Quality Prediction and Control of Injection Molding Process using Multistage MWGRNN Method

2006 ◽  
Author(s):  
Xiao-ping Guo ◽  
Fu-li Wang ◽  
Shu Wang
Keyword(s):  
Injection Molding ◽  
Injection Molding Process ◽  
Quality Prediction ◽  
Molding Process ◽  
Prediction And Control ◽  
And Control

Reduction of Rejection Rate for High Gloss Plastics Product Using Six Sigma Method

2014 ◽  
Vol 606 ◽  
pp. 141-145
Author(s):  
Che Ku Abdullah Che Ku Kairulazam ◽  
M.I. Hussain ◽  
Zuraidah Mohd Zain ◽  
Nabilah A. Lutpi
Keyword(s):  
Injection Molding ◽  
Six Sigma ◽  
Rejection Rate ◽  
Injection Molding Process ◽  
Molding Process ◽  
Sigma Level ◽  
Potential Factors ◽  
High Rejection Rate ◽  
And Control ◽  
Plastic Injection

High gloss plastics part in injection molding industries were widely used in Malaysia. However the high rejection rate in this industries were major problem affecting the economic aspects. Therefore this paper presents an approach of implementing six sigma method to reduce the rejection rate in a plastic injection molding process for high gloss plastics part. Define, Measure, Analyze Improve and Control (DMAIC) methodology was applied as basis of the study. By using current process, the average of rejection is 40.6% and the aim of this study is to reduce the rejection rate to less than 10 % . All potential factors were taken into account to identify the significant factors. The improvement process was made base on the analysis output. This study was successful with increment in sigma level from 1.74 σ to 3.00 σ. .

scholarly journals Intelligent Injection Molding on Sensing, Optimization, and Control

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Peng Zhao ◽  
Jianfeng Zhang ◽  
Zhengyang Dong ◽  
Junye Huang ◽  
Hongwei Zhou ◽  
...  
Keyword(s):  
Injection Molding ◽  
Control Strategies ◽  
Research Field ◽  
Future Research ◽  
Injection Molding Process ◽  
Molding Process ◽  
Geometric Precision ◽  
Optimization And Control ◽  
Active Research ◽  
And Control

Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

scholarly journals Design of Multimodel based MPC and IMC control schemes applied to injection molding machine

2014 ◽  
Vol 3 (2) ◽  
pp. 82
Author(s):  
Kanaga Lakshmi ◽  
D. Manamalli ◽  
M. Mohamed Rafiq
Keyword(s):  
Injection Molding ◽  
Temperature Control ◽  
Weighted Average ◽  
Injection Molding Process ◽  
Monitoring And Control ◽  
Molding Machine ◽  
Molding Process ◽  
Melt Temperature ◽  
Injection Molding Machine ◽  
And Control

Good control of plastic melt temperature for injection molding is very important in reducing operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and set points of barrel temperature also depend on the precise monitoring and control of plastic melt temperature. Motivated by the practical temperature control of injection molding, this paper proposes MPC and IMC based control scheme. A robust system identification and control methodology is developed which uses canonical varieties analysis for identification and model predictive control for regulation. The injection molding process consists of three zones and the mathematical model for each of the zone is different. The control output for each zone controller is assigned a weight based on the computed probability of each model and the resulting action is the weighted average of the control moves of the individual zone controllers. Keywords: Injection-Molding Machine (IMM), IMC Control, Temperature Control.

Processability Testing of Injection Molding Rubber Compounds

1984 ◽  
Vol 57 (4) ◽  
pp. 826-842 ◽  
Author(s):  
John A. Sezna ◽  
P. J. DiMauro
Keyword(s):  
Injection Molding ◽  
Injection Pressure ◽  
Mold Temperature ◽  
Injection Molding Process ◽  
Capillary Rheometer ◽  
Excellent Correlation ◽  
Molding Process ◽  
Rubber Compounds ◽  
Using Data ◽  
And Control

Abstract A simple model of the injection molding process has been constructed using data from a capillary rheometer (MPT) and the Oscillating Disk Rheometer (ODR). For an NR and an SBR compound, the model had an excellent correlation with injection molding trials. The model successfully predicted the effects of adjusting injection pressure, mold temperature, and barrel temperature on injection times and scorch conditions. Such a model enables an injection molder to predict the effect of adjusting molding conditions, optimize his process for a given mold and compound, and control processability of his compounds batch-to-batch.

Sign in / Sign up

Export Citation Format

Share Document