http://www.iaeme.com/IJMET/index.asp 31 editor@iaeme.com International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 6, November–December 2016, pp.31–40, Article ID: IJMET_07_06_004 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=7&IType=6 Journal Impact Factor (2016): 9.2286 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication DATA MINING APPROACH FOR QUALITY PREDICTION AND IMPROVEMENT OF INJECTION MOLDING PROCESS THROUGH SANN, GCHAID AND ASSOCIATION RULES Dr. E V Ramana Professor, Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India S Sapthagiri Associate Professor, Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India P Srinivas Associate Professor, Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India ABSTRACT Data mining technologies are slowly finding their way in determining complex relationships among process variables in large datasets generated by different industrial processes. Extracting process knowledge by finding trends, patterns, and anomalies in these datasets, and making use of them in real time process control is a challenging task. Plastic injection molding process is no exception due to the influence of large number of factors on the quality of product. In this study, a data mining approach has been applied for the quality prediction of the plastic injection molding process by using Statistica Automated Neural Networks (SAAN), General Chi-square Automatic Interaction Detector (GCHAID) and Association Rules. Key words: Injection Molding; Statistica Automated Neural Networks (SAAN); General CHAID (GCHAID) and Association Rules. Cite this Article: Dr. E V Ramana, S Sapthagiri and P Srinivas, Data Mining Approach for Quality Prediction and Improvement of Injection Molding Process through SANN, GCHAID and Association Rules. International Journal of Mechanical Engineering and Technology, 7(6), 2016, pp. 31–40. http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=7&IType=6 1. INTRODUCTION Quality improvement of injection molding products requires collection and analysis of process data to identify quality related problems. The main causes of defects in injection molding are generally related with material, packing, filling, cooling etc. The improper selection of processing parameters results in defective products in injection molding. In this paper, an investigation has been made to apply data mining