The document reports descriptive statistics for two groups ("sixtypercent" and "fiftypercent") across multiple trials. It provides the number of observations, minimum and maximum values, mean, and standard deviation for each group in each trial. One-sample t-tests are also reported comparing the mean of each group to a test value of 0 for each trial.
The multiple linear regression model aims to predict water cases produced from four predictor variables: run time, downtime, setup time, and efficiency. Preliminary analysis found run time has the highest correlation to water cases. Residual analysis showed non-constant variance, so a square root transformation of water cases was tested but did not improve the model. Further analysis is needed to develop the best-fitting multiple linear regression model.
Foundations of Statistics for Ecology and Evolution. 5. Linear ModelsAndres Lopez-Sepulcre
1. Fitting: Least Squares vs Maximum Likelihood
2. Anatomy of the Linear Model
- Interpretation and significance of parameters
- The Design Matrix
3. Discrete factors and planned comparisons
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This thesis analyzes the elastic behavior of honeycombs and auxetic materials with variations in cell geometry using finite element analysis. Models of honeycomb and auxetic structures with different cell angles and wall thicknesses were developed and analyzed in ABAQUS to determine their elastic moduli, Poisson's ratios, and stress-strain behaviors under compression loading. 3D printed prototypes of selected geometries were also experimentally tested and showed good agreement with FEA results. Key trends identified include increasing elastic modulus and Poisson's ratio with decreasing cell angle for honeycombs, and negative Poisson's ratio values for auxetic geometries.
Determination of Contact Stress Distribution in Pin Loaded Orthotropic Platestomlinson_n
This document summarizes the analysis and results of determining stress distribution in an orthotropic plate loaded by a pin. The analysis considers the plate to be orthotropic and accounts for clearance between the pin and plate as well as friction. Contact angle, stresses, and their variation with materials, friction, and clearance are determined and presented. Key conclusions are that contact angle and stresses are affected by friction and clearance.
The document presents an analytical method for determining stress distributions in pin-loaded orthotropic plates. The method assumes a rigid pin, clearance between the pin and plate, and a constant coefficient of friction. Numerical results are shown for normal, tangential and shear stresses on the cavity for different composite layups. The method can predict stresses for varying clearances and a perfectly fitting pin. Experimental validation and improvements to contact modeling are recommended.
The document reports descriptive statistics for two groups ("sixtypercent" and "fiftypercent") across multiple trials. It provides the number of observations, minimum and maximum values, mean, and standard deviation for each group in each trial. One-sample t-tests are also reported comparing the mean of each group to a test value of 0 for each trial.
The multiple linear regression model aims to predict water cases produced from four predictor variables: run time, downtime, setup time, and efficiency. Preliminary analysis found run time has the highest correlation to water cases. Residual analysis showed non-constant variance, so a square root transformation of water cases was tested but did not improve the model. Further analysis is needed to develop the best-fitting multiple linear regression model.
Foundations of Statistics for Ecology and Evolution. 5. Linear ModelsAndres Lopez-Sepulcre
1. Fitting: Least Squares vs Maximum Likelihood
2. Anatomy of the Linear Model
- Interpretation and significance of parameters
- The Design Matrix
3. Discrete factors and planned comparisons
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
This thesis analyzes the elastic behavior of honeycombs and auxetic materials with variations in cell geometry using finite element analysis. Models of honeycomb and auxetic structures with different cell angles and wall thicknesses were developed and analyzed in ABAQUS to determine their elastic moduli, Poisson's ratios, and stress-strain behaviors under compression loading. 3D printed prototypes of selected geometries were also experimentally tested and showed good agreement with FEA results. Key trends identified include increasing elastic modulus and Poisson's ratio with decreasing cell angle for honeycombs, and negative Poisson's ratio values for auxetic geometries.
Determination of Contact Stress Distribution in Pin Loaded Orthotropic Platestomlinson_n
This document summarizes the analysis and results of determining stress distribution in an orthotropic plate loaded by a pin. The analysis considers the plate to be orthotropic and accounts for clearance between the pin and plate as well as friction. Contact angle, stresses, and their variation with materials, friction, and clearance are determined and presented. Key conclusions are that contact angle and stresses are affected by friction and clearance.
The document presents an analytical method for determining stress distributions in pin-loaded orthotropic plates. The method assumes a rigid pin, clearance between the pin and plate, and a constant coefficient of friction. Numerical results are shown for normal, tangential and shear stresses on the cavity for different composite layups. The method can predict stresses for varying clearances and a perfectly fitting pin. Experimental validation and improvements to contact modeling are recommended.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Correlation and Reg...J. García - Verdugo
The document discusses correlation and regression analysis. It provides an overview of key concepts like the regression coefficient, correlation coefficient, and fitted line plots. It also describes how to calculate regression using the method of least squares and how to validate factors using tools like t-tests, ANOVA, and regression. An example is shown analyzing the relationship between softening temperature measured at a supplier vs. a customer. The correlation between the two factors is calculated to be 0.834, indicating a strong positive correlation.
This master's thesis investigates the surface topography of cutting inserts using two work packages. In work package 1, three variants of uncoated inserts were analyzed and it was found that variant MSG158 had the most texture while MSG160 was smoothest. In work package 2, five coated variants were examined and variants MSG189 and MSG187 showed the highest texture. Parameters like average height and void volume were selected to characterize the topography and compare the variants. Future work involves machining tests and analyzing texture propagation between the two work packages.
The document discusses variance and standard deviation. Variance measures how dispersed or spread out values are from the mean, while standard deviation is the positive square root of variance. Standard deviation indicates the average amount of variation from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means more variation and dispersion from the mean. The coefficient of variation measures standard deviation relative to the mean and is used to compare the variability of different data sets even if the means are different.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
Presentation on the inclusive analysisRuturaj Apte
This document discusses adding a single top channel (Wt) analysis to simultaneous cross-section measurements of other channels using the AIDA technique at 8 TeV. The aim is to measure the Wt cross-section independently to reduce model dependencies and search for new physics. Preliminary plots of missing energy, invariant mass, and jet binning are shown using MC data with one electron and one muon. The motivation, significance studies comparing binning techniques, inclusion of normalization and important shape systematics, and conclusions are discussed.
The document discusses test validity and reliability. It provides formulas for calculating validity, including point biserial correlation, and shows sample calculations for item validity. Reliability is assessed using split-half reliability and Pearson product-moment correlation. Sample reliability calculations are shown for several test items. The results indicate some items have high reliability while others do not. Guidelines are provided for interpreting reliability coefficients.
The document discusses hypothesis testing to determine if districts with smaller class sizes have higher test scores. It summarizes the steps taken: 1) Estimation to calculate the difference in average test scores between districts with low vs high student-teacher ratios (STRs), 2) Hypothesis testing to determine if the difference is statistically significant by calculating a t-statistic and comparing it to a critical value, 3) Construction of a confidence interval for the difference between the means. The analysis found the difference in average test scores between low and high STR districts was statistically significant based on a t-statistic greater than the critical value.
This document discusses selective harmonic elimination pulse width modulation (SHEPWM) for multilevel inverters using a generalized Hopfield neural network. It begins with an abstract that introduces multilevel inverters, SHEPWM, and the use of a generalized Hopfield neural network for SHEPWM. It then provides more details on the background and concepts of multilevel inverters, SHEPWM, and the Fourier expansion of the staircase output voltage waveform generated by multilevel inverters. The document focuses on applying SHEPWM to eliminate lower order harmonics from the output waveform using a generalized Hopfield neural network approach.
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
1) The document investigates using electrical discharge machining (EDM) to machine aluminum foam. It aims to identify the effects of different EDM process parameters on material removal rate (MRR) and tool wear rate (TWR).
2) An experiment is conducted using an L8 Taguchi orthogonal array with four EDM parameters (duty cycle, pulse on time, gap voltage, pulse current) at two levels. MRR and TWR are measured for each experimental run.
3) Fuzzy logic is used to map MRR and TWR as input variables to productivity as the output variable. The mapping reveals maximum productivity is achieved when MRR is between 95-110 and TWR is between 100
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 T- TestJ. García - Verdugo
This document provides information about performing a T-test, including:
- The theory and requirements of a T-test to compare two sample means
- How to calculate a T-value and determine its significance based on degrees of freedom and p-value
- An example comparing the means of new and old processes using a two-sample T-test in Minitab, finding no significant difference between the means.
"When the top is not single: a theory overview from monotop to multitops" to...Rene Kotze
This document discusses potential deviations from the standard model in top quark pair production (ttbar) due to beyond standard model (BSM) physics. It summarizes that ttbar production is well measured but sensitive to BSM effects like resonant contributions from new particles that decay to top quark pairs. Non-resonant effects are also possible and can be parameterized using effective field theory operators. The document provides examples of limits set on specific BSM models like Z' bosons by the CMS experiment through analyses of the ttbar invariant mass spectrum and other observables.
This document provides an overview of linear regression techniques. It begins with introducing deterministic vs statistical relationships and simple linear regression. It then covers model evaluation, gradient descent, and polynomial regression. The document discusses bias-variance tradeoff and various regularization techniques like lasso, ridge regression and stochastic gradient descent. It concludes with discussing robust regressors that are robust to outliers in the data.
This document discusses sequence alignment and contains four sections:
1) Global alignment which finds the highest scoring alignment between entire sequences using dynamic programming.
2) Scoring matrices which generalize alignment scoring by assigning scores to individual character matches/mismatches based on biological evidence.
3) Local alignment which finds the best scoring alignment between substrings of sequences to identify conserved regions, as global alignment may miss these.
4) Ways to solve the local alignment problem efficiently in quadratic time instead of quartic time by computing alignments from each vertex in the grid.
The document provides details on hypothesis testing using OLS regression. It discusses estimating the slope (β1) and intercept (β0) coefficients, testing hypotheses about β1, and constructing confidence intervals for β1. Specifically, it shows that the test statistic for testing H0: β1 = β1,0 versus H1: β1 ≠ β1,0 is distributed as t with degrees of freedom n-2. The p-value can be used to reject or fail to reject the null hypothesis. A 95% confidence interval for β1 is constructed as the estimate ± 1.96 times the standard error of the estimate. The document provides an example using data on test scores and student-teacher ratios to illustrate
This document discusses measures of dispersion such as standard deviation and variance. It provides formulas and examples of calculating standard deviation, variance, and coefficient of variation from data sets. It also describes steps for conducting a chi-square test on frequency data, including determining the appropriate test, establishing significance level, formulating hypotheses, calculating test statistics, determining degrees of freedom, and comparing the computed statistic to critical values. An example contingency table and chi-square calculation are also provided.
01_FEA overview 2023-1 of fhtr j thrf for any.pptxRaviBabaladi2
Finite Element Analysis (FEA) is a numerical technique used to determine the behavior of complex geometries and systems. It breaks components down into finite elements in order to solve problems that cannot be solved through classical calculations. FEA provides outputs like stresses, strains, displacements and structural capacity that help evaluate a design. The FEA process involves preprocessing like creating a model and mesh, solving with applied loads and materials, and postprocessing the results. Models are simplified to reduce run time while ensuring accuracy of important features. FEA can be used to optimize designs before physical testing.
EUGM 2013 - Dragos Horváth (Labooratoire de Chemoinformatique Univ Strasbourg...ChemAxon
1) The document discusses methods for setting up similarity-driven virtual screening using various molecular similarity metrics and descriptor spaces.
2) It finds that traditional dogmas like only using Tanimoto similarity above 0.85 can be inaccurate, and recommends calibrating similarity cutoffs specifically for each target, query, and chemical space.
3) Tversky similarity with an alpha value of 0.7-0.9, which more heavily penalizes the query missing features of actives, is found to often give excellent results. The best approach is to test multiple options and calibrate for each individual virtual screening project.
Shobin John-solar pv cell utilization and chargingShobin John
In the present scenario of world is energy driven and batteries have turned into an essential part as an energy source considering the mechanical advances in electric and frameworks. Batteries are requiring recharging because of energy limitation. Recharging batteries with solar powered vitality by methods for sunlight-based cells can offer an advantageous alternative for shrewd customer hardware. In the interim, batteries can be utilized to address the discontinuity worry of photovoltaics.
The technology lead-acid battery capable of long cycle and most efficiently recycled commodity metal. Over a 99% of battery recycled in USA and Europe. Even though Li-ion and other types of battery have advantages in terms of specific energy and energy density, but selection of lead-acid battery depend on its sustainability of chemistry, completely recycled energy storage system and partially recycled metal parts [1]. In addition, that electrochemical models have been computationally complex in terms of parameter identification and constant phase element dynamics [2].
Battery charging control system play important role of stabilized power supply. The maximum power point tracking (MPPT) and pulse width modulation along with smart charging methods helps to get maximum power, intelligent utilization of energy and reduce battery charging time [3].
Battery thermal management system (BTMS) is performance and design bottle neck of many electric vehicles mechatronic and energy system. Advanced storing solar energy shift towards sustainable transportation system. Oil pumps in the electric vehicles capable to manage effective cooling system of battery and used for lubrication of various metallic bearings. This paper proposes a solar driven oil management system in electric vehicle.
In this paper discussed about (a) PV and IV characteristics of solar panel based on Simulink simulation (b) Designed a MPPT controller (Easy EDA). The generic algorithm was designed to MPPT and PWM control battery system. Compare different battery charge method. The design consists of four stages which include current booster, battery level indicator, battery charge controller and power supply unit. (c) Solar energy data log by LabVIEW interface (d) tested and optimized best PWM controlled charging method (e) implemented proposed model in oil pump test rig.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Correlation and Reg...J. García - Verdugo
The document discusses correlation and regression analysis. It provides an overview of key concepts like the regression coefficient, correlation coefficient, and fitted line plots. It also describes how to calculate regression using the method of least squares and how to validate factors using tools like t-tests, ANOVA, and regression. An example is shown analyzing the relationship between softening temperature measured at a supplier vs. a customer. The correlation between the two factors is calculated to be 0.834, indicating a strong positive correlation.
This master's thesis investigates the surface topography of cutting inserts using two work packages. In work package 1, three variants of uncoated inserts were analyzed and it was found that variant MSG158 had the most texture while MSG160 was smoothest. In work package 2, five coated variants were examined and variants MSG189 and MSG187 showed the highest texture. Parameters like average height and void volume were selected to characterize the topography and compare the variants. Future work involves machining tests and analyzing texture propagation between the two work packages.
The document discusses variance and standard deviation. Variance measures how dispersed or spread out values are from the mean, while standard deviation is the positive square root of variance. Standard deviation indicates the average amount of variation from the mean. A low standard deviation means values are close to the mean, while a high standard deviation means more variation and dispersion from the mean. The coefficient of variation measures standard deviation relative to the mean and is used to compare the variability of different data sets even if the means are different.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
Presentation on the inclusive analysisRuturaj Apte
This document discusses adding a single top channel (Wt) analysis to simultaneous cross-section measurements of other channels using the AIDA technique at 8 TeV. The aim is to measure the Wt cross-section independently to reduce model dependencies and search for new physics. Preliminary plots of missing energy, invariant mass, and jet binning are shown using MC data with one electron and one muon. The motivation, significance studies comparing binning techniques, inclusion of normalization and important shape systematics, and conclusions are discussed.
The document discusses test validity and reliability. It provides formulas for calculating validity, including point biserial correlation, and shows sample calculations for item validity. Reliability is assessed using split-half reliability and Pearson product-moment correlation. Sample reliability calculations are shown for several test items. The results indicate some items have high reliability while others do not. Guidelines are provided for interpreting reliability coefficients.
The document discusses hypothesis testing to determine if districts with smaller class sizes have higher test scores. It summarizes the steps taken: 1) Estimation to calculate the difference in average test scores between districts with low vs high student-teacher ratios (STRs), 2) Hypothesis testing to determine if the difference is statistically significant by calculating a t-statistic and comparing it to a critical value, 3) Construction of a confidence interval for the difference between the means. The analysis found the difference in average test scores between low and high STR districts was statistically significant based on a t-statistic greater than the critical value.
This document discusses selective harmonic elimination pulse width modulation (SHEPWM) for multilevel inverters using a generalized Hopfield neural network. It begins with an abstract that introduces multilevel inverters, SHEPWM, and the use of a generalized Hopfield neural network for SHEPWM. It then provides more details on the background and concepts of multilevel inverters, SHEPWM, and the Fourier expansion of the staircase output voltage waveform generated by multilevel inverters. The document focuses on applying SHEPWM to eliminate lower order harmonics from the output waveform using a generalized Hopfield neural network approach.
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
1) The document investigates using electrical discharge machining (EDM) to machine aluminum foam. It aims to identify the effects of different EDM process parameters on material removal rate (MRR) and tool wear rate (TWR).
2) An experiment is conducted using an L8 Taguchi orthogonal array with four EDM parameters (duty cycle, pulse on time, gap voltage, pulse current) at two levels. MRR and TWR are measured for each experimental run.
3) Fuzzy logic is used to map MRR and TWR as input variables to productivity as the output variable. The mapping reveals maximum productivity is achieved when MRR is between 95-110 and TWR is between 100
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 T- TestJ. García - Verdugo
This document provides information about performing a T-test, including:
- The theory and requirements of a T-test to compare two sample means
- How to calculate a T-value and determine its significance based on degrees of freedom and p-value
- An example comparing the means of new and old processes using a two-sample T-test in Minitab, finding no significant difference between the means.
"When the top is not single: a theory overview from monotop to multitops" to...Rene Kotze
This document discusses potential deviations from the standard model in top quark pair production (ttbar) due to beyond standard model (BSM) physics. It summarizes that ttbar production is well measured but sensitive to BSM effects like resonant contributions from new particles that decay to top quark pairs. Non-resonant effects are also possible and can be parameterized using effective field theory operators. The document provides examples of limits set on specific BSM models like Z' bosons by the CMS experiment through analyses of the ttbar invariant mass spectrum and other observables.
This document provides an overview of linear regression techniques. It begins with introducing deterministic vs statistical relationships and simple linear regression. It then covers model evaluation, gradient descent, and polynomial regression. The document discusses bias-variance tradeoff and various regularization techniques like lasso, ridge regression and stochastic gradient descent. It concludes with discussing robust regressors that are robust to outliers in the data.
This document discusses sequence alignment and contains four sections:
1) Global alignment which finds the highest scoring alignment between entire sequences using dynamic programming.
2) Scoring matrices which generalize alignment scoring by assigning scores to individual character matches/mismatches based on biological evidence.
3) Local alignment which finds the best scoring alignment between substrings of sequences to identify conserved regions, as global alignment may miss these.
4) Ways to solve the local alignment problem efficiently in quadratic time instead of quartic time by computing alignments from each vertex in the grid.
The document provides details on hypothesis testing using OLS regression. It discusses estimating the slope (β1) and intercept (β0) coefficients, testing hypotheses about β1, and constructing confidence intervals for β1. Specifically, it shows that the test statistic for testing H0: β1 = β1,0 versus H1: β1 ≠ β1,0 is distributed as t with degrees of freedom n-2. The p-value can be used to reject or fail to reject the null hypothesis. A 95% confidence interval for β1 is constructed as the estimate ± 1.96 times the standard error of the estimate. The document provides an example using data on test scores and student-teacher ratios to illustrate
This document discusses measures of dispersion such as standard deviation and variance. It provides formulas and examples of calculating standard deviation, variance, and coefficient of variation from data sets. It also describes steps for conducting a chi-square test on frequency data, including determining the appropriate test, establishing significance level, formulating hypotheses, calculating test statistics, determining degrees of freedom, and comparing the computed statistic to critical values. An example contingency table and chi-square calculation are also provided.
01_FEA overview 2023-1 of fhtr j thrf for any.pptxRaviBabaladi2
Finite Element Analysis (FEA) is a numerical technique used to determine the behavior of complex geometries and systems. It breaks components down into finite elements in order to solve problems that cannot be solved through classical calculations. FEA provides outputs like stresses, strains, displacements and structural capacity that help evaluate a design. The FEA process involves preprocessing like creating a model and mesh, solving with applied loads and materials, and postprocessing the results. Models are simplified to reduce run time while ensuring accuracy of important features. FEA can be used to optimize designs before physical testing.
EUGM 2013 - Dragos Horváth (Labooratoire de Chemoinformatique Univ Strasbourg...ChemAxon
1) The document discusses methods for setting up similarity-driven virtual screening using various molecular similarity metrics and descriptor spaces.
2) It finds that traditional dogmas like only using Tanimoto similarity above 0.85 can be inaccurate, and recommends calibrating similarity cutoffs specifically for each target, query, and chemical space.
3) Tversky similarity with an alpha value of 0.7-0.9, which more heavily penalizes the query missing features of actives, is found to often give excellent results. The best approach is to test multiple options and calibrate for each individual virtual screening project.
Shobin John-solar pv cell utilization and chargingShobin John
In the present scenario of world is energy driven and batteries have turned into an essential part as an energy source considering the mechanical advances in electric and frameworks. Batteries are requiring recharging because of energy limitation. Recharging batteries with solar powered vitality by methods for sunlight-based cells can offer an advantageous alternative for shrewd customer hardware. In the interim, batteries can be utilized to address the discontinuity worry of photovoltaics.
The technology lead-acid battery capable of long cycle and most efficiently recycled commodity metal. Over a 99% of battery recycled in USA and Europe. Even though Li-ion and other types of battery have advantages in terms of specific energy and energy density, but selection of lead-acid battery depend on its sustainability of chemistry, completely recycled energy storage system and partially recycled metal parts [1]. In addition, that electrochemical models have been computationally complex in terms of parameter identification and constant phase element dynamics [2].
Battery charging control system play important role of stabilized power supply. The maximum power point tracking (MPPT) and pulse width modulation along with smart charging methods helps to get maximum power, intelligent utilization of energy and reduce battery charging time [3].
Battery thermal management system (BTMS) is performance and design bottle neck of many electric vehicles mechatronic and energy system. Advanced storing solar energy shift towards sustainable transportation system. Oil pumps in the electric vehicles capable to manage effective cooling system of battery and used for lubrication of various metallic bearings. This paper proposes a solar driven oil management system in electric vehicle.
In this paper discussed about (a) PV and IV characteristics of solar panel based on Simulink simulation (b) Designed a MPPT controller (Easy EDA). The generic algorithm was designed to MPPT and PWM control battery system. Compare different battery charge method. The design consists of four stages which include current booster, battery level indicator, battery charge controller and power supply unit. (c) Solar energy data log by LabVIEW interface (d) tested and optimized best PWM controlled charging method (e) implemented proposed model in oil pump test rig.
Shobin John completed a course in FRP Composites Engineering and Manufacturing held at Högskolan In Halmstad Sweden between 2015 week 45 and 2016 week 11. The course was instructed by Carl-Johan Lindholm and Håkan Johansson of CCG Europe.
This document summarizes an experiment that used Taguchi methods to optimize diesel engine parameters to reduce NOx emissions and improve fuel economy. A single cylinder diesel engine was tested across four levels of five parameters: clearance volume, valve opening pressure, injection timing, nozzle hole area, and load torque. Testing was conducted according to an orthogonal array experimental design. Results showed that valve opening pressure, clearance volume, and injection timing had the greatest impact on NOx emissions and fuel consumption. Optimal parameter settings were identified that minimized both NOx emissions and fuel consumption. A confirmation test found good agreement between predicted and actual results.
Just nu pågår ett flertal rekryteringar till Krohne Inor. De är inne i en positiv tillväxtfas med framgångar inom
både nationella och internationella projekt. Tillsammans med koncernledningen har de satt mål för vidare
expansion av Krohne Inor så att nya krav från deras kunder kan bemötas och ge nya framgångar.
Krohne Inor är framför allt i behov av att förstärka sina teknikresurser för att exekvera avancerade projekt i
internationell miljö samtidigt som de har ett utvecklingsprogram med många produktprojekt framför sig. De
pågående rekryteringarna är ett steg för att föra företaget vidare i deras internationella expansion.
Vi ser fram mot att få träffa dig som söker en utmaning i ett teknikföretag med stor teamanda och hög dynamik
som skapar utveckling för både företaget och deras medarbetare.
This document provides a summary of chapters from a book on quality management. It discusses definitions of quality, the history and importance of quality, and various quality philosophies and frameworks. It summarizes chapters on total quality in organizations, focusing on customers, leadership and strategic planning, and developing a high performance workforce. The overall document aims to convey key concepts from each chapter in evaluating approaches to quality management.
The PESTEL analytical tool normally conducted from a Chevalier perspective (Fig ) to help plan for future direction based on macro-environmental factors. The framework consists of six main macro-environmental influences political, economic, social, technological, environmental and legal Johnson, Scholes and Whittington (2008). Ihsan (2012) mentions that it is not possible for a company to survive in the long run without knowledge of the changes in their macro-environment.
The document is a mechanical engineering student's design notebook containing various homework assignments and exercises. It includes reflections on studying the design process, exercises on generating ideas for producing electricity and moving vehicles without engines, and a homework assignment to read the ASME Code of Ethics.
This certificate certifies that the recipient has completed the Sandvik Coromant Academy Knowledge Test: Metal Cutting Technology E-learning program. The program covered fundamentals of metal cutting, application areas, choosing the right cutting tool, production economics, improving productivity and profitability, cutting data formulas, optimizing tool life, tool wear identification and remedies, and solving metal cutting problems. The program was developed by Sandvik Coromant based on production needs worldwide and aims to help customers improve profitability through improved metal cutting competence.
The document analyzes surface roughness profiles for different cutoff values of 0.8, 2.5, and 8. Tables show that as the cutoff value increases from 0.8 to 2.5 to 8, the waviness (Wa, Wq, Wz) decreases slightly while the roughness (Ra, Rq, Rz) increases slightly. Charts of the surface profiles are also provided for each of the cutoff values.
The document describes the design and optimization of an airplane bearing bracket using Inspire software. The initial design was optimized to reduce the mass by 23% while meeting the design envelope requirements and withstanding three load cases. Modifications made during optimization included allowing movement of fastener footprints and modifying cross-sectional changes. The final optimized design had a mass of 228 grams and stress levels under 100% of the yield stress. The bracket is intended to be manufactured using additive manufacturing.
The document discusses material selection for a disc clutch component in a bicycle flywheel project. It describes using Ashby's material selection method and the CES EduPack software to rank material attributes and select materials based on charts plotting hardness vs specific heat, price vs specific heat, and machinability vs price. This led to selecting cast aluminum alloy as it met desired criteria of hardness, price, heat capacity and machinability. High carbon steel and aluminum/silicon carbide composite were identified as alternative materials.
This master's thesis examines the surface topography of cutting inserts through two work packages. In work package one, uncoated inserts from three variants are analyzed to determine the best parameters for comparison and if the topography correlates with the manufacturing process. In work package two, coated inserts from five variants are studied to understand how the coating outcome relates to pre-treatment and what measurement approach is needed. Statistical analysis methods like average and standard deviation, Spearman's correlation, and ANOVA/t-tests are used to evaluate the surface roughness parameters and compare the variants. The goal is to develop an approach for Sandvik Coromant to characterize different surface textures.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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Presentation14
1. Master Thesis:
Surface topographical Analysis OF
Cutting inserts
2018-04-14
Master students in Mechanical Engineering
1
Final Presentation
Shobin & Zoelfi
2. AGENDA
Cutting inserts and region of interest
Aim of the study
Theoretical framework
Methodology
Average and Standard deviation method
Spearman’s correlation
Error bar : Average and Standard deviation method
followed by Anova and T-test
Results of work package 1 and work package 2
Conclusion for work package 1 and work package 2
3. Cutting inserts and region of
interest
3 variants in WP1
1. MSG 157
2. MSG158
3. MSG 160
1. MSG 186
2. MSG187
3. MSG189
4. MSG190
5. MSG191
Edge Rounding +Pre
treatment
Post treatmentCoating
Treatment
5variantsinWP2
4. Cutting inserts and region of interest
Region of Interest
Rake Face, it is defined as the whole upper side of the insert,
where the chips breaks
Edge Rounding (ER), the radius of the cutting edge.
All measurement had done on the rake face
20 reading for each variant.
Rake face
Flank
face
Nose
Radius
Edge
Rounding
5. Aim of the study
In Work Package one WP1 :
Which are the Parameters are important when comparing different
variants ?
Which Surface topography of the variants can correlate to the
manufacturing process?
Is there any predominant direction of the topography?
In Work Package Two WP2 :
Which parameters are important to look at when comparing to each
other?
If there is a connection can be found between the treatment prior to
coating and the outcome of the treatment after coating?
If there is any different measurement approaches to measure the
surface roughness on variants in WP 1 and WP 2 ?
6. Theoretical framework
WP1Edge Rounding +Pre
treatment
• Surface texture measurement by
using interferometer and SEM.
• Software used after the reading
MountainsMap 7, Microsoft excel.
• 3D surface texture parameter ISO
25178-2
MSG157:Edge rounding is done through blasting.
The abrasive particles have a high kinetic energy
when they hit the surface of the inserts and therefore
some WC grains can break or crack.
MSG158: ER blasted; also blasted with a finer grit
size of media, the kinetic energy of the particles is
lower and thus the blasting should not fracture any
new grains of WC.
MSG 160:ER treated with blasting the same way as
MSG157 and MSG158 but before coating, it was
polished. The polishing is done through shooting out
rubber particles covered in a fine grit abrasive
material through a nozzle
8. 1. Average and Standard deviation method
METHODS
1. Find the average and standard deviation
2. basis of the intervals and the mean
3. Then For each parameter, an interval for
good parts and for bad parts is calculated
with the coverage factor K, here we took
K=2
4. 𝐼′
𝑚𝑖𝑛 = 𝜇′
𝑖 − 𝑘𝜎′
𝑖 𝑎𝑛𝑑 𝐼′
𝑚𝑎𝑥 = 𝜇′
𝑖 +
𝑘𝜎′
𝑖
Check threshold & disjunct function
Select parameter have ´+´ve (disjunct)
significant factor
Reject Parameters Have ´-´ Si factor
𝑆 =
𝑑(𝐼′ 𝑖, 𝐼′′ 𝑖)
1
2 (𝜇′ 𝑖 + 𝜇′′ 𝑖)
Parameters - According
to ISO 25178
Significant factor
between MSG157
and MSG158
Significant Factor
between MSG158
and MSG160
Significant Factor
between MSG157
and MSG160
Sa (Arithemetic Mean
Height)
Si Factor ´-´ve
Rejected
0,20 0,05
Smc (p = 10%)(Inverse
Areal Material Ratio
0,05 0,29 0,11
Sxp (p = 50%, q =
96.5%)(Extreme Peak
Height)
Si Factor ´-´ve
Rejected
0,13 0,04
Vv (p = 10%)(Void
Volume)
0,05 0,29 0,10
Vmc (p = 10%, q =
80%)(Core Material
Volume)
0,07 0,32 0,11
Vvc (p = 10%, q = 80%)
(Core Void Volume)
0,09 0,35 0,12
9. Spearman’s correlation coefficient is a statistical measure of the strength of a
monotonic relationship between paired data., is denoted by 𝑟𝑠 , monotonic is a
function between ordered sets that preserves or reverses the given order (in calclus
means function is strickly increases or strickly decreases either positive or negative
di= the difference between
the ranks of corresponding
values, n= number of value in each
data set
Find the spearmen correlation
denoted by rs, (0≤rs ≤1.00).
• 020-39 Weak
• 0,4-0,69 Moderate
• 0,70-0,89 strong
• 0.9- 1, 0 very strong
Height Parameter
Sq
Ssk
Sku
Sp
Sv
Sz
Sa
Sq 100%
Ssk -54% 100%
Sku 44% -78% 100%
Sp 20% 25% 27% 100%
Sv 77% -84% 74% 7% 100%
Sz 71% -50% 73% 64% 81% 100%
Sa 83% -9% -9% 11% 39% 36% 100%
Selected
parameters
correlations
Smc Sq Vm Vv Vmc Sdq
Sxp 0,96
Sa 0,96
Vmp 1
Vmc 0,96
Vvc 0,99 0,99
Sdr 0,99
𝑟𝑠 = 1 −
6 𝑑𝑖
2
𝑁3 − 𝑑𝑖
2
𝑁
2. Spearman’s correlation
10. 1. Error bar : Average and Standard deviation method followed
by Anova and T-test
. Check the condition choosing the parameters:
Error bar overlapping : Neglect
All error bar not overlapping: Accept, means that experimental data
falling far Outside of Standard deviation are considered
. Error bar overlapping : Neglect
All error bar not overlapping:
Accept, means that
experimental data falling far
Outside of Standard deviation
are considered
11. 1. Error bar : Average and Standard deviation method
followed by Anova and T-test
Analysis of variance
Find the sum of parameters for each variant
Find the mean(average) for each variant
Find the difference between the observation and the mean (X-mean)
Find the variance (X-mean)2 Sum of the square
Find the total sum of the observation of the variants
Find the total sum of the square between group and the sum within the group
Find the degree of freedom between the group as well as with the group
Divide the sum of squares between groups by the degree of freedom
between groups MSw, divide the sum of squares within groups by degree of
freedom within groups MSB
Find F statistic ratio equal = MSw/ MSB
F > F Critical and P value less than 0.05 (p < 0.05) with (95% confidence), and
degree of freedom between group <F < degree of freedom within group,
means variants interval are disjunct for particular parameter (TRUE).
12. TRUE P(T<=t)t
wo
tail<(0,05
)
Parameter is disjunct for variants with
95% confident interval
FALSE P(T<=t)t
wo
tail>(0,05
)
Parameter is non-disjunct for variants
with 95% confident interval
PARAMETERS
(ISO25178,WP2)
NumberTRUE
S (Row)>6
Accept/
Reject
Sa(Arithemefic Mean
Height)
7 Accept
Smc (InverseAreal Material
Ratio)
8 Accept
Vv(Void Volume) 8 Accept
Vmc (Core Material
Volume)
8 Accept
Vvc(Core Void Volume) 8 Accept
Comparisn Between
Different
Variants(WP2)
Number of
TRUES(Coulu
mn) >15
SignificantI
Not
Significant
Comparison
between MSG186&
189
18 Significant
Comparison
between MSG189&
190
22 Significant
Comparison
between MSG189&
191
22 Significant
PARAMETERS
MSG186and187
MSG186and189
MSG186and190
MSG186and191
MSG187and189
MSG187and190
MSG187and191
MSG189and190
MSG189and191
MSG190and191
Sq F T F F F F F T T F
Ssk T F T F F T T T T F
Sku F F T T F T T T T F
Sp F T F F F F F T T F
Sv F T F F T F F T T F
Sz F T F F T F F T T F
Sa F T T T F T T T T F
Smr T T F F T T F T T F
Smc T T T T F T T T T F
Sxp F T F T F F F T T F
Sal T F T F T T T F F F
Str F T T F F F F T T F
Std F F F F F F F F F F
Sdq F T F F T F F T T F
Sdr F T F F T F F T T F
Vm F T F F F F F T T F
Vv T T T T F T T T T F
Vmp F T F F F F F T T F
Vmc T T T T F T T T T F
Vvc T T T T F T T T T F
Vvv F T F F T F F T T F
Spd F F T T F T T T T F
Spc F T T T T F F T T F
F: FALSE T: TRUE
• The comparison between the MSG186 and
MSG189, MSG189 and MSG190 , MSG189
and MSG191 are the highly significant
• The comparison between the MSG186 and
MSG189, 18 Trues in the column
• MSG189 and MSG190 , MSG189 and
MSG191 are the highly significant, 22
TRUES in the column
13. 2. Average and Standard deviation method
METHODS
1. Find the average and standard deviation
2. basis of the intervals and the mean
3. Then For each parameter, an interval for
good parts and for bad parts is calculated
with the coverage factor K, here we took
K=2
4. 𝐼′
𝑚𝑖𝑛 = 𝜇′
𝑖 − 𝑘𝜎′
𝑖 𝑎𝑛𝑑 𝐼′
𝑚𝑎𝑥 = 𝜇′
𝑖 +
𝑘𝜎′
𝑖
Check threshold & disjunct function
Select parameter have ´+´ve (disjunct)
significant factor
Reject Parameters Have ´-´ Si factor
𝑆 =
𝑑(𝐼′ 𝑖, 𝐼′′ 𝑖)
1
2 (𝜇′ 𝑖 + 𝜇′′ 𝑖)
Parameters - According
to ISO 25178
Significant factor
between MSG157
and MSG158
Significant Factor
between MSG158
and MSG160
Significant Factor
between MSG157
and MSG160
Sa (Arithemetic Mean
Height)
Si Factor ´-´ve
Rejected
0,20 0,05
Smc (p = 10%)(Inverse
Areal Material Ratio
0,05 0,29 0,11
Sxp (p = 50%, q =
96.5%)(Extreme Peak
Height)
Si Factor ´-´ve
Rejected
0,13 0,04
Vv (p = 10%)(Void
Volume)
0,05 0,29 0,10
Vmc (p = 10%, q =
80%)(Core Material
Volume)
0,07 0,32 0,11
Vvc (p = 10%, q = 80%)
(Core Void Volume)
0,09 0,35 0,12
14. Spearman’s correlation coefficient is a statistical measure of the strength of a
monotonic relationship between paired data., is denoted by 𝑟𝑠 , monotonic is a
function between ordered sets that preserves or reverses the given order (in calculus
means function is strickly increases or strickly decreases either positive or negative
di= the difference between
the ranks of corresponding
values, n= number of value in each
data set
Find the spearmen correlation
denoted by rs, (0≤rs ≤1.00).
• 020-39 Weak
• 0,4-0,69 Moderate
• 0,70-0,89 strong
• 0.9- 1, 0 very strong
Height Parameter
Sq
Ssk
Sku
Sp
Sv
Sz
Sa
Sq 100%
Ssk -54% 100%
Sku 44% -78% 100%
Sp 20% 25% 27% 100%
Sv 77% -84% 74% 7% 100%
Sz 71% -50% 73% 64% 81% 100%
Sa 83% -9% -9% 11% 39% 36% 100%
Selected
parameters
correlations
Smc Sq Vm Vv Vmc Sdq
Sxp 0,96
Sa 0,96
Vmp 1
Vmc 0,96
Vvc 0,99 0,99
Sdr 0,99
𝑟𝑠 = 1 −
6 𝑑𝑖
2
𝑁3 − 𝑑𝑖
2
𝑁
3. Spearman’s correlation
15. Results of work package 1
• The colour code of
the table is based on
the visual
estimations.
• Comparison between
different variants
with selected
parameters only used
for compare this
particular study.
B: blasting,
FGB: fine grain blasting,
P: polishing
SURFACE TEXTURE
ANALYSIS
Comparison only for WP 1
variants
Description for highest
values
Parameter Selected IS025178-2
Sa
Arithemeti
c Mean
Height
Sxp
(p = 50%),
(q=97.5%)
Smc
(P=10%)
Vv
(p =10%)
Vmc
(p=10%)
(q=80%)
Vvc
(p=10%,
q= 80%)
Units
µm µm µm µm³/µm² µm³/µm² µm³/µm²
Smooth <0,20 <0,6 <0,30 < 0,30 <0,20 <0,30
Medium 0,20-0,30 0,6-0,8 0,30-0,40 0,30-0,50
0,20-
0,30
0,30-
0,40
Rough >0,30 >0.8 >0.50 >0,50 >0,3 >0,40
MSG157
( B)
Higher bearing
of the material
from peak,
More Texture.
0,25 0,71 0,39 0,40 0,27 0,35
MSG158
(B-FGB)
Higher overall
texture, Higher
Bearing area.
Higher amount
fluid retention.
0,33 0,88 0,52 0,54 0,34 0,47
MSG160
(B.P )
Wide space
texture,
Comparatively
smooth surface
0,19 0,52 0,29 0,30 0,19 0,26
16. Results of work package 1
Sa=0,31um
Sa=0,34um
Sa=0,23um
MSG157
MSG160
MSG158
MSG157
MSG158
MSG160
17. • MSG157 surface characteristics, Str=0,7 Texture as suggesting highly isotropic texture,
without any lay. Uniform surfaces texture in all direction
• MSG158 Shows more texture, Str=0,4 Surface has a medium anisotropic texture
indicates or the presence of a dominating pattern in certain directions.
• MSG160 shows smoother Surface, anisotropic Str=0,3 Surface shows a
directionality.
MSG158
0.200
Parameters Value Unit
Isotropy 90.3 %
Periodicity ***** %
Period ***** µm
Directionof period ***** °
Results of work package 1
MSG160
0.200
Parameters Value Unit
Isotropy 59.1 %
Periodicity ***** %
Period ***** µm
Directionof period ***** °
0.200
Parameters Value Unit
Isotropy 84.5 %
Periodicity ***** %
Period ***** µm
Directionof period ***** °
MSG157
Str=0,7 Str=0,4 Str=0,3
18. Results of work package 2
PARAMETERS Selected From ISO 25718-2
Sa Smc (p = 10%)
Vv (p =
10%)
Vmc (p =
10%, q =
80%)
Vvc (p =
10%, q =
80%)
SURFACE TEXTURE ANALYSIS
(Comparison only for WP2 variants &
Description for highest values)
Units µm µm µm³/µm² µm³/µm² µm³/µm²
Smooth <0,2 <0,25 0,25 <0,20 <0,20
Medium 0,2-0,35 0,25-0,45 0,25 -0,50 0,2-0,30 0,20-0,35
Rough >0,35 >0,45 >0,50 >0,30 >0,35
Variant Surface
MG186 B-0-B
High bearing of materials
from peak
0,20 0,30 0,32 0,19 0,26
MSG187 B-FGB-B
High fluid retention and
scrap entrapment, Much
material beard away during
process, high bearing area
0,32 0,46 0,47 0,28 0,39
MSG189 B-P-B
High overall texture, high
bearing of material from
peaks, more fluid retention,
more wetted surface
0,37 0,49 0,52 0,26 0,40
MSG190 B-P-B, P
Surface in good condition,
smooth flat surfaces 0,17 0,24 0,24 0,15 0,19
MSG191 B-0-B,P
Surface in good condition,
smooth and flat surfaces 0,19 0,22 0,21 0,15 0,17
B: Blasting; FGB: Fine Grain Blasting; P: Polishing
21. Conclusion of work package 1
• The parameters which are important to look at when comparing the different variants to
each other are: arithmetic mean height(Sa), extreme peak height(Smc), void
volume(Vv), Core material volume(Vmc), Core void volume(Vvc) and Area
height difference(Sxp).
Which parameters are important for comparing the different variants to each
other?
Variants Manufacturing Process Comments are based on the analysis
from the parameters
MSG157 Blasting Higher bearing of the material from peak,
More Texture.
MSG158 Blasting followed by fine grain
blasting
Higher overall texture, Higher Bearing
area. Higher amount fluid retention.
MSG160 Blasting followed by polishing Wide space texture, Comparatively
smooth
How well does the study of surface topography of variants correlate to the
manufacturing process?
If there are a predominant direction of the topography? Yes
• MSG 157 shows larger ratio values i.e. Str> 0.5, indicate isotropy or uniform surface texture in all
directions.
• MSG 158 Indicates anisotropy or the presence of a dominating pattern in certain directions
• MSG 160 Str= 0,3 value shows small value; indicate anisotropy or the presence of a dominating
pattern in certain directions. It shows certain directionality.
22. If there is a connection found between the treatment prior to
coating and the outcome of the treatment after coating? Yes
Which are the parameters are important to look at when comparing to
each other?
The parameters which are important to look at when comparing the different variants
to each other are: arithmetic mean height (Sa), extreme peak height (Smc), void
volume (Vv), Core material volume (Vmc) and Core void volume (Vvc).
Variants
Manufacturing Process
(Pretreatment-ER Treatment-
Post treatment)
Comments are based on the analysis from the
parameters
MSG 186 Blasting -0-Blasting High bearing of materials from the peak
MSG 187 Blasting- Fine Grain Blasting-
Blasting
High fluid retention and scrap entrapment. Much
material beard away during process, high
bearing area
MSG 189 Blasting -Polishing-Blasting
High overall texture, high bearing of material
from peaks, more fluid retention, more wetted
surface
MSG 190
Blasting -Polishing- Blasting,
Polishing
Surface in good condition, smooth and flat
surfaces
MSG 191
Blasting -0-Blasting, Polishing Surface in good condition, smooth and flat
surfaces
Conclusion of work package 2
23. Is there any different measurement approach needed to evaluate the surface
roughness on variants in Work Package 2 compared to Work Package 1? Yes
Interferometer Reading
Variants
>3
Select Parameter
NEBNO=V
𝑺 𝒊 <
𝟎. 𝟎𝟓
𝑽
Number of
Trues > V+1
Reject Parameter
Yes
Work Package 2
Yes
Work Package1
Yes
No
NO NO
Yes
NEBNO=V
Yes
Yes
Average and SD
Custom Error Bar
Nod
MountainsMap
Excel/SPSS
24. PHASE 1 PHASE 2 PHASE 3
ccMSG 157
Blasting,
Sa=0,3um
MSG 158
Blasting-Fine Grain
Blasting
Sa=0,3um
MSG 160
Blasting-Polishing
Sa=0,2um
MSG 186
Blasting-0-Blasting
Sa=0,2um
MSG 187
Blasting-Fine Grain
Blasting-Blasting
Sa=0,3um
MSG 189
Blasting-Polishing-
Blasting
Sa=0,4um
MSG 190
Blasting-Polishing-
Blasting, Polishing
Sa=0,2um
MSG 191
Blasting-0-Blasting,
Polishing
Sa=0,2um
Comparison of different variants for work package1 and work package 2
Work Package 1 Work Package 2
Editor's Notes
Analysis of variance:
Find the sum of parameters for each variant
Find the mean(average) for each variant
Find the difference between the observation and the mean (X-mean)
Find the variance (X-mean)2 Sum of the square
Find the total sum of the observation of the variants
Find the total sum of the square between group and the sum within the group
Find the degree of freedom between the group as well as with the group
Divide the sum of squares between groups by the degree of freedom between groups MSw, divide the sum of squares within groups by degree of freedom within groups MSB
Find F statistic ratio equal = MSw/ MSB
F > F Critical and P value less than 0.05 (p < 0.05) with (95% confidence), and degree of freedom between group <F < degree of freedom within group, means variants interval are disjunct for particular parameter (TRUE).