Quality analysis and prediction for multi-phase multi-mode injection molding processes

2018 ◽  
Author(s):  
Qing Yuan ◽  
Luping Zhao ◽  
Shu Wang ◽  
Yuqing Chang ◽  
Fuli Wang
Keyword(s):  
Injection Molding ◽  
Quality Analysis ◽  
Multi Phase ◽  
Multi Mode

scholarly journals Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1321
Author(s):  
Luping Zhao ◽  
Xin Huang ◽  
Hao Yu
Keyword(s):  
Process Monitoring ◽  
Single Mode ◽  
Batch Processes ◽  
Quality Analysis ◽  
Quality Prediction ◽  
Monitoring Strategy ◽  
Related Information ◽  
Multi Phase ◽  
Different Characteristics ◽  
Multi Mode

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.

scholarly journals Quality Analysis and Prediction for Start-up Process of Injection Molding Processes

2018 ◽  
Vol 51 (18) ◽  
pp. 233-238 ◽  
Author(s):  
Mingjun Zou ◽  
Luping Zhao ◽  
Shu Wang ◽  
Yuqing Chang ◽  
Fuli Wang
Keyword(s):  
Injection Molding ◽  
Quality Analysis ◽  
Start Up

A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

2019 ◽  
Vol 233 (16) ◽  
pp. 6019-6031
Author(s):  
Jing Zhang ◽  
Wu Yu ◽  
Xiangju Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Model Simulation ◽  
Particle Swarm ◽  
Planning Model ◽  
Swarm Optimization ◽  
Multi Phase ◽  
Multi Mode

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

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.

A Case Study on Data Quality, Privacy, and Entity Resolution

2014 ◽  
pp. 66-87
Author(s):  
William Decker ◽  
Fan Liu ◽  
John Talburt ◽  
Pei Wang ◽  
Ningning Wu
Keyword(s):  
Data Quality ◽  
Identity Management ◽  
Entity Resolution ◽  
Quality Analysis ◽  
State Education ◽  
Resolution System ◽  
Resolution Rule ◽  
Rule Sets ◽  
Education Agency ◽  
Multi Phase

This chapter presents ongoing research conducted through collaboration between the University of Arkansas at Little Rock and the Arkansas Department of Education to develop an entity resolution and identity management system. The process includes a multi-phase approach consisting of data-quality analysis, selection of entity-identity attributes for entity resolution, development of a truth-set, and implementation and benchmarking of an entity-resolution rule set using the open source entity-resolution system named OYSTER. The research is the first known of its kind to evaluate privacy-enhancing, entity-resolution rule sets in a state education agency.

scholarly journals Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 43
Author(s):  
Luping Zhao ◽  
Xin Huang
Keyword(s):  
Regression Model ◽  
Layer Structure ◽  
Batch Process ◽  
Batch Processes ◽  
Quality Prediction ◽  
Two Dimensional ◽  
Batch Process Monitoring ◽  
Multi Phase ◽  
The Relationship ◽  
Multi Mode

In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document