Study on the life distribution of microdrills

2002 ◽  
Vol 216 (3) ◽  
pp. 301-305 ◽  
Author(s):  
Z Yang ◽  
Y Chen ◽  
Y Yang
Keyword(s):  
Distribution Function ◽  
Normal Distribution ◽  
Reliability Function ◽  
Test Methods ◽  
Life Distribution ◽  
Machining Processes ◽  
Experimental Conditions ◽  
Distribution Parameters ◽  
Log Normal ◽  
Log Normal Distribution

In this paper, the life distribution of microdrills has been studied experimentally. The fit and test methods of the life distribution function of microdrills are described. Under the experimental conditions of the present work, it is concluded that the life of microdrills follows the log-normal distribution and the distribution parameters are different for various workpiece materials. The life distribution function and reliability function of microdrills from the present work provide a base for correct changing of drills in automatic machining processes.

scholarly journals Peculiarities of data processing for optical measurements of disperse parameters of bimodal media

2019 ◽  
Vol 43 (4) ◽  
pp. 692-698 ◽  
Author(s):  
A.A. Zhirnov ◽  
O.B. Kudrjashova
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Distribution Function ◽  
Particulate Matter ◽  
Particle Size Distribution ◽  
Normal Distribution ◽  
Size Distribution ◽  
A Priori ◽  
Log Normal ◽  
Log Normal Distribution

This study is focused on enhancing the informativity of optical measurement techniques for particulate matter. The problem is that the description of particulate matter with bimodal and multimodal distributions by an a priori defined analytical function of particle size distribution (for example, a log-normal distribution) is not accurate enough. Here, we explore if experimental data can be approximated by a multivariable function of particle size distribution instead of using the a priori defined log-normal distribution. For the comparison of the approximation results, experiments are conducted on standard samples with granulometric compositions OGS-01LM and OGS-08LM separately and jointly in a mix. The experimental data are recorded by a high-selectivity turbidimetric technique in water suspensions of these samples. The purpose of this study is to present the measurement results as a distribution function that enables one to identify more accurately the particle-size distribution profile and the corresponding disperse characteristics of the aerosol in question when measuring parameters of disperse media by optical techniques. The main objective of this work is to develop, implement and verify a search algorithm for the particle-size distribution function by way of a multi-parameter function. We show that the solution to the problem proposed herein is more universal because it allows slow and fast processes in suspensions and aerosols to be examined with a lower error. The algorithm can be applied to the problems which are based on solving first-kind Fredholm equations.

scholarly journals The aerosol distribution in Europe derived with the Community Multiscale Air Quality (CMAQ) model: comparison to near surface in situ and sunphotometer measurements

2008 ◽  
Vol 8 (1) ◽  
pp. 1457-1503 ◽  
Author(s):  
V. Matthias
Keyword(s):  
Distribution Function ◽  
Air Quality ◽  
Normal Distribution ◽  
Distribution Functions ◽  
Near Surface ◽  
Aerosol Mass ◽  
Aerosol Distribution ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Distribution In Europe

Abstract. The aerosol distribution in Europe was simulated with the Community Multiscale Air Quality (CMAQ) model system for the years 2000 and 2001. The results were compared with daily averages of PM10 measurements taken in the framework of EMEP and with aerosol optical depth (AOD) values measured within Aeronet. The modelled total aerosol mass is typically about 30–60% lower than the corresponding measurements. However a comparison of the chemical composition of the aerosol revealed a considerably better agreement between the modelled and the measured aerosol components for ammonium, nitrate and sulfate, which are on average only 15–20% underestimated. Sligthly worse agreement was determined for sea salt, that was only avaliable at two sites. The largest discrepancies result from the aerosol mass which was not chemically specified by the measurements. The agreement between measurements and model is better in winter than in summer. The modelled organic aerosol mass is higher in summer than in winter but it is significantly underestimated by the model. This could be the main reason for the discrepancies between measurements and model results. The probability distribution function of the PM10 measurements follows a log-normal distribution at most sites. The model is only able to reproduce this distribution function at non-coastal low altitude stations. The AOD derived from the model results is 20–70% lower than the values observed within Aeronet. This is mainly attributed to the missing aerosol mass in the model. The day-to-day variability of the AOD and the log-normal distribution functions are quite well reproduced by the model. The seasonality on the other hand is underestimated by the model results because better agreement is achieved in winter.

scholarly journals The aerosol distribution in Europe derived with the Community Multiscale Air Quality (CMAQ) model: comparison to near surface in situ and sunphotometer measurements

2008 ◽  
Vol 8 (17) ◽  
pp. 5077-5097 ◽  
Author(s):  
V. Matthias
Keyword(s):  
Distribution Function ◽  
Air Quality ◽  
Normal Distribution ◽  
The Other ◽  
Near Surface ◽  
Aerosol Mass ◽  
Aerosol Distribution ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Distribution In Europe

Abstract. The aerosol distribution in Europe was simulated with the Community Multiscale Air Quality (CMAQ) model system version 4.5 for the years 2000 and 2001. The results were compared with daily averages of PM10 measurements taken in the framework of EMEP and with aerosol optical depth (AOD) values measured within AERONET. The modelled total aerosol mass is typically about 30–60% lower than the corresponding measurements. However a comparison of the chemical composition of the aerosol revealed a considerably better agreement between the modelled and the measured aerosol components for ammonium, nitrate and sulfate, which are on average only 15–20% underestimated. Sligthly worse agreement was determined for sea salt, that was only avaliable at two sites. The largest discrepancies result from the aerosol mass which was not chemically specified by the measurements. The agreement between measurements and model is better in winter than in summer. The modelled organic aerosol mass is higher in summer than in winter but it is significantly underestimated by the model. This could be one of the main reasons for the discrepancies between measurements and model results. The other is that primary coarse particles are underestimated in the emissions. The probability distribution function of the PM10 measurements follows a log-normal distribution at most sites. The model is only able to reproduce this distribution function at non-coastal low altitude stations. The AOD derived from the model results is 20–70% lower than the values observed within AERONET. This is mainly attributed to the missing aerosol mass in the model. The day-to-day variability of the AOD and the log-normal distribution functions are quite well reproduced by the model. The seasonality on the other hand is underestimated by the model results because better agreement is achieved in winter.

Estimation of thermal time model parameters for seed germination in 15 species: the importance of distribution function

2021 ◽  
pp. 1-8
Author(s):  
Dali Chen ◽  
Xianglai Chen ◽  
Jingjing Wang ◽  
Zuxin Zhang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Distribution Function ◽  
Seed Germination ◽  
Normal Distribution ◽  
Distribution Functions ◽  
Thermal Time ◽  
Logistic Distribution ◽  
Model Parameters ◽  
Time Model ◽  
Log Normal ◽  
Log Normal Distribution

Abstract Thermal time models have been widely applied to predict temperature requirements for seed germination. Generally, a log-normal distribution for thermal time [θT(g)] is used in such models at suboptimal temperatures to examine the variation in time to germination arising from variation in θT(g) within a seed population. Recently, additional distribution functions have been used in thermal time models to predict seed germination dynamics. However, the most suitable kind of the distribution function to use in thermal time models, especially at suboptimal temperatures, has not been determined. Five distributions (log-normal, Gumbel, logistic, Weibull and log-logistic) were used in thermal time models over a range of temperatures to fit the germination data for 15 species. The results showed that a more flexible model with the log-logistic distribution, rather than the log-normal distribution, provided the best explanation of θT(g) variation in 13 species at suboptimal temperatures. Thus, at least at suboptimal temperatures, the log-logistic distribution is an appropriate candidate among the five distributions used in this study. Therefore, the distribution of parameters [θT(g)] should be considered when using thermal time models to prevent large deviations; furthermore, an appropriate equation should be selected before using such a model to make predictions.

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