Development of artificial neural network-based in-process mixed-material-caused flash monitoring (ANN-IPMFM) system in injection molding

This paper describes the development of an artificial neural network-based in-process mixed-material-caused flash monitoring system (ANN-IPMFM) in the injection molding process. This proposed system integrates two sub-systems. One is the vibration monitoring sub-system that utilizes an accelerometer sensor to collect and process vibration signals during the injection molding process. The other, a threshold prediction sub-system, predicts a control threshold based on the process parameter settings, thus allowing the system to adapt to changes in these settings. The integrated system compares the monitored vibration signals with the control threshold to predict whether or not flash will occur. The performance of the ANN-IPMFM system was determined by using varying ratios of polystyrene (PS) and low-density polyethylene (LDPE) in the injection molding process, and comparing the number of actual occurrences of flash with the number of occurrences predicted by the system. After a 180 trials, results demonstrated that the ANN-IPMFM system could predict flash with 92.7% accuracy.

[1]  Alice E. Smith.  Predicting product quality with backpropagation: A thermoplastic injection moulding case study , 1993 .

[2]  Mrinal Bhattacharya,et al.  Processing of biodegradable blends of wheat gluten and modified polycaprolactone , 1998 .

[3]  Dong Young Jang,et al.  Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning , 1996 .

[4]  Tim A. Osswald,et al.  Injection molding handbook , 2008 .

[5]  David Kazmer,et al.  Hybrid neural models for pressure control in injection molding , 1999 .

[6]  Z. Xia,et al.  Control of dimensional variability in injection molded plastic parts , 1997 .

[7]  Joseph C. Chen,et al.  An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .

[8]  Xiuzhi Susan Sun,et al.  Properties of soy protein isolate/polycaprolactone blends compatibilized by methylene diphenyl diisocyanate ☆ , 2001 .

[9]  D. H. Besterfield.  Quality control , 1979 .

[10]  Furong Gao,et al.  Melt temperature profile prediction for thermoplastic injection molding , 1999 .

[11]  N. Rewal,et al.  Predicting part quality in injection molding using artificial neural networks , 1998 .

[12]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[13]  Joseph C. Chen,et al.  On-line surface roughness recognition system using artificial neural networks system in turning operations , 2003 .

[14]  Wei He,et al.  Development of a Fuzzy-Neuro System for Parameter Resetting of Injection Molding , 2001 .

[15]  Douglas J. Cooper,et al.  Online pattern‐based part quality monitoring of the injection molding process , 1996 .

[16]  T. Richardson.  Industrial plastics: theory and application , 1989 .

[17]  Sang-Gook Kim,et al.  Optimization of process parameters of injection molding with neural network application in a process simulation environment , 1994 .

[18]  Jae Ryoun Youn,et al.  Shrinkage Analysis of Molded Parts Using Neural Network , 1999 .

[19]  A. B. Strong,et al.  Plastics: Materials and Processing , 1996 .

[20]  M. Viens,et al.  On-line ultrasonic monitoring of the injection molding process , 1997 .

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