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

Analyzing processing quality of machine tools via processed product: Example of ball valve processing machine

2020 ◽  
Vol 234 (4) ◽  
pp. 331-341 ◽  
Author(s):  
Kuen-Suan Chen ◽  
Der-Fa Chen ◽  
Ming-Chieh Huang ◽  
Tsang-Chuan Chang
Keyword(s):  
Machine Tools ◽  
Process Capability ◽  
Quality Characteristics ◽  
Process Quality ◽  
Quality Analysis ◽  
Process Capability Indices ◽  
Processing Quality ◽  
Accuracy And Precision ◽  
Capability Indices

Machine tools are fundamental equipment in industrial production, and their processing quality exerts a direct impact on the quality of the component product that they process. Thus, machine tool manufacturers develop various machine tools depending on market needs and processing functions, and the processed component products generally possess multiple smaller-the-better, larger-the-better, and nominal-the-best quality characteristics at the same time. For this reason, this study employed the widely used process capability indices, [Formula: see text], [Formula: see text], and [Formula: see text] to develop a model that can evaluate the process quality of component products and analyze the processing quality of various machine tools. We first converted the process capability indices into functions of the accuracy and precision indices and constructed a multi-characteristic quality analysis chart that can identify the reason for poor process quality in a quality characteristic. Furthermore, considering the fact that the process capability indices can only be estimated, which may lead to misjudgment in the evaluation of process quality, we derived the [Formula: see text] upper confidence limits of indices and the coordinates formed by the corresponding accuracy and precision indices. Manufacturers can then evaluate the process quality levels of the quality characteristics based on where the coordinates falls in the multi-characteristic quality analysis chart. This can more reliably assist manufacturers in monitoring the processing quality of their machine tools and providing feedback to the machine tool manufacturers for machine improvement.

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.

scholarly journals PROCESS QUALITY ANALYSIS AND MONITORING METHODOLOGY FOR ROLLER BEARINGS CAGES STAMPING

2012 ◽  
Vol XXI (XI), 2012/1 (1) ◽  
Author(s):  
Ripanu Marius-Ionut ◽  
Nagiţ Gheorghe ◽  
Merticaru Vasile ◽  
Huşanu Valerică ◽  
Iacob-Strugaru Sorin-Claudiu
Keyword(s):  
Process Quality ◽  
Quality Analysis ◽  
Roller Bearings
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