Using Stacked Auto-Encoder and Bi-Directional LSTM For Batch Process Quality Prediction

2021 ◽  
Vol 54 (4) ◽  
pp. 144-151
Author(s):  
Jiakang Qi ◽  
Na Luo
Keyword(s):  
Batch Process ◽  
Process Quality ◽  
Quality Prediction

Between-Mode Quality Analysis Based Multimode Batch Process Quality Prediction

2014 ◽  
Vol 53 (40) ◽  
pp. 15629-15638 ◽  
Author(s):  
Luping Zhao ◽  
Chunhui Zhao ◽  
Furong Gao
Keyword(s):  
Batch Process ◽  
Process Quality ◽  
Quality Analysis ◽  
Quality Prediction

Improved Batch Process Monitoring and Quality Prediction Based on Multiphase Statistical Analysis

2008 ◽  
Vol 47 (3) ◽  
pp. 835-849 ◽  
Author(s):  
Chunhui Zhao ◽  
Fuli Wang ◽  
Zhizhong Mao ◽  
Ningyun Lu ◽  
Mingxing Jia
Keyword(s):  
Statistical Analysis ◽  
Process Monitoring ◽  
Batch Process ◽  
Quality Prediction ◽  
Batch Process Monitoring

Quality Prediction Based on the OHIF Elman Neural Network for Key Process

2011 ◽  
Vol 271-273 ◽  
pp. 713-718
Author(s):  
Jie Yang ◽  
Gui Xiong Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Process Quality ◽  
Quality Prediction ◽  
Static System ◽  
Quality Model ◽  
Elman Neural Network ◽  
Prediction And Control ◽  
Hidden Layer ◽  
Major Factors

Quality prediction and control methods are crucial in acquiring safe and reliable operation in process quality control. A hierarchical multiple criteria decision model is established for the key process and the weight matrix method stratified is discussed, and then KPCA is used to eliminate minor factors and to extract major factors among so many quality variables. Considering The standard Elman neural network model only effective for the low-level static system, then a new OHIF Elman is proposed in this paper, three different feedback factor are introduced into the hidden layer, associated layer, and output layer of the Elman neural network. In order to coordinate the efficiency of prediction accuracy and prediction, LM-CGD mixed algorithm is used for training the network model. The simulation and experiment results show the quality model can effectively predict the characteristic values of process quality, and it also can identify abnormal change pattern and enhance process control accuracy.

Research on Quality Control Architecture for Multistage Machining Processes in Small Batch Mode

2011 ◽  
Vol 130-134 ◽  
pp. 2573-2576
Author(s):  
Yan Wang ◽  
Ping Yu Jiang
Keyword(s):  
Quality Control ◽  
Process Quality ◽  
Propagation Model ◽  
Quality Data ◽  
Quality Prediction ◽  
Machining Processes ◽  
Batch Mode ◽  
Online Quality Control ◽  
Variation Propagation ◽  
Key Enabling Technologies

This paper presents a type of architecture of multistage machining processes in small batch mode, named Small-batch Quality Control System (SQCS), through analyzing various process quality control methods. The SQCS integrates complex network, workpiece variation propagation model and process quality prediction. And then, the three key enabling technologies are discussed in detail. Sensor network could be used to acquire real-time quality data, which include workpieces’ physical and dimensional information. Based on the above mentioned ideas, a general model of stage flow in small batch mode is constructed in order to realize process-driven online quality control and improve product machining quality.

Material procedure quality forecast based on genetic BP neural network

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740080 ◽  
Author(s):  
Bao-Hua Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
High Accuracy ◽  
Process Quality ◽  
Quality Prediction ◽  
Fast Convergence Rate ◽  
Material Process ◽  
Quality Forecast

Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

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