FIS-SMED: a fuzzy inference system application for plastic injection mold changeover

Dr. Shingo’s SMED (single minute exchange of dies) methodology is the most well-known method for changeover time reduction using both simple methodological solutions and tool/design changes. Simplification and standardization are the main technique of SMED to make the changeover process independent from personal experience. However, in plastic injection molding, process parameter setting after changing the molds totally depends on the varied expertise levels of setup experts. The number of available setup experts in a shift dictates the number of changeover that can be given to the production plan. Due to this dependency, expected benefits of SMED cannot be realized. In this paper, an application of a fuzzy inference system (FIS) is presented for parameter adjustments during changeovers on plastic injection molds. The proposed system captures the highest level of domain expertise and makes it applicable by machine operators. Integrating this system into SMED applications encourages production lot size reduction. Moreover, proposed FIS increases the quality awareness of machine operators and can be used to train new ones.

[1]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[2]  Donald V. Rosato,et al.  Injection Molding Handbook , 1985 .

[3]  Elias Siores,et al.  An intelligent system for plastic injection molding process design , 1996 .

[4]  C. K. Kwong,et al.  Application of case based reasoning injection moulding , 1997 .

[5]  Huei-Huang Lee,et al.  Application of Taguchi's method on the optimal process design of an injection molded PC/PBT automobile bumper , 1997 .

[6]  Andrew Y. C. Nee,et al.  Automated process parameter resetting for injection moulding: a fuzzy-neuro approach , 1998, J. Intell. Manuf..

[7]  C. K. Kwong,et al.  A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach , 1998 .

[8]  Geraint W Owen,et al.  Rapid changeover – a pre‐requisite for responsive manufacture , 1999 .

[9]  Henry C. W. Lau.  Neural-fuzzy modeling of plastic injection molding machine for intelligent control , 1999 .

[10]  Ws S. Lau,et al.  An intelligent hybrid system for initial process parameter setting of injection moulding , 2000 .

[11]  B.H.M. Sadeghi,et al.  A BP-neural network predictor model for plastic injection molding process , 2000 .

[12]  Spyros G. Tzafestas,et al.  Incremental fuzzy supervisory controller design for optimizing the injection molding process , 2001, Expert Syst. Appl..

[13]  Arie Ben-David,et al.  Control of properties in injection molding by neural networks , 2001 .

[14]  Ming-Chih Huang,et al.  The effective factors in the warpage problem of an injection-molded part with a thin shell feature , 2001 .

[15]  Tao-Chih Chang,et al.  Shrinkage behavior and optimization of injection molded parts studied by the taguchi method , 2001 .

[16]  C. K. Kwong,et al.  Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding , 2002, J. Intell. Manuf..

[17]  C. Lotti,et al.  Prediction of the shrinkage of injection molded iPP plaques using artificial neural networks , 2002 .

[18]  Fanhuai Shi,et al.  Optimisation of Plastic Injection Moulding Process with Soft Computing , 2003 .

[19]  L. S. Tsou,et al.  Optimal process conditions of shrinkage and warpage of thin‐wall parts , 2004 .

[20]  Y. C. Su,et al.  Shrinkage and warpage prediction of injection-molded thin-wall parts using artificial neural networks , 2004 .

[21]  Hasan Kurtaran,et al.  Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm , 2005 .

[22]  R. Chien,et al.  Optimum Design of Gas-assisted Injection Molding , 2005 .

[23]  Babur Ozcelik,et al.  Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm , 2006 .

[24]  R Spina,et al.  OPTIMIZATION OF INJECTION MOLDED PARTS BY USING ANN-PSO APPROACH , 2006 .

[25]  Yung-Kuang Yang,et al.  Optimization of Injection-Molding Process of Short Glass Fiber and Polytetrafluoroethylene Reinforced Polycarbonate Composites via Design of Experiments Method: A Case Study , 2006 .

[26]  Hasan Kurtaran,et al.  Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm , 2006 .

[27]  Qian Li,et al.  Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method , 2007 .

[28]  Mancang Song,et al.  Research on effects of injection process parameters on the molding process for ultra-thin wall plastic parts , 2007 .

[29]  N. Ismail,et al.  The use of Taguchi method in the design of plastic injection mould for reducing warpage , 2007 .

[30]  H. Öktem,et al.  Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part , 2007 .

[31]  Yuehua Gao,et al.  Adaptive geometry and process optimization for injection molding using the kriging surrogate model trained by numerical simulation , 2008 .

[32]  Yuehua Gao,et al.  An effective warpage optimization method in injection molding based on the Kriging model , 2008 .

[33]  Wen-Chin Chen,et al.  A systematic optimization approach in the MISO Plastic Injection molding process , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[34]  Wen-Chin Chen,et al.  Process parameter optimization for MIMO plastic injection molding via soft computing , 2009, Expert Syst. Appl..

[35]  D. Mathivanan,et al.  Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm , 2009 .

[36]  Wen-Chin Chen,et al.  An integrated parameter optimization system for MISO plastic injection molding , 2009 .

[37]  Chih-Hung Tsai,et al.  Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis , 2009, Expert Syst. Appl..

[38]  Yuehua Gao,et al.  Surrogate-based process optimization for reducing warpage in injection molding , 2009 .

[39]  Yung-Kuang Yang,et al.  Modeling and Optimization of Injection Molding Process Parameters for Thin-Shell Plastic Parts , 2009 .

[40]  Kuo-Ming Tsai,et al.  A study of the effects of process parameters for injection molding on surface quality of optical lenses , 2009 .

[41]  Mirigul Altan,et al.  Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods , 2010 .

[42]  Y. Lam,et al.  A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage , 2010 .

[43]  Zahid A. Khan,et al.  Application of Taguchi Method in the Optimization of Injection Moulding Parameters for Manufacturing Products from Plastic Blend , 2010 .

[44]  Wu-Lin Chen,et al.  Optimization of plastic injection molding process by dual response surface method with non‐linear programming , 2010 .

[45]  Xionghui Zhou,et al.  Multi-objective optimal approach for injection molding based on surrogate model and particle swarm optimization algorithm , 2010 .

[46]  Bin Luo,et al.  An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding , 2011 .

[47]  Lin Hua,et al.  Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding , 2011 .

[48]  L. Hua,et al.  A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters , 2011 .

[49]  S. Kamaruddin,et al.  Investigating the Effects of Injection Molding Parameters on the Mechanical Properties of Recycled Plastic Parts Using the Taguchi Method , 2011 .

[50]  Chih-Cherng Chen,et al.  Experimental Investigation of Designed Parameters on Dimension Shrinkage of Injection Molded Thin-Wall Part by Integrated Response Surface Methodology and Genetic Algorithm: A Case Study , 2011 .

[51]  Shahrul Kamaruddin,et al.  Optimization of mechanical properties of recycled plastic products via optimal processing parameters using the Taguchi method , 2011 .

[52]  Shahrul Kamaruddin,et al.  Multi-Response Optimization of Injection Moulding Processing Parameters Using the Taguchi Method , 2011 .

[53]  Gang Xu,et al.  Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization , 2012 .

[54]  Shahrul Kamaruddin,et al.  External Setup in SMED Improvement in an Injection Molding Manufacturing Company , 2012 .

[55]  Chih-Hung Tsai,et al.  A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology , 2012 .

[56]  David Frederick Ross,et al.  Distribution: Planning and Control , 2012 .

[57]  Y. H. Çelik,et al.  Calculating Molding Parameters in Plastic Injection Molds with ANN and Developing Software , 2012 .

[58]  Huizhuo Shi,et al.  A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy , 2013 .

[59]  Wu-Lin Chen,et al.  Finding efficient frontier of process parameters for plastic injection molding , 2013 .

[60]  Konstantinos Salonitis,et al.  Improving Changeover Time: A Tailored SMED Approach for Welding Cells , 2013 .

[61]  Rongji Wang,et al.  Evaluation of Effect of Plastic Injection Molding Process Parameters on Shrinkage Based on Neural Network Simulation , 2013 .

[62]  Jorge Luis García Alcaraz,et al.  Optimization of injection molding process parameters by a hybrid of artificial neural network and artificial bee colony algorithm , 2013 .

[63]  Gyung Ju Kang,et al.  Process Factor Optimization for Reducing Warpage and Shrinkage in Injection Molding Using Design of Experiments , 2014 .

[64]  Mehmet Cakmakci,et al.  Improvement of changeover times via Taguchi empowered SMED/case study on injection molding production , 2014 .

[65]  Wen-Chin Chen,et al.  Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA , 2014 .

[66]  Wen-Chin Chen,et al.  An integrated parameter optimization system for MIMO plastic injection molding using soft computing , 2014 .

[67]  Dimitris Mourtzis,et al.  Knowledge Capturing and Reuse to Support Manufacturing of Customised Products: A Case Study from the Mould Making Industry☆ , 2014 .

[68]  Jian Zhao,et al.  Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm , 2015, The International Journal of Advanced Manufacturing Technology.

[69]  Lijun Li,et al.  Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory , 2015 .

[70]  Dilip Datta,et al.  Process parameter optimization of plastic injection molding: a review , 2015, International Journal of Plastics Technology.