E. V. Ramana ¹, S. Sapthagiri ², P. Srinivas ²

Affiliations
1 Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad – 500 090, Telangana, IN
2 Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Hyderabad - 501506, Telangana, IN

Abstract

Objectives: To implement data mining approach to diagnose the causes of faults occurring in the injection molding product and to predict the quality of product for a particular setting of process parameters. Methods and Statistical Analysis: Decision Tree, k-Nearest Neighbor (k-NN) and Polynomial by Binomial classification techniques are used to build the data mining models by training them on dataset collected during the injection molding of a cap for 25 ml container. Findings: These models are evaluated on test dataset and their prediction accuracy is found to be 95%. Sink marks are caused by low injection speed, nozzle temperature and injection pressure. Low nozzle and mould temperatures and injection pressure resulted in short shot. High barrel temperature at Zone 2 and injection speed are responsible for burn marks in the product. Applications/Improvements: The higher prediction accuracy of these models is helpful in predicting the quality of product before its manufacture and thereby avoiding the production of defective parts. This approach can be further extended for injection molded parts made out of various plastic materials and process conditions.

Keywords

Data Mining, Fault Diagnosis, Injection Molding, Quality Prediction.

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E. V. Ramana ¹, S. Sapthagiri ², P. Srinivas ²

Affiliations
1 Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad – 500 090, Telangana, IN
2 Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Hyderabad – 501506, Telangana, IN

Abstract

Objectives: This paper proposes data mining approach to detect the causes of sink marks, short shot, burn marks and flash in 25 ml container cap and control the injection molding process to produce defect free product by optimal setting of process parameters. Methods and Statistical Analysis: Neural Net, Naive Bayes classifier and rule induction techniques are implemented to build the data mining models on training dataset acquired during the injection molding of 25 ml container cap. Findings: Neural Net and Rule Induction Models outperformed over Naive Bayes model (80%) with prediction accuracies of 95% on test dataset. Rule Induction model detected that sink marks are caused by high molding temperature, low injection speed, nozzle temperature and injection time. Low injection pressure, barrel temperature and mould temperature are responsible for short shot to occur in the product. Flash is caused by high mould temperature. Applications/Improvements: The custom prediction for quality of the product for a specific parameter setting can be made by applying Neural Net and Rule Induction models in order to control the process to ensure defect free caps. This approach can be applied for other injection molded products manufactured from other plastic materials.

Keywords

Data Mining, Fault Diagnosis, Quality Prediction and Control, Injection Molding.

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