scholarly journals In-Mold Sensors for Injection Molding: On the Way to Industry 4.0

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3551 ◽  
Author(s):  
Tatyana Ageyeva ◽  
Szabolcs Horváth ◽  
József Gábor Kovács
Keyword(s):  
Process Control ◽  
Injection Molding ◽  
Process Monitoring ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Industry 4.0 ◽  
Mold Cavity ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process

The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques—direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.

scholarly journals Towards a general application programming interface (API) for injection molding machines

2020 ◽  
Vol 6 ◽  
pp. e302
Author(s):  
Olga Ogorodnyk ◽  
Mats Larsen ◽  
Ole Vidar Lyngstad ◽  
Kristian Martinsen
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Data Exchange ◽  
Application Programming Interface ◽  
Raspberry Pi ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Application Programming ◽  
Programming Interface

Injection molding is a complicated process, and the final part quality depends on many machine and process parameters settings. To increase controllability of the injection molding process, acquisition of the process data is necessary. This paper describes the architecture and development of a prototype of an open application programming interface (API) for injection molding machines (IMMs), which has the potential to be used with different IMMs to log and set the necessary process parameter values. At the moment, the API includes an implementation of EMI data exchange protocol and can be used with ENGEL IMMs with CC300 and RC300 controllers. Data collection of up to 97 machine and process parameters (the number might vary depending on the type of machine at hand), obtained from sensors installed in the machine by the manufacturer is allowed. The API also includes a module for the acquisition of data from additional 3d party sensors. Industrial Raspberry Pi (RevPi) was used to perform analog-to-digital signal conversion and make sensors data accessible via the API prototype. The logging of parameters from the machine and from sensors is synchronized and the sampling frequency can be adjusted if necessary. The system can provide soft real-time communication.

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

scholarly journals Constant Temperature Approach for the Assessment of Injection Molding Parameter Influence on the Fatigue Behavior of Short Glass Fiber Reinforced Polyamide 6

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1569
Author(s):  
Selim Mrzljak ◽  
Alexander Delp ◽  
André Schlink ◽  
Jan-Christoph Zarges ◽  
Daniel Hülsbusch ◽  
...  
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Fatigue Properties ◽  
Injection Molding Process ◽  
Molding Process ◽  
Short Glass Fiber ◽  
Glass Fiber Reinforced ◽  
Molding Parameter ◽  
Short Glass

Short glass fiber reinforced plastics (SGFRP) offer superior mechanical properties compared to polymers, while still also enabling almost unlimited geometric variations of components at large-scale production. PA6-GF30 represents one of the most used SGFRP for series components, but the impact of injection molding process parameters on the fatigue properties is still insufficiently investigated. In this study, various injection molding parameter configurations were investigated on PA6-GF30. To take the significant frequency dependency into account, tension–tension fatigue tests were performed using multiple amplitude tests, considering surface temperature-adjusted frequency to limit self-heating. The frequency adjustment leads to shorter testing durations as well as up to 20% higher lifetime under fatigue loading. A higher melt temperature and volume flow rate during injection molding lead to an increase of 16% regarding fatigue life. In situ Xray microtomography analysis revealed that this result was attributed to a stronger fiber alignment with larger fiber lengths in the flow direction. Using digital volume correlation, differences of up to 100% in local strain values at the same stress level for different injection molding process parameters were identified. The results prove that the injection molding parameters have a high influence on the fatigue properties and thus offer a large optimization potential, e.g., with regard to the component design.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

Optimization of Injection Molding Process Parameters with Material Properties Based on GA and BP

2013 ◽  
Vol 345 ◽  
pp. 586-590 ◽  
Author(s):  
Xiao Hong Tan ◽  
Lei Gang Wang ◽  
Wen Shen Wang
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Axial Deformation ◽  
Molding Process ◽  
Melt Temperature ◽  
Bp Network ◽  
Injection Parameters ◽  
Moldflow Simulation

To obtain optimal injection process parameters, GA was used to optimize BP network structure based on Moldflow simulation results. The BP network was set up which considering the relationship between volume shrinkage of plastic parts and injection parameters, such as mold temperature, melt temperature, holding pressure and holding time etc. And the optimal process parameters are obtained, which is agreed with actual results. Using BP network to predict injection parameters impact on parts quality can effectively reduce the difficulty and workload of other modeling methods. This method can be extended to other quality prediction in the process of plastic parts.Keyword: Genetic algorithm (GA);Neural network algorithm (BP);Injection molding process optimization;The axial deformation

Injection molding process monitoring using a self-energized dual-parameter sensor

CIRP Annals ◽  
2008 ◽  
Vol 57 (1) ◽  
pp. 389-393 ◽  
Author(s):  
Robert X. Gao ◽  
Zhaoyan Fan ◽  
David O. Kazmer
Keyword(s):  
Injection Molding ◽  
Process Monitoring ◽  
Injection Molding Process ◽  
Molding Process
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