Data mining for knowledge discovery and quality improvement of tmt bar
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Data mining for knowledge discovery and quality improvement of tmt bar Data mining for knowledge discovery and quality improvement of tmt bar Document Transcript

  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 281 DATA MINING FOR KNOWLEDGE DISCOVERY AND QUALITY IMPROVEMENT OF TMT BAR RE-ROLLING PROCESS Prof.E.V.Ramana1 and Dr. P. Ravinder Reddy2 1 Professor & Head, Department of Mechanical Engineering, Shadan College of Engineering & Technology, Peerancheru, Hyderabad, India 2 Professor & Head, Department of Mechanical Engineering, Chaitanya Bharati Institute of Technology, Hyderabad, India ABSTRACT Data mining tools makes it possible to automatically discover interesting and useful patterns in various manufacturing processes. These patterns can be exploited to enhance the process performance to achieve the desired levels of quality. This paper focuses on data mining techniques to extract the quality related knowledge from re-rolling process used for manufacturing of TMT (Thermo Mechanically Treated) bars. MS Naïve Bayes, Association, Neural Networks and Logistic Regression algorithms available in SQL Server 2008 have been applied to build data mining models on the process data to find the relation between various grades of TMT bar(target attribute) and input attributes(manufacturing process data). This quality related process knowledge shall be used in optimum setting of process parameters to achieve the desired level of quality (grade) and quality prediction for a particular setting of input process attributes. Keywords: Association rules, Data Mining, Knowledge discovery, Naïve Bayes, Neural Networks 1. INTRODUCTION Data mining is a collection of tools that explore data in order to discover previously unknown patterns. [1] It helps to detect hidden patterns, trends, associations, dependencies, and causal relationships from the data stored in manufacturing databases to acquire the knowledge. Data mining has been applied successfully in manufacturing applications such as INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 3, May - June (2013), pp. 281-288 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 282 quality control, preventive maintenance, fault detection, yield improvement, process optimization etc. [2][3] An analysis of the usage of DM (Data Mining) applications for the quality task indicates that predicting quality is the most frequently performed task. Applications involving classification of quality and parameter optimization are in considerable number. [4] TMT (Thermo Mechanically Treated) bars are used in high rise buildings, flyovers, bridges etc. They are re-rolled from billets and need to be strong in tension and at the same time ductile enough to be shaped or bent. Thermo mechanical treatment result in a material structure with a soft ferrite-pearlite core and tempered martensite surface layer which is very hard. It provides an optimum combination of strength, ductility, weld- ability, bend- ability, higher elongation and other desirable properties to the material. TMT re-bars are available in different grades such as Fe415, Fe500, Fe550, Fe600 etc. In the present study, Microsoft (MS) Naïve Bayes, Neural Network, Logistic regression and association models have been built on the process data collected from a re-rolling mill involved in manufacturing of Fe500 and Fe550 grade TMT bars to extract the quality related knowledge. The acquired knowledge from these models shall be applied to identify critical process parameters that have impact in obtaining desirable grades of TMT bars. It enables quality prediction in terms of grade for the given process parameters and helps in optimal setting of parameters to achieve desired level of quality. 2. ROLLING OF TMT BARS A standard re- rolling mill involved in manufacturing of Fe500 and Fe550 grade TMT bars of 8 mm cross section has been selected for study. The reheated billet from the furnace will be passed through roughing stand where maximum reduction of cross sectional area takes place. It is rolled further through intermediate and finishing stands to obtain final dimensions and shape of bar. The bars from finishing stand are cut to size by shear cutter and quenched in quenching box to the appropriate temperature by pressurized water jets. The surface of bar is instantly cooled to martensite while the core is austenitic. Further cooling of bars on cooling bed causes austenitic core to get converted into ferrite-pearlite core. 2.1 Data Set For this study, process data has been collected at various stages of the rolling process starting from in coming raw material to cooling of bars on cooling bed through sensors, material testing equipment and log books. Samples (32 records) are collected from manufacturing process of TMT bar (8 mm cross section) from different lots. The process attributes that are considered in rolling for analysis are presented in Table 1. Table 1 Rolling Process Attributes Sample Number Percentage of carbon Percentage of manganese Percentage of Phosphorous Percentage of Sulphur Temperature of Billet (o C) Temperature of bar before quenching (o C) Temperature of bar after quenching (o C) Pressure of water for quenching (kgf/cm2 ) Temperature of water for quenching (o C ) Temperature of furnace(o C) weight per meter length(kg/m)
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 283 3. NAIVE BAYES CLASSIFICATION Naïve Bayes is one of the simplest classifiers and performs better in many applications. It predicts well when input attributes are relatively independent. It is important to verify the accuracy of this model with hold out data using lift chart. [5] Mining model based on Microsoft (MS) Naïve Bayes algorithm has been developed by making use of process attributes as given in Table 1. This model identified billet temperature, temperature of bar before and after quenching as key input attributes that have impact on target attribute i.e. grade of TMT bar in the current study. Naïve Bayes dependency network view displayed in Fig.1shows the key attributes that are having strong impact in predicting the grade of bar Fig.2 shows discrimination scores for Fe500 and Fe550 grades. Attribute-value pairs favoring each grade are presented to discriminate both the grades. MS Naïve Bayes algorithm parameters are set to the values given below. Maximum input attributes (default: 255), Maximum output attributes (default: 255), Maximum states (default: 100, Minimum-Dependency-Probability (0.4, specifies minimum dependency probability between input and output attributes) Fig.1 Dependency network view Fig.2 Discrimination scores for Fe500 and Fe 550
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 284 4. ASSOCIATION RULE MINING (ARM) Association Rule Mining (ARM) can be defined as the discovery of association rules showing attribute-value conditions that occur frequently together in a given set of data. [6] A predicate that participates in a rule is called an item. A set of such items is called an item set. A rule can be described as a pair containing a left hand itemset (condition) and a right hand itemset (conclusion). [5] Mining frequent patterns leads to discovery of interesting associations and correlations with in data. [7] Associative rule mining has been implemented in this paper by applying MS Association algorithm on process data set. Association rules that are qualified based on minimum probability, support and importance scores criteria have been displayed below for Fe500 and Fe550 grades of TMT bar. An association with high confidence and support is considered to be strong and may be potentially useful. Importance of rule is calculated by the log likelihood of the right-hand side of rule, given the left-hand side of rule. Higher importance score implies that quality of rule is better. 4.1 Association Rules for Fe500 4.1.1 Temperature of Bar after Quenching >= 602o C → Grade = Fe500 (Confidence=100%, Importance=0.477, Support=8 cases) 4.1.2 Temperature of Billet = 1134o C-1136o C → Grade = Fe500 (Confidence=100%, Importance=0.341, Support=5 cases) 4.1.3 Temperature of Bar before Quenching = 875o C-8790 C → Grade = Fe500 (Confidence=100%, Importance=0.301, Support=4 cases) 4.2 Association Rules for Fe550 4.2.1 Temperature of Billet=1145o C → Grade = Fe550 (Confidence=100%, Importance=0.477, Support=8 cases) 4.2.2 Temperature of Bar after Quenching >= 570o C-581o C → Grade = Fe550 (Confidence=100%, Importance=0.381, Support= 6 cases) 5. NEURAL NETWORK CLASSIFICATION Neural Network (NN) classification is being commonly used to analyze the manufacturing processes. Neural Networks often referred to as Artificial Neural Networks (ANN) to distinguish them from biological neural networks and are modeled after the working of human brain. [8] It attempts to exhibit intelligent behavior by trying to mimic structure of nervous system. The nervous system is viewed as weighted directed graph where the nodes are neurons and edges between them are connections between neurons and weights are assigned to neural connections. [9] NN approach has gained the popularity due to the fact that real world problems have complex decision boundaries and contain noise. [10] ANN models shall be trained to solve non-linear and complex problems and are robust against noise. ANN have been implemented in variety of applications such as injection molding process, thermal spray process, plasma spraying process, reaming process, laser sintering process [4] One of the disadvantages of NN is large computational time compared to other mining techniques. In this work, Neural Network model have been developed by Microsoft NN algorithm to predict the grade of TMT bar. Table 2 shows the impact of Attribute-Value (AV) pairs related to predicting grades Fe500 and Fe550. The most important AV pair that favors Fe500 grade is Temperature of Billet in the range between 1134o C- 1136o C and the most important AV pair that favors Fe550 is Temperature of Billet in the range between 1140o C-1145o C.
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 285 Table 2 Neural Network Viewer Attribute Value Favors Fe500 Favors Fe550 Temp Of Billet 1134 - 1136 100 Temp Of Billet 1140 - 1145 88.25 Temp Of Bar After Quenching >= 602 88.15 Temp Of Bar After Quenching 581 - 595 84.31 Temp Of Bar After Quenching 570 - 581 79.38 Temp Of Bar Before Quenching 875 - 879 78.33 Temp Of Bar After Quenching < 570 77.85 Temp Of Bar Before Quenching 908 - 914 75.16 Temp Of Bar Before Quenching >= 914 75 Weight Per Meter Length < 0.37724999995 74.11 Temp Of Billet >= 1145 70.85 Temp Of Billet < 1134 56.6 Temp Of Bar After Quenching 595 - 602 54.75 Temp Of Billet 1136 - 1140 47.41 Temp Of Bar Before Quenching < 875 46.41 Temp Of Bar Before Quenching 879 - 908 38.5 Weight Per Meter Length >= 0.39449999995 36.91 Pressure Of Water For Quenching 12.5 27.74 Weight Per Meter Length 0.37724999995 - 0.38157142855 26.8 Pressure Of Water For Quenching 12.7 25.88 Weight Per Meter Length 0.3853333333 - 0.39449999995 23.89 Temp Of Furnace 1225 22.13 Temp Of Furnace 1215 20.77 Percentage Of Manganese 0.6 19.94 Temp Of Water For Quenching 30 18.35 Percentage Of Manganese 0.55 17.93 Temp Of Water For Quenching 29 17.84 Percentage Sulphur 0.04 16.71 Percentage Sulphur 0.055 16.24 Percentage Of Phosphorous 0.055 15.84 Percentage Of Phosphorous 0.06 15.03 Percentage Of Carbon 0.23 14.75 Percentage Of Carbon 0.21 14.59 Weight Per Meter Length 0.38157142855 - 0.3853333333 6.32
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 286 6. CLASSIFICATION BY LOGISTIC REGRESSION The mathematical model used in classic logistic regression is identical to the one mathematical model of MS Neural Network model, which does not contain a hidden layer. It is less expensive to train and not a weaker predictor. MS Logistic Regression is implemented by forcing the hidden layer of neural network to have zero nodes. [5] MS logistic Regression model have been applied to predict the grade of TMT bar by making use of input attributes. Fig.3 shows comparative view of attributes and their values favoring Fe500 and Fe550 grades of TMT bar. The most important AV pair that favors Fe500 grade is Weight per meter length < 0.377 kg. The most important AV pair that favors Fe550 is Temperature of Bar after Quenching < 570o C. Fig.3 MS Neural Network Viewer showing Attribute-Value pairs favoring Fe500 & Fe550 7. EVALUATION MEASURES Data mining models can be assessed for their prediction accuracy by using accuracy chart, classification matrix and cross validation. The prediction accuracy of MS Naïve Bayes, Association, Neural Network and Logistic models presented in this paper have been evaluated by standard lift chart and classification matrix. Fig.4 presents a matrix per model with counts of each pair wise combination of actual values and predicted value. Classification matrix shows how many times each model made a correct prediction and what predictions were given when predictions were wrong. Fig.5 shows standard lift chart displaying the probabilities of prediction of these data mining models in predicting Fe500 grade of TMT bar. Naives Bayes, Neural Network, Logistic Regression Association models predicted Fe500 grade of TMT bar on test data with probabilities of 93.1%, 96.3%, 96.3% and 100% respectively. Lift chart measured the accuracy of models on test data with 20% hold out cases.
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 287 Fig.4 Classification matrix Fig.5 Standard Lift Chart 8. CONCLUSION In this work, MS Naïve Bayes, Neural Network, Association and Logistic Regression models have been implemented to extract useful and expressive knowledge from rolling process data set. The billet temperature, temperature of bar before and after quenching, weight/length ratio were identified by data mining models as major parameters that have impact on different grades of TMT bar. The rules extracted from Association model and attribute-value pairs identified by other models favoring particular grades shall be used in optimal setting of process parameters in achieving desirable grade. It is also possible to predict grade of TMT bar for a particular combination of attribute-value pair by applying any one or more of the data mining models of our choice.
  • International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 288 9. FUTURE WORK The quality of prediction of these models can be enhanced further by considering other parameters such as rolling force, peripheral velocity of rollers, temperatures of bar and cross sectional area reduction at each roller stand etc. A process data base shall be build up by collecting process data at regular intervals by employing a data acquisition system equipped with sensors. The data mining models can be made more robust and reliable by training them on such process data base and can be up dated at predetermined time intervals. An expert system can be developed by extracting knowledge through the mining models and after validation of the same by domain experts to manufacture TMT bars of high quality. REFERENCES [1] Lior Rokach, Oded Maimon (2006), “Data mining for improving the quality of manufacturing: a feature set decomposition approach”, Journal of Intelligent Manufacturing, Vol.1, pp. 285-299. [2] A.K.Choudhary , J.A.Harding, M.K.Tiwari (2008), “Data mining in manufacturing: a review based on kind of knowledge”, Journal of Intelligent Manufacturing. [3] J.A. Harding, M.Shahbaz, Srinivas, A.Kusiak (2006), Data mining in Manufacturing: A Review, Journal of Manufacturing Science and Engineering, Vol. 128, pp.969-976. [4] Gulser Koksal, Inci Batmaz, Murat Caner Testik (2011), “A review of data mining applications for quality improvement in manufacturing industry”, Expert systems with Applications, Vol. 38, pp.13448-13467. [5] Jamie Maclennan, ZhaoHui Tang, Bogdan Crivat, Data Mining with Microsoft SQL Server 2008(Wiley India Pvt. Ltd, 2009), pp.215-317. [6] Ruhaizan Ismail, Zalinda Othman and Azuraliza Abu Bakar (2010), “Associative Prediction Model and Clustering for Product Forecast Data”, IEEE, pp1459-1464. [7] Han.J and Kamber.M, Data Mining: Concepts and Techniques, (Morgan Kaufmann Publishers, 2008), pp.227-230. [8] Margaret H. Dunham, Data Mining, (Pearson, 2012), pp. 59-63. [9] Vikram Pudi, P.Radha Krishna, Data Mining,( Oxford University Press, 2010), pp.111- 116. [10] Karim M.A, Russ G., and Islam A., “Detection of faulty products using data mining”, Proceedings of International Workshop on Data Mining and Artificial Intelligence (DMAI' 08) 24 December, 2008, Khulna, Bangladesh, pp.101-107. [11] D. Kanakaraja, P. Hema and K. Ravindranath, “Comparative Study on Different Pin Geometries of Tool Profile in Friction Stir Welding using Artificial Neural Networks”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp. 245 - 253, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [12] Dr P.Ravinder Reddy, Dr K.Srihari and Dr S. Raji Reddy, “Combined Heat and Mass Transfer in MHD Three-Dimensional Porous Flow with Periodic Permeability & Heat Absorption”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 573 - 593, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.