Research on Warpage of the Microfluidic Chip in Injection Molding Process

2014 ◽  
Vol 926-930 ◽  
pp. 345-348
Author(s):  
Lai Yu Zhu ◽  
Chun Peng Chu ◽  
Bing Yan Jiang
Keyword(s):  
Injection Molding ◽  
Coordinate Measuring Machine ◽  
Holding Time ◽  
Injection Molding Process ◽  
Microfluidic Chips ◽  
Mould Temperature ◽  
Molding Process ◽  
Injection Speed ◽  
Cover Sheet ◽  
Measuring Machine

Reducing volumetric warpage during the injection molding process is a challenging problem in the production of microfluidic chips, as the warpage directly affects the bonding quality of the substrate and the cover sheet. In this study, the injection molding of substrate and the cover sheet, composed of PolymethylMethacrylate(PMMA), was simulated. The effect of different process parameters, holding pressure, holding time, mould temperature and injection speed, were investigated via single factor experiments, observing the warpage of the sheet with Three-Coordinate Measuring Machine. The analysis showed that the warpage was affected by non-uniform shrinkage and residual stress of the melt. Holding pressure and holding time had a greater effect on the warpage than the mould temperature and injection speed did. Therefore, reasonable holding pressure and holding time can effectively reduce the warpage of microfluidic chips in the injection molding process.

scholarly journals Experimental Study and Numerical Simulation of Injection Molding Process for Special-shaped Plastic Part

2015 ◽  
Vol 9 (1) ◽  
pp. 416-421
Author(s):  
Chen Xiaoyong ◽  
Wang Qian
Keyword(s):  
Numerical Simulation ◽  
Experimental Study ◽  
Injection Molding ◽  
Orthogonal Experiment ◽  
Coordinate Measuring Machine ◽  
Injection Molding Process ◽  
Plastic Part ◽  
Molding Process ◽  
Measuring Machine ◽  
Cae Technology

Taking the special-shaped plastic part as the research object, experimental study and numerical simulation of injection molding process were performed using numerical simulation technology, orthogonal experiment method, software Moldflow, injection machine and coordinate measuring machine (CMM). The better feeding system and optimal molding process parameters were proposed and qualified products were produced. The research results show that the efficiency of the simulation guidance would be significantly improved by combining the CAE technology and production experience.

scholarly journals Effects of Injection Molding Parameters on Properties of Insert-Injection Molded Polypropylene Single-Polymer Composites

Polymers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Jian Wang ◽  
Qianchao Mao ◽  
Nannan Jiang ◽  
Jinnan Chen
Keyword(s):  
Tensile Strength ◽  
Injection Molding ◽  
Injection Pressure ◽  
Holding Time ◽  
Injection Molding Process ◽  
Processing Temperature ◽  
Molding Process ◽  
Multiple Production ◽  
Injection Speed ◽  
Injection Molded

The reinforcement and matrix of a polymer material can be composited into a single polymer composite (SPC), which is light weight, high strength, and has easy recyclability. The insert injection molding process can be used to realize the multiple production of SPC products with a short cycle time and wide processing temperature window. However, injection molding is a very complicated process; the influence of several important parameters should be determined to help in the future tailoring of SPCs to specific applications. The effects of varying barrel temperature, injection pressure, injection speed, and holding time on the properties of the insert-injection molded polypropylene (PP) SPC parts were investigated. It was found that the sample weight and tensile properties of the PP SPCs varied in different rules with the variations of these four parameters. The barrel temperature has a significant effect, followed by the holding time and injection pressure. Suitable parameter values should be determined for enhanced mechanical properties. Based on the tensile strength, a barrel temperature of 260 °C, an injection pressure of 127.6 MPa, an injection speed of 0.18 m/s, and a holding time of 60 s were determined as the optimum processing conditions. The best tensile strength and peel strength were up to 120 MPa and 19.44 N/cm, respectively.

scholarly journals A Review on Machine Learning Models in Injection Molding Machines

2022 ◽  
Vol 2022 ◽  
pp. 1-28
Author(s):  
Senthil Kumaran Selvaraj ◽  
Aditya Raj ◽  
R. Rishikesh Mahadevan ◽  
Utkarsh Chadha ◽  
Velmurugan Paramasivam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Injection Molding ◽  
Performance Criteria ◽  
Machine Learning Techniques ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Detection Techniques ◽  
And Performance

One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

Optimization and Design for Parameter in Injection Molding Technology of Cell Shell of Polypropylene

2011 ◽  
Vol 189-193 ◽  
pp. 537-540
Author(s):  
Jia Min Zhang ◽  
Ming Yi Zhu ◽  
Zhao Xun Lian ◽  
Rong Zhu
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Technological Parameters ◽  
Molding Process ◽  
Melt Temperature ◽  
Injection Speed ◽  
Technical Parameters ◽  
High Degree

The use of L27 (35) orthogonal to the battery shell injection molding process is optimized. The main factors of technical parameters were determined mould temperature, melt temperature, the speed of injection, injection pressure, cooling time.On the basis of actual production, to determine the factors values of different process parameters.Combination of scrapped products in key (reduction and a high degree of tolerance deflated) tests were selected in the process parameters within the scope of the assessment. Various factors impact on the product of the total height followed by cooling time, mold temperature, melt temperature, injection pressure, injection speed from strong to weak .The best products technological parameters were determined.Good results were obtained for production.

scholarly journals Effects of Injection Molding Process Parameters on the Chemical Foaming Behavior of Polypropylene and Polystyrene

Polymers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 2331
Author(s):  
Chen-Yuan Chung ◽  
Shyh-Shin Hwang ◽  
Shia-Chung Chen ◽  
Ming-Chien Lai
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Mold Temperature ◽  
Expansion Ratio ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Foaming Agent ◽  
Experimental Database ◽  
Injection Speed

In the present study, semi-crystalline polypropylene (PP) and amorphous polystyrene (PS) were adopted as matrix materials. After the exothermic foaming agent azodicarbonamide was added, injection molding was implemented to create samples. The mold flow analysis program Moldex3D was then applied to verify the short-shot results. Three process parameters were adopted, namely injection speed, melt temperature, and mold temperature; three levels were set for each factor in the one-factor-at-a-time experimental design. The macroscopic effects of the factors on the weight, specific weight, and expansion ratios of the samples were investigated to determine foaming efficiency, and their microscopic effects on cell density and diameter were examined using a scanning electron microscope. The process parameters for the exothermic foaming agent were optimized accordingly. Finally, the expansion ratios of the two matrix materials in the optimal process parameter settings were compared. After the experimental database was created, the foaming module of the chemical blowing agents was established by Moldex3D Company. The results indicated that semi-crystalline materials foamed less due to their crystallinity. PP exhibits the highest expansion ratio at low injection speed, a high melt temperature, and a low mold temperature, whereas PS exhibits the highest expansion ratio at high injection speed, a moderate melt temperature, and a low mold temperature.

Robust parameter search for IC tray injection molding using regrind resin

2020 ◽  
Vol 40 (10) ◽  
pp. 876-885
Author(s):  
Ming-Shyan Huang ◽  
Shih-Chih Nian
Keyword(s):  
Injection Molding ◽  
Integrated Circuit ◽  
Pressure Profile ◽  
Injection Molding Process ◽  
Repeated Injection ◽  
Molding Process ◽  
Injection Speed ◽  
Parametric Search ◽  
Continuous Mass ◽  
Robust Parameter

AbstractQuality consistency is essential in maximizing the productivity rate of the injection molding process and minimizing the production cost. The quality consistency problem is particularly acute in the case of injection molding processes performed using regrind resin, for which the rheological properties are less uniform and more unpredictable than those of virgin material. Accordingly, the present study proposes a two-stage approach for optimizing the injection molding process parameters in such a way as to achieve a consistent molding quality over repeated injection molding cycles. In the first stage, the values of the injection speed/pressure, velocity-to-pressure (V/P) switchover point, and packing pressure are individually determined based on an inspection of the cavity pressure profile and machine parameters provided by the injection molding machine controller. In the second stage, a robust parametric search method based on a first-order regression model is employed to determine the optimal combination of the process parameter settings. Using an Integrated Circuit (IC) tray fabricated from regrind resin for illustration purposes, the results confirm that the proposed method overcomes the problem of small variations in the melt quality and therefore provides an effective technique for improving the yield rate and quality of the continuous mass production.

scholarly journals Study of an Online Monitoring Adaptive System for an Injection Molding Process Based on a Nozzle Pressure Curve

Polymers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 555
Author(s):  
Jia-Chen Fan-Jiang ◽  
Chi-Wei Su ◽  
Guan-Yan Liou ◽  
Sheng-Jye Hwang ◽  
Huei-Huang Lee ◽  
...  
Keyword(s):  
Control System ◽  
Injection Molding ◽  
Peak Pressure ◽  
Viscosity Index ◽  
Injection Molding Process ◽  
Pressure Curve ◽  
Molding Process ◽  
Injection Speed ◽  
Nozzle Pressure ◽  
Product Weight

Injection molding is a popular process for the mass production of polymer products, but due to the characteristics of the injection process, there are many factors that will affect the product quality during the long fabrication processes. In this study, an adaptive adjustment system was developed by C++ programming to adjust the V/P switchover point and injection speed during the injection molding process in order to minimize the variation of the product weight. Based on a series of preliminary experiments, it was found that the viscosity index and peak pressure had a strong correlation with the weight of the injection-molded parts. Therefore, the viscosity index and peak pressure are used to guide the adjustment in the presented control system, and only one nozzle pressure sensor is used in the system. The results of the preliminary experiments indicate that the reduction of the packing time and setting enough clamping force can decrease the variation of the injected weight without turning on the adaptive control system; meanwhile, the master pressure curve obtained from the preliminary experiment was used as the control target of the system. With this system, the variation of the product weight and coefficient of variation (CV) of the product weight can be decreased to 0.21 and 0.05%, respectively.

scholarly journals Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Polymers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3874
Author(s):  
Yan-Mao Huang ◽  
Wen-Ren Jong ◽  
Shia-Chung Chen
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Transfer Learning ◽  
Analysis Data ◽  
Mold Temperature ◽  
Training Data ◽  
Injection Molding Process ◽  
Backpropagation Neural Network ◽  
Molding Process ◽  
Injection Speed

This study addresses some issues regarding the problems of applying CAE to the injection molding production process where quite complex factors inhibit its effective utilization. In this study, an artificial neural network, namely a backpropagation neural network (BPNN), is utilized to render results predictions for the injection molding process. By inputting the plastic temperature, mold temperature, injection speed, holding pressure, and holding time in the molding parameters, these five results are more accurately predicted: EOF pressure, maximum cooling time, warpage along the Z-axis, shrinkage along the X-axis, and shrinkage along the Y-axis. This study first uses CAE analysis data as training data and reduces the error value to less than 5% through the Taguchi method and the random shuffle method, which we introduce herein, and then successfully transfers the network, which CAE data analysis has predicted to the actual machine for verification with the use of transfer learning. This study uses a backpropagation neural network (BPNN) to train a dedicated prediction network using different, large amounts of data for training the network, which has proved fast and can predict results accurately using our optimized model.

scholarly journals Optimization on the Injection Molding Propypopylene Parameters Using Central Composite Design for Minimizing Defects

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Moh. Hartono ◽  
Pratikto ◽  
Purnomo B. Santoso ◽  
Sugiono
Keyword(s):  
Injection Molding ◽  
Central Composite Design ◽  
Injection Pressure ◽  
Holding Time ◽  
Clamping Force ◽  
Molding Process ◽  
Injection Speed ◽  
Injection Temperature ◽  
Central Composite ◽  
Plastic Products

This study aims to simultaneously forecast and investigate the optimization process characterization of the design of controlled parameters in the injection process of polypropylene molding including injection pressure combination, clamping force, injection temperature, injection speed, and holding time, and their interaction to produce qualified plastic by minimizing defects. The experimental methods used the central composite design of response surface method with five factors and a variety of levels. This method is more effective because it is an improvement on and a development from previous studies—especially those related to the plastic molding process. Additionally, it can simultaneously predict and optimize the obtaining of the highest quality plastic products as well as minimizing defects. The results are in the form of a combination of control level factors and interactions among the factors that generate the robust output of plastic products with minimum defects. Moreover, the optimum settings of the parameters provides a global solution at an injection temperature of 275°C, injection pressure of 75 bar, injection speed of 98%, clamping force of 88 tons, and a holding time of 8 seconds to generate a response to product probability defects by 0.0062. The benefit is that it can reveal the behavior and characteristics of parameter design and their interactions in the plastic injection molding process to produce qualified plastics and minimize product defects.

scholarly journals In-mold and Machine Sensing and Feature Extraction for Optimized IC-tray Manufacturing

Polymers ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1348 ◽  
Author(s):  
Shih-Chih Nian ◽  
Yung-Chih Fang ◽  
Ming-Shyan Huang
Keyword(s):  
Injection Molding ◽  
Optimal Parameter ◽  
Flow Behavior ◽  
Injection Pressure ◽  
Effective Control ◽  
Injection Molding Process ◽  
Molding Process ◽  
Operator Experience ◽  
Injection Speed ◽  
Screw Position

Injection molding is a mature technology that has been used for decades; factors including processed raw materials, molds and machines, and the processing parameters can cause significant changes in product quality. Traditionally, researchers have attempted to improve injection molding quality by controlling screw position, injection and packing pressures, and mold and barrel temperatures. However, even when high precision control is applied, the geometry of the molded part tends to vary between different shots. Therefore, further research is needed to properly understand the factors affecting the melt in each cycle so that more effective control strategies can be implemented. In the past, injection molding was a “black box”, so when based on statistical experimental methods, computer-aided simulations or operator experience, the setting of ideal process parameters was often time consuming and limited. Using advanced sensing technology, the understanding of the injection molding process is transformed into a “grey box” that reveals the physical information about the flow behavior of the molten resin in the cavity. Using the process parameter setting data provided by the machine, this study developed a scientific method for optimal parameter adjustment, analyzing and interpreting the injection speed, injection pressure, cavity pressure, and the profile of the injection screw position. In addition, the main parameters for each phase are determined separately, including injection speed/pressure during the mold filling phase, velocity-to-pressure switching point, packing pressure and time. In this study, the IC tray was taken as an example. The experimental results show that the method can effectively reduce the warpage of the IC-tray from 0.67 mm to 0.20 mm. In addition, the parameters profiles obtained by parameter optimization can be applied for continuous mass production and process monitoring.

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