High-speed channels and statistical analysis

2014 ◽  
Author(s):  
Tzong-Lin Wu ◽  
Jose Hejase
Keyword(s):  
Statistical Analysis ◽  
High Speed

scholarly journals Flank Wear Modelling in High Speed Hard Milling of AISI D2 Steel

2020 ◽  
Vol 5 (2) ◽  
pp. 50-54
Author(s):  
Muataz Al Hazza ◽  
Khadijah Muhammad
Keyword(s):  
Statistical Analysis ◽  
Wear Rate ◽  
Taguchi Method ◽  
Feed Rate ◽  
High Speed ◽  
Flank Wear ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
High Speed Machining ◽  
Aisi D2

High speed machining has many advantages in reducing time to the market by increasing the material removal rate. However, final surface quality is one of the main challenges for manufacturers in high speed machining due to the increasing of flank wear rate. In high speed machining, the cutting zone is under high pressure associated with high temperature that lead to increasing of the flank wear rate in which affect the final quality of the machined surface. Therefore, one of the main concerns to the manufacturer is to predict the flank wear to estimate and predict the surface roughness as one of the main outputs of the machining processes. The aim of this study is to determine experimentally the optimum cutting parameters: depth of cut, cutting speed (Vc) and feed rate (f) that maintaining low flank wear (Vb). Taguchi method has been applied in this experiment. The Taguchi method has been universally used in engineering analysis.  JMP statistical analysis software is used to analyse statically the development of flank wear rate during high speed milling of hardened steel AISI D2 to 60 HRD. The experiment was conducted in the following boundaries: cutting speed 200-400 m/min, feed rate of 0.01-0.05 mm/tooth and depth of cut of 0.1-0.2 mm. Analysis of variance ANOVA was conducted as one of important tool for statistical analysis. The result showed that cutting speed is the most influential input factors with 70.04% contribution on flank wear.

scholarly journals Implementation of experimental design methodology in preparation and characterization of zolmitriptan loaded chitosan nanoparticles

2017 ◽  
Vol 6 (3) ◽  
pp. 16-22 ◽  
Author(s):  
Satish K. Mandlik ◽  
Nisharani S. Ranpise
Keyword(s):  
Statistical Analysis ◽  
Factorial Design ◽  
High Speed ◽  
Chitosan Nanoparticles ◽  
Spherical Shape ◽  
Entrapment Efficiency ◽  
Pharmaceutical Journal ◽  
Optimization Method ◽  
Fickian Diffusion ◽  
Contour Plots

The present study investigated the implementation of 32 factorial design of experiment and statistical analysis for the optimization of chitosan nanoparticles containing zolmitriptan an antimigraine drug. The influence of chitosan concentration (X1) and sodium tripoly phosphate (X2) on responses namely nanoparticle size (Y1), and entrapment efficiency (Y2), was studied. As per design, nine runs of nanoparticles were prepared by modified ionic gelation method using high speed vortex mixing. The particle size was found in the range of 151-880 nm and entrapment efficiency was 72.3-81.2%. A statistical analysis was performed using licensed design expert software V.8.0 with respect to ANOVA, regression analysis. The contour plots and response surface plots showed visual representation of relationship between the experimental responses and the set of independent variables. Regression model equations were validated by a numerical and graphical optimization method. Further, optimized drug loaded nanoparticles showed +23.7mV zeta potential indicating storage stability, electron micrograph reflects spherical shape and mixed type of drug release followed by Fickian diffusion (n=0.266) was observed. Thus, using systematic factorial design approach, desirable goals can be achieved in shortest possible time with lesser number of experiments which was proven to be an effective tool in quality by design.Mandlik and Ranpise, International Current Pharmaceutical Journal, February 2017, 6(3): 16-22http://www.icpjonline.com/documents/Vol6Issue3/01.pdf

THE HIGH-SPEED MILITARY TRACKED VEHICLE MAINTENACE SYSTEM - MODIFICATION IS NEEDED

2010 ◽  
Vol 158 (4) ◽  
pp. 71-83
Author(s):  
Adam DRYHUSZ ◽  
Kazimierz KOWALSKI
Keyword(s):  
Statistical Analysis ◽  
Management System ◽  
High Speed ◽  
Maintenance Management ◽  
Tracked Vehicle ◽  
Tracked Vehicles ◽  
Maintenance Activity ◽  
Maintenance System ◽  
Original Interpretation

The maintenance system of high-speed military tracked vehicles and the graphic original interpretation of maintenance activity (mainly maintenance) are described. A modification of the maintenance system of the above-mentioned vehicles based on dependability-oriented maintenance (Reliability Cantered Maintenance – RCM) is proposed. Additionally, the use of the statistical analysis of maintenance cases and the development of Computerised Maintenance Management System – CMMC are proposed as well.

Statistical analysis of sand grain/bed collision process recorded by high-speed digital camera

Sedimentology ◽  
2008 ◽  
Vol 55 (2) ◽  
pp. 461-470 ◽  
Author(s):  
DAWEI WANG ◽  
YUAN WANG ◽  
BIN YANG ◽  
WEI ZHANG
Keyword(s):  
Statistical Analysis ◽  
High Speed ◽  
Digital Camera ◽  
Collision Process

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Mahmoud Hassan ◽  
Ahmad Sadek ◽  
M. H. Attia ◽  
Vincent Thomson
Keyword(s):  
Statistical Analysis ◽  
Condition Monitoring ◽  
High Speed ◽  
Threshold Level ◽  
Processing Technique ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
Important Principle ◽  
Tool Condition ◽  
Cutting Processes

In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis (LDA) model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.

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