scholarly journals Statistical Modeling of Industrial Process Parameters

Procedia CIRP ◽  
2015 ◽  
Vol 33 ◽  
pp. 203-208 ◽  
Author(s):  
Francesco Aggogeri ◽  
Giulio Barbato ◽  
Gianfranco Genta ◽  
Raffaello Levi
Keyword(s):  
Statistical Modeling ◽  
Process Parameters ◽  
Industrial Process

High-Pressure Die-Casting Process Modelling Using Neural Networks

2006 ◽  
pp. 182-198 ◽  
Author(s):  
M. Imad Khan ◽  
Saeid Nahavandi ◽  
Yakov Frayman
Keyword(s):  
High Pressure ◽  
Process Parameters ◽  
Current Knowledge ◽  
Die Casting ◽  
Process Condition ◽  
Industrial Process ◽  
Casting Process ◽  
High Pressure Die Casting ◽  
Die Casting Process ◽  
Pressure Die Casting

This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number of inter- and intradependent process parameters makes it difficult to obtain an accurate physical model of the HPDC process that is paramount to understanding the effects of process parameters on casting defects such as porosity. The first stage of the work was to obtain an accurate model of the die-casting process using a feed-forward multilayer perceptron (MLP) from the process condition monitoring data. The second stage of the work was to find out the effect of different process parameters on the level of porosity in castings by performing sensitivity analysis. The results obtained are in agreement with the current knowledge of the effects of different process parameters on porosity defects, demonstrating the ability of the MLP to model the die-casting process accurately.

scholarly journals ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process

2014 ◽  
Vol 16 (1) ◽  
pp. 103-109 ◽  
Author(s):  
Ivan Mihajlović ◽  
Isidora Đurić ◽  
Živan Živković
Keyword(s):  
Statistical Modeling ◽  
Fuzzy Inference ◽  
Industrial Process ◽  
Sodium Aluminate ◽  
Alumina Production ◽  
Inference System ◽  
Aluminate Solution ◽  
Leaching Process ◽  
Efficient Prediction ◽  
Degree Of Recovery

Abstract This paper presents the results of nonlinear statistical modeling of the bauxite leaching process, as part of Bayer technology for alumina production. Based on the data, collected during the year 2011 from the industrial production in the alumina factory Birač, Zvornik (Bosnia and Herzegovina), nonlinear statistical modeling of the industrial process was performed. The model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input parameters of the leaching process: content of Al2O3, SiO2 and Fe2O3 in the bauxite, as well as content of Na2Ocaustic and Al2O3 in the starting sodium aluminate solution. As the statistical modeling tool, Adaptive Network Based Fuzzy Inference System (ANFIS) was used. The model, defined by the ANFIS methodology, expressed a high fitting level and accordingly can be used for the efficient prediction of the Al2O3 degree of recovery, as a function of the process inputs under the industrial conditions.

DEVELOPMENT OF ARM MICROCONTROLLER BASED CONTROLLER FOR INDUSTRIAL PROCESS PARAMETERS

2020 ◽  
Vol 8 (2) ◽  
pp. 16
Author(s):  
A. POTE SHWETA ◽  
S. WAGH PRANALI ◽  
K. PISE TEJASWI ◽  
S. K. TILEKAR ◽  
◽  
...  
Keyword(s):  
Process Parameters ◽  
Industrial Process

Feasibility of Motion-Capture Techniques Applied to Tube Bending

2015 ◽  
Vol 651-653 ◽  
pp. 1128-1133 ◽  
Author(s):  
Enrico Simonetto ◽  
Andrea Ghiotti ◽  
Stefania Bruschi
Keyword(s):  
Motion Capture ◽  
Process Parameters ◽  
Industrial Process ◽  
Measurement Unit ◽  
Main Process ◽  
Tube Bending ◽  
New Production ◽  
Experimental Apparatus ◽  
Set Up ◽  
Capture Techniques

Tube bending is one of the most relevant manufacturing processes for the production of structural elements, but it suffers from the problem of springback that requires the tuning of the process parameters at every launch of new production batches. Off-line optimization approaches can be found in literature, but they often require complex characterization of the material properties or the application of approaches based on numerical simulation analyses. So, the development of new and more flexible on-line approaches to measure and correct the springback is crucial especially for highly automated machines as for example the tube benders. The paper presents a new measurement approach, based on the application of motion-capture techniques, to provide real-time measurements of the bent tube orientation, in order to decrease the time for the set-up of the main process parameters. A new methodology as well as a new experimental apparatus for the in-line monitoring of the tube springback is presented, as well as the evaluation of its accuracy when applied to the industrial process. An Inertial Measurement Unit (IMU) is linked to the tube during bending and the measurements from three gyroscopes and three accelerometers are used to perform the computation of the tube orientation in the 3D space. The proposed approach appeared promising for the evaluation of the springback through the measurement of the final angular configuration reached after bending.

Modeling Difficult-to-Measure Process Parameters Based on Intrinsic Mode Functions Frequency Spectral Features of Mechanical Vibration and Acoustical Signals

2014 ◽  
Vol 989-994 ◽  
pp. 3671-3674 ◽  
Author(s):  
Jian Tang ◽  
Zhuo Liu ◽  
Yong Jian Wu ◽  
Li Jie Zhao
Keyword(s):  
Process Parameters ◽  
Mechanical Vibration ◽  
Feature Selection Method ◽  
Industrial Process ◽  
Ensemble Empirical Mode Decomposition ◽  
Spectral Features ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
Mechanical Devices ◽  
Acoustical Signals

Heavy mechanical devices of complex industrial process produce soundly mechanical vibration and acoustical signals. Some difficult-to-measure key process parameters have direct relationship with these signals. A newly ensemble empirical mode decomposition (EEMD), Fast Fourier Transform (FFT), Mutual information (MI), and Kernel partial least squares (KPLS) based modeling approach is proposed to measure these process parameters. At first, different scale intrinsic mode functions (IMFs) of mechanical vibration and acoustical signals are obtained using EEMD technology. Then, FFT transforms these multi-scale IMFs into frequency domain, and MI based feature selection method selects interesting frequency spectral features. Finally, KPLS constructs the final soft sensor models using the selected features. Experimental results based on vibration and acoustical signals of ball mill demonstrate this approach is more effective than other exist multi-scale decomposition based methods.

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