Analysis of the Machine-Specific Behavior of Injection Molding Machines

被引:5
作者
Knoll, Julia [1 ]
Heim, Hans-Peter [1 ]
机构
[1] Univ Kassel, Inst Mat Engn, Polymer Engn, D-34125 Kassel, Germany
关键词
injection molding; machine-specific behavior; process analysis; machine fingerprint; QUALITY PREDICTION; NEURAL-NETWORK; OPTIMIZATION; COMBINATION;
D O I
10.3390/polym16010054
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
The performance of an injection molding machine (IMM) influences the process and the quality of the parts manufactured. Despite increasing data collection capabilities, their machine-specific behavior has not been extensively studied. To close corresponding research gaps, the machine-specific behavior of two hydraulic IMMs of different sizes and one electric IMM were compared with each other as part of the investigations. Both the start-up behavior from the cold state and the behavior of the machine at different operating points were considered. To complement this, the influence of various material properties on the machine-specific behavior was investigated by processing an unreinforced and glass-fiber-reinforced polyamide. The results obtained provide crucial insights into machine-specific behavior, which may, for instance, account for disparities between computer fluid dynamic (CFD) simulations and experimental results. Furthermore, it is expected that the description of the machine-specific behavior can contribute to transfer knowledge when applying transfer learning algorithms. Looking ahead to future research, it is advised to create what is referred to as a "machine fingerprint", and this proposal is accompanied by some preliminary recommendations for its development.
引用
收藏
页数:21
相关论文
共 33 条
[1]  
[Anonymous], 2012, ISO 527-2
[2]  
Bichler M., 2002, Prozessgrossen Beim Spritzgiessen: Analyse und Optimierung
[3]   Online Prediction of Molded Part Quality in the Injection Molding Process Using High-Resolution Time Series [J].
Bogedale, Lucas ;
Doerfel, Stephan ;
Schrodt, Alexander ;
Heim, Hans-Peter .
POLYMERS, 2023, 15 (04)
[4]   Enhancement of Injection Molding Consistency by Adjusting Velocity/Pressure Switching Time Based on Clamping Force [J].
Chen, J-Y ;
Liu, C-Y ;
Huang, M-S .
INTERNATIONAL POLYMER PROCESSING, 2019, 34 (05) :564-572
[5]   A neural network-based approach for dynamic quality prediction in a plastic injection molding process [J].
Chen, Wen-Chin ;
Tai, Pei-Hao ;
Wang, Min-Wen ;
Deng, Wei-Jaw ;
Chen, Chen-Tai .
EXPERT SYSTEMS WITH APPLICATIONS, 2008, 35 (03) :843-849
[6]   The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation [J].
Chicco, Davide ;
Warrens, Matthijs J. ;
Jurman, Giuseppe .
PEERJ COMPUTER SCIENCE, 2021,
[7]  
Eben J., 2014, Ph.D Thesis
[8]   Learning quality characteristics for plastic injection molding processes using a combination of simulated and measured data [J].
Finkeldey, Felix ;
Volke, Julia ;
Zarges, Jan-Christoph ;
Heim, Hans-Peter ;
Wiederkehr, Petra .
JOURNAL OF MANUFACTURING PROCESSES, 2020, 60 :134-143
[9]   Transfer learning of machine learning models for multi-objective process optimization of a transferred mold to ensure efficient and robust injection molding of high surface quality parts [J].
Gim, Jinsu ;
Yang, Huaguang ;
Turng, Lih-Sheng .
JOURNAL OF MANUFACTURING PROCESSES, 2023, 87 :11-24
[10]  
Haman S., 2004, THESIS TU CHEMNITZ C