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.

Robust Parameter Design of the Precision Injection Molding Process

1994 ◽  
Author(s):  
Sornkrit Leartcheongchowasak ◽  
Merwan Mehta ◽  
Hamid Al-Kadi ◽  
Keith Sequeira ◽  
Brian Snow ◽  
...  
Keyword(s):  
Injection Molding ◽  
Taguchi Methods ◽  
Injection Molding Process ◽  
Robust Parameter Design ◽  
Molding Process ◽  
Product Lines ◽  
Molded Parts ◽  
Robust Parameter ◽  
Independent Process ◽  
The Right

Abstract The most important problem, causing defective parts, in the injection molding process, is nonuniform shrinkage of molded parts. This leads to an iterative trial-and-error cycles of modification of mold cavity and core to arrive at the right dimensional size required which can occasionally to complete retooling. For this process, there are many factors that can be thrown out of control. Using the traditional scientific approach, engineers have longed to understand the mechanics of the process to control it, with limited success. In this paper, a design of experiments setup, using the Taguchi Methods, was done to reduce the nonuniform shrinkage. The company where the experiment was carried out is a precision parts molder for their own product lines. By using the internal experts from the company, a list of independent process parameters with no interactions which were thought the most responsible for dimensional size were listed. As there were 13 such parameters, it was decided to use the L27 orthogonal array. The optimum value that the company experts thought would produce the right part were used as the settings for the initial experiment. The 27 experiments were then performed, allowing sufficient time to let the machine stabilized between the experiments. The S/N ratio calculation for 27 experiments was explained. Next the calculations for the percentage that each parameter contributes to the dimension was determined. Finally, a confirmation experiment was performed to verify the results.

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.

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 Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model

Polymers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 3297
Author(s):  
Jinsu Gim ◽  
Byungohk Rhee
Keyword(s):  
Injection Molding ◽  
Pressure Profile ◽  
Polymer Materials ◽  
Quality Analysis ◽  
Injection Molding Process ◽  
Cavity Pressure ◽  
Molding Process ◽  
Part Quality ◽  
Analysis Methodology ◽  
Molding Condition

The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.

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.

Quantitative Study of Shrinkage and Warpage Behavior for Microcellular and Conventional Injection Molding

Materials ◽  
2005 ◽  
Author(s):  
Adam Kramschuster ◽  
Ryan Cavitt ◽  
Don Ermer ◽  
Chris Shen ◽  
Zhongbao Chen ◽  
...  
Keyword(s):  
Injection Molding ◽  
Hold Time ◽  
Fractional Factorial ◽  
Injection Molding Process ◽  
Molding Process ◽  
Injection Speed ◽  
Microcellular Injection Molding ◽  
Molded Parts ◽  
Injection Molded ◽  
Factorial Design Of Experiments

This research investigated the effects of processing conditions on the shrinkage and warpage (S&W) behavior of a box-shaped, polypropylene part using conventional, microcellular, and microcellular co-injection molding. Three sets of 26-1 fractional factorial design of experiments (DOE) were employed to perform the experiments and proper statistical theory was used to analyze the data. After the injection molding process reached steady state, molded samples were collected and measured using an optical coordinate measurement machine (OCMM), which had been evaluated using a proper repeatability and reproducibility (R&R) measurement study. By analyzing the statistically significant main and two-factor interaction effects, the results show that the supercritical fluid (SCF) content (nitrogen in this case, in terms of SCF dosage time) and the injection speed affect the S&W of microcellular injection and microcellular co-injection molded parts the most, whereas pack/hold pressure and pack/hold time have the most significant effect on the S&W of conventional injection molded parts. Also, this study quantitatively showed that, within the processing range studied, a reduction in the S&W could be achieved with the microcellular injection molding and micro- cellular co-injection molding processes.

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