Optimal process variance under Taguchi loss

1995 ◽  
Vol 12 (9) ◽  
pp. 14-29 ◽  
Author(s):  
Samar K. Mukhopadhyay ◽  
Debopam Chakraborty
Keyword(s):  
Optimal Process ◽  
Process Variance

Sensitivity Analysis and Selection of Check Index of Signal Intersection Simulation Model Based on VISSIM

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Mengda Zhang ◽  
Chenjing Zhou ◽  
Tian-tian Zhang ◽  
Yan Han
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Model ◽  
Optimal Process ◽  
Parameter Calibration ◽  
Index Set ◽  
The Core ◽  
Core Module ◽  
Simulation Parameter ◽  
Simulation Parameters ◽  
Selection Of

Selecting check index quantitatively is the core of the calibration of micro traffic simulation parameters at signal intersection. Five indexes in the node (intersection) module of VISSIM were selected as the check index set. Twelve simulation parameters in the core module were selected as the simulation parameters set. Optimal process of parameter calibration was proposed and model of the intersection of Huangcun west street and Xinghua street in Beijing was built in VISSIM to verify it. The sensitivity analysis between each check index and simulation parameter in their own set was conducted respectively. Sensitive parameter sets of different check indices were obtained and compared. The results show that different indexes have different size of set, and average vehicle delay's is maximum, so it's necessary to select index quantitatively. The results can provide references for scientific selection of the check indexes and improve the study efficiency of parameter calibration.

scholarly journals A Bayesian Control Chart for Monitoring Process Variance

2021 ◽  
Vol 11 (6) ◽  
pp. 2729
Author(s):  
Chien-Hua Lin ◽  
Ming-Che Lu ◽  
Su-Fen Yang ◽  
Ming-Yung Lee
Keyword(s):  
Service Quality ◽  
Control Chart ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Process ◽  
Time Varying ◽  
Exponential Process ◽  
Free Process ◽  
Process Variance

Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency of service quality is an important part of the automation process. The quality control methods widely used in the manufacturing industry can provide service quality measurement and service process monitoring. In particular, the control chart as an online monitoring technique can be used to quickly detect whether a service process is out of control. However, the control of the service process is more difficult than that of the manufacturing process because the variability of the service process comes from widespread and complex factors. First of all, the distribution of the service process is usually non-normal or unknown. Moreover, the skewness of the process distribution can be time-varying, even if the process is in control. In this study, a Bayesian procedure is applied to construct a Phase II exponential weighted moving average (EWMA) control chart for monitoring the variance of a distribution-free process. We explore the sampling properties of the new monitoring statistic, which is suitable for monitoring the time-varying process distribution. The average run lengths (ARLs) of the proposed Bayesian EWMA variance chart are calculated, and they show that the chart performs well. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect any shift of a process variance. Finally, a numerical example of bank service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of the process control.

scholarly journals Mechanical properties optimization of the steering column based on multi-objective optimization design

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668294 ◽  
Author(s):  
Si Chen ◽  
Zhaohui Wang ◽  
Mi Lv
Keyword(s):  
Mechanical Properties ◽  
Process Parameters ◽  
Optimization Design ◽  
Strain Difference ◽  
Optimization Method ◽  
Equivalent Strain ◽  
Multi Objective Optimization ◽  
Optimal Process ◽  
Multi Objective ◽  
Initial Results

The mechanical properties of the steering column have a significant influence on the comfort and stability of a vehicle. In order for the mechanical properties to be improved, the rotary swaging process of the steering column is studied in this article. The process parameters, including axial feed rate, hammerhead speed, and hammerhead radial reduction, are systematically analyzed and optimized based on a multi-objective optimization design. The response surface methodology and the genetic algorithm are employed for optimal process parameters to be obtained. The maximum damage value, the maximum forming load, and the equivalent strain difference obtained with the optimal process parameters are, respectively, decreased by 30.09%, 7.44%, and 57.29% compared to the initial results. The comparative results present that the quality of the steering column is improved. The torque experiments and fatigue experiments are conducted with the optimal steering column. The maximum torque is measured to be 260 NM, and the service life is measured to be 2 weeks (40 NM, 2500 times), which are, respectively, increased by 8.3% and 8.69% compared to the initial results. The above results display that the mechanical properties of the steering column are optimized to verify the feasibility of the multi-objective optimization method.

Optimal process characteristics of melting of polyisoprene

2021 ◽  
Author(s):  
K. Mohanraj ◽  
Rajakumar S. Rai ◽  
M. Suresh ◽  
B. Logapriya ◽  
S. Marichamy ◽  
...  
Keyword(s):  
Optimal Process ◽  
Process Characteristics
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