degree of repeatability and small region of heat affected zone (HAZ). In laser machining, surface roughness is one of the most important quality evaluation factors. The surface roughness is generally dependent upon the properties of the work material being cut, workpiece thickness, focal



length, stand of distance, gas pressure, cutting speed, etc. including the type of cutting gas. Besides the investigation of CO2 laser cutting parameters investigations are also being studied to further understand the relationship between the gas and the cutting parameters to obtain a good cutting quality. The laser cutting machine used for the experiment is LVD Helius Hybrid 2514 CO2. The gas used for the laser machining in precise is a mixture of N2 (55%), He (40%) & CO2 (5%) with purity (99.995%). The need to precisely control a large number of parameters, often with random components makes the task of improving the process performance very difficult. The corresponding parameters which are (corresponding to power, speed, pressure, focal distance, standoff distance, frequency, duty cycle) need to be tested by numbers of experiments before able to achieve the desired surface roughness or kerf width which waste cost and time

IV. MACHINING CONDITIONS AND WORK MATERIAL

The workpiece materials, design parameters, laser machine type and its capability together with the entire equipments / apparatus used in this research activity are listed on coming pages. The standards used in data collection and interpretation also stated.

Table 1. Parameters and levels

Level

Controllable Parameters Low High

Stand of Distance (mm) 1 1.5 Focal Distance (mm) 0 0.5 Gas Pressure 5 8 Power (Watt) 1600 2300 Cutting Speed 1800 2500 Frequency (Hz) 1500 1800 Duty Cycle 75 85 A. Work Material • DIN 17155 HII standard • 5mm Manganese-Molybdenum • Grade: B • Tensile Strength: 550-690 MPa B. Laser machine • Model: Helius Hybrid 2514 CO2 Laser Cutting

Machine • Controller: FANUC Series 160 i-L • Maximum capacity: 4 kW • Laser source that use to create laser beam is CO2 gas.

The real ingredient is mixture of N2 (55%), He (40%) & CO2 (5%) with purity 99.995%.

• Pressure = Max 3 bar

V. METHODOLOGY

The methodology practiced in carrying out the entire research project is represented in the form of a flow chart as illustrated in Fig. 4.

VI. GUI DEVELOPMENT FOR MODELING

The GUI is created by using GUIDE (Graphic User Interface Developing Environment) in MATLAB. The GUI will be divided into two main interface; main GUI and sub-GUI. The main GUI will let the user select the output that they wanted to predict and the sub-GUI will predict the desired values once the input parameters are keyed-in into the respective data boxes accordingly. The main GUI is shown in Fig.5 and the Sub-GUI is shown in Fig.6



Fig. 4: Methodology Flow chart

Fig. 5: Main GUI

The purpose of the developed GUI is to ease the programming part during modeling. Therefore, the GUI is represented with basic graphics such as push buttons. Users are only required to handle graphic objects while the program automates the work as desired. The following GUI is the completed version which is created via MATLAB’s GUIDE. This GUI is capable of predicting surface roughness and kerf width of the material which thickness 2.5mm and 5mm. In this paper, the surface roughness prediction of 2.5mm is elaborated.

Fig. 6: Sub GUI

VII. RESULT AND DISCUSSION

The main GUI with sub-GUIs have been successfully established and used in modeling of laser processing. It



enables the user to select any of the fuzzy inference system file (‘fis’) or manually select their own created ‘.fis’ file to integrated with GUI. This provides the freedom of modeling in GUI environment and multiple working conditions. Once the ‘fis’ file is selected, the user could analyze the error of the selected system. This would provide user friendly conditions to the user in which they will know directly if their selected combinations of the fuzzy variables are the best by referring to the automatically generated RMSE by the system.

Fig. 7: Selection of ‘.fis’ file – from main GUI The main aim of the developed GUI is to model laser processing phenomenon and predict the cut quality of end product, namely surface roughness. Therefore, seven significant laser machining variables; power, speed, pressure, focal distance, stand of distance, frequency and duty cycle were used for fuzzy predictive modeling with the best combination of fuzzy variables to predict the surface roughness, Ra. The best fuzzy model was selected based on the analysis of RMSE values which were obtained via various combinations of fuzzy variables. Each combination setting differs depending on type of membership functions and defuzzification methods. From the analysis, it was found that the best fuzzy variable combination is with triangular membership function and bisector defuzzification method with 1.706 as the RMSE value. A. Fuzzy Modeling and Prediction

In order to select the best model among the tested fuzzy variable combinations, the lowest RMSE value was taken as a benchmark. Firstly, the Mamdani was selected as the fuzy inference system (fis). Secondly, three membership functions were selected to be combined, they are generalized bell (gbellmf), triangular (trimf) and Gaussian (gausmf). Thirdly, the defuzzification methods were selected to be combined. They are centroid, bisector, Mean of Maximum (mom), Largest of Maximum (lom) and Smallest of Maximum (SoM). The data sets were then matricide to analyze RMSE values.

RMSE, Ei of an individual program i is evaluated using equation (1).

(1)

Where P(ij) is the value predicted by the individual program i for sample case j (out of n sample cases); and Tj is the target value for sample case j. For a perfect fit, P(ij) = Tj and Ei = 0. So, the Ei index ranges from 0 to infinity, with 0 corresponding to the ideal. The RMSE values generated by the models were then summarized accordingly as shown in Table 2.

Table 2: Summary of RMSE for various combined fuzzy models

The Triangular membership function as shown in Fig. 8 has demonstrate to be the best membership function for the input because of its simplicity and precision in determining the value of the input parameters.

Fig. 8: Triangular membership function for each tested machining variable

The model variables were then plot in histograms to provide better visualization of defuzzification method characterization according to their respective type of membership functions. The RMSE values gained based



on the combinations are shown in Fig. 9, where it clearly shows that the bisector defuzzification method with triangular membership function provides the lowest RMSE and therefore, the model was later validated by conducting prediction accuracy test using percentage error, where, it is used to check the prediction strength.

Fig. 9: RMSE for each combined fuzzy models

The percentage error of each prediction is used to determine the effectiveness of the Fuzzy model and it was estimated at least 90 percent of the tested data sets to achieve at least to the accuracy of not less then 80 percent. The percentage error was calculated based on equation (2).

(2)

The observed and expected signatures are shown by Fig. 10 which clearly indicates the good correlation of prediction result as compared to the observed values. Over 128 data sets tested, the trend shows that the tabulation is statistically balanced.

Fig. 10. The observed and prediction plot Detailed values of the predictions can be seen in Fig. 11 in which, the values were found to fall at both side of the trend-line proving balanced and excellent fuzzy predictive model has been established.

Fig. 11. Histogram of observed vs. prediction The surface models with two significant parameters showing two way interactions and relationship towards the desired response, surface roughness is shown by Fig. 12 (a) the interaction of gas pressure and stand of distance, (b) stand of distance and cutting speed, (c) stand of distance and focal distance, (d) gas pressure and frequency. The proportional and non-proportional relationship of the machining variables towards the desired response are clearly viseble in these figures to study the main and interaction effects in achieving high cut quality of end product.

Fig. 12. (a) Interaction of gas pressure and stand of distance



Fig. 12. (b) interaction of cutting speed and stand of distance

Fig. 12. (c) Interaction of focal distance and stand of distance

Fig. 12. (d) Interaction of gas pressure and frequency. The fuzzy rule viewer of the established model is shown in Figure 13. It indicates the behavirol of the response over the change in values of all the seven significant laser machining parameters.

Fig. 13: Rules viewer of developed fuzzy model over its response / defuzzified values

VIII. CONCLUSSIONS

Graphical User Interface (GUI) for fuzzy modeling, specially by Mamdani inference syatem has been successfully developed as expected to predict the response by optimizing the fuzzy variables on Matlab environment. The RMSE values for all the developed models were analyzed and optimized to obtain the lowest which indicates the model strength. All of the models created have shown that error is unavoidable in developing model as they are developed based on intuition and ANFIS help to determine the membership function. The error for the model was quite high especially for the midst and end of experiments. Experiment number 80 produces the highest error which is 72 percent and the least was witnessed by experiment number 42 with one percent error. As a conclusion, the fuzzy model has shown that the ability to predict output from given input of the laser cutting machine is achieved but not to the expectation level. This could be due to the very non-linearity behavior of laser processing which Mamdani fuzzy inference system couldn’t take it. Anyway, the idea to carry out fuzzy modeling by GUI for researchers who are phobia of programming and Matlab is achieved. The prediction accuracy is expected to be improved by using Takagi Sugeno Fuzzy Inference System, where TSFIS can be adopted and configured better to a non-linear process. For future work, it is expected that the researchers will make a comparison study of Mamdani and Sugeno fuzzy inference system to see which model could produce better prediction accuracy of laser machining process. This modied Fuzzy modeling is known as ANFIS adopted ruled based where, the limitation of traditional crisp fuzzy modeling is overcomed to suit non-linear laser processing.

ACKNOWLEDGEMENT



The authors are obligated to sincerely thank the top level management of PT Puspetindo Engineering - Manufacturing of Plant Equipments (Surabaya, Indonesia) and Kara Power Pte. Ltd. The authors would also like to thank Mr. Khor Wong Ghee, Engr.Vijayan & Engr.Segaran for their sincere guide, advice and expert knowledge sharing in making this research possible. Special thanks goes to Facuty of Manufacturing Engineering, Universiti Teknikal Malysia Melaka for their support.

REFERENCES

[1] Wong, SV. and Hamouda, A.M.S., “A Fuzzy Logic Based Expert System for Machinability Data-On-Demand on the Internet”, Journal of Materials Processing Technology, vol. 124, pp. 57-66, 2002.

[2] Suleyman, Y. Faruk, U. and Haci, S., “Comparisons of Experimental Results Obtained by Design Dynometer to Fuzzy Model for Predicting Cutting Forces in Turning”, Material and Design, vol. 27, pp. 1139-1147, 2006.

[3] Tansel, IN. Wang, X. Chen, P. Yenilmez, A. and Ozcelik, B,

“Transformation in Machining. Part 2: Evaluation of Machining and Detection of Turning by using S-transformation”, International Journal of Machine Tool and Manufacture, vol. 46, pp. 43-50, 2006.

[4] Chiang, KT. and Chang FP, “Application of Grey Fuzzy Logic on the Optimal Process Design of an Injection Molded Part with Thin Shell Feature”, International Communication of Heat and Mass Transfer, vol. 33, pp. 94-101, 2006.

[5] Doraid, D. and Omar, B., “A Fuzzy Logic Approach to the Selection of the Best Silicon Crystal Slicing Technology”, Expert Systems with Applications, vol. 176, pp. 641-648, 2008.

[6] Akter, T. and Simonovic, S.P., “Aggregation of Fuzzy Views of a Large Number of Stake Holders for Multi-objective Flood Management Decision Making”, Journal of Environmental Management, vol. 77, pp. 133-143, 2005.

[7] Lim, J.S., “Reservoir Properties Determination Using Fuzzy Logic and Neural Networks from Well Data in Offshore Korea”, Journal of Petroleum Science & Engineering, vol. 49, pp. 182-192, 2005.

[8] Hany, F., “Handwriting Digit Reorganization with Fuzzy Logic”, Jurnal Teknik Elektro, Indonesia, vol. 5, pp. 84-87, 2003.

[9] Howlett, R.J. Lee, S.H. Crua C. and Walters S.D., “Fuzzy Logic and Neuro-fuzzy Modelling of Diesel Spray Penetration : A Comparative Study”, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, vol. 18, 2007.

[10] Fuzzy Logic Toolbox™ User’s Guide, 13th Edition, The MathWorks, Inc. Natick, US, 2008.

BIOGRAPHIES

Ir. Sivarao is a Professional Engineer (P.Eng.) in the field of mechanical engineering and currently he serves Universiti Teknikal Malaysia Melaka as a researcher in the field of manufacturing engineering, specializing in precision machining and artificial intelligence. He has published his findings in more than 60 reputated international journals and conference proceedings. He is also an active reviewer for Journal of Engineering Manufacturer (UK), Journal of Mechanical Engineering Science (UK), IJMPT (special issue) and JEEER together with few International Conferences. To date he has been awarded few research grants totaling up to 200K and he has a product patented and commercialized. He also won eight medals in various innovative product design competitions including the one held in Geneva in year 2007. He is also an active member of few professional associations including Board of Engineers Malaysia,

Academy of Malaysian SMEs, The Institute of Engineers Malaysia, Malaysian Invention and Design Society and International Association of Engineers, UK. Peter Brevern is a PhD holder, an expatriate from Germany who is currently the Dean, Faculty of Engineering and Technology (FET), Multimedia University, Malaysia., He has earned his Bachelor, Masters Degree and PhD in the field of Mechanical Engineering from Germany. He has specialized in cutting technologies and consulted few manufacturing industries in Malaysia as well as in abroad. He has published an adequate number of journals and proceedings. N.S.M. El-Tayeb is a PhD holder, an expatriate from Iran, currently serves as a senior lecturer at Faculty of Engineering and Technology (FET), Multimedia University, Malaysia. He has earned his Bachelor Degree from Iran and his Masters and PhD from United Kingdom. He is a specialist in the area of tribology and he has published few high impact journals and proceedings and he has also chaired few international conferences. V.C.Vengkatesh is a Professor of machining who is also an expatriate from India. He is currently attached to Faculty of Engineering and Technology, Multimedia University, Malaysia. He has published hundreds of journal papers and almost 700 conference papers. He is very well recognized in the area of precision machining in United States where he collaborates most of his research work with many Universities and experts in US. He also holds few product patents and won few innovative awards. His knowledge and ability is incomparable and he has attributed vast contribution to the world of manufacturing.

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GUI Based Mamdani Fuzzy Inference System Modeling