Applied Design Of Experiments And Taguchi Methods

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Applied Design Of Experiments And Taguchi Methods

1.1 What is the Taguchi approach and who is Taguchi? 1-1 1.2 A New Philosophy and Approach to Quality 1-2 1.3 A New Discipline 1-4 1.4 A Simplified and Standardized Experiment Design Technique 1-6 1.5 Seminar Objectives 1-7 Review Questions 1-9

Taguchi Grey Relational Optimization Of Sol–gel Derived Hydroxyapatite From A Novel Mix Of Two Natural Biowastes For Biomedical Applications

2.1 Basics of Experimental Design 2-1 2.2 Experimenting with 2-Level Factors 2-4 2.3 Designing Seven-Factor 2-Level Experiments 2-6 2.4 Seven-Factor Full Factorial Design 2-7 2.5 Tools for Experimental Design 2 – 6 8 2.6 Application steps 2-11 2.7 Experiment planning procedure

2-13 2.8 Results with Multiple Evaluation Criteria 2-14 2.9 Common Terms and Their Definitions 2-15 2.10 Analysis of Results 2-17 2.11 Multifactorial Experimental Design 2-21 2.12 Basic Experiment Design 2-21 Analysis of Basic Experiments Accu2-3. Experiments 2-23 2.14 Learning Checklist 2-24

3.1 Experiments Designed to Study Interactions 3-1 3.2 Guidelines for Interaction Studies 3-5 3.3 Steps in Interaction Analysis 3-6

4.1 Modifying Standard Orthogonal Arrays 4-1 4.2 Upgrading Column Levels 4-2 4.3 Lowering Columns 4-6 4.4 Level Incompatible Factors 4-10 4.5 Combination Design (Special Technique) 4-11 4-11 RECAP Course

Taguchi’s Methodology For Multi‐response Optimization: A Case Study In The Indian Plastics Industry

5.1 Ambitious Business Goals 5-1 5.2 Mechanics of External Array Design 5-3 5.3 Advantages of External Array Design 5-5 5.4 Analysis of Replicate Results 5-6 5.5 Definition of MSD for Three QCs 5-8 and Analysis Experiment Strate Design 5 – 12 5.7 Tips for Experiment Design 5-13

6.1 Guidelines for Analysis 6-1 6.2 Test for the Presence of Interaction 6-5 6.3 ANOVA 6-6 6.4 Summary Technique 6-13 6.5 Confidence Intervals of Main Effects 6-15 6.6 Discussion of Some Key Issues 6-15 Example 6-15 Analyzes 6-15 : with more movements 6-19 6.8 Example 9A: S/N analysis – nominal is best 6-22 6.9 Example 9B: S/N analysis – smaller is better 6-24 6.10 Example 9C: S/N analysis – bigger is better 6 -26 6.11 Understanding and Interpreting Error Terms 6-29

8.1 Application Description Steps 8-2 8.2 Design of Experiments (DOE) Thinking Ideas 8-3 8.3 General Evaluation Criteria 8-5 8.4 Taguchi Vs. DOE Classic 8-8 Open Access Policy Institutional Open Access Program Guidelines for Special Issues Editorial Process Research and Publication Ethics Article Processing Awards Awards Statements

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Design Optimization Of Rotor Bearing System Considering Critical Speed Using Taguchi Method

Feature Papers represent cutting-edge research with significant potential for major impact in the field. Short papers are submitted by individual call or recommendation of scientific editors and are peer-reviewed before publication.

A Feature Paper can be either an original research article, a significant new research study often involving several techniques or methods, or a comprehensive review paper with concise and precise updates of the latest advances in the field. n systematic review of the most exciting developments in science. literature. This type of work provides an overview of possible future research directions or applications.

Editor’s Choice articles are based on recommendations from scientific journal editors from around the world. The editors select a small number of recently published journal articles that they believe will be of particular interest to readers, or important in a particular area of ​​research. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Received: 10 June 2020 / Revised: 23 July 2020 / Accepted: 27 July 2020 / Published: 30 July 2020

Integration Of Multivariate Control Charts And Decision Tree Classifier To Determine The Faults Of The Quality Characteristic(s) Of A Melt Spinning Machine Used In Polypropylene As Spun Fiber Manufacturing Part I: The Application

In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error approach to selecting CNN parameters. In this study, an AlexNet network with optimal parameters is proposed for face image recognition. The Taguchi method is used to select the preliminary factors, and the experiments are performed by designing an orthogonal table. The proposed method filters out significantly affected factors. Finally, the experimental results show that the proposed Taguchi-based AlexNet achieves an average accuracy of 87.056% and 98.72% in image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than the original AlexNet network in the CIA and MORPH databases, respectively.

In recent years, facial recognition has been widely used in various fields. Since the face is a very complex and important biological feature, it contains a lot of information, such as gender, age and facial expression. Ref. [1, 2, 3, 4] Automatic analysis of gender and age has produced many applications for business models. For example, collecting demographic data and selling products [5]. The intelligent facial recognition system can be based on big data about the customer’s gender and age, so that the seller can accurately and clearly understand the group of customers who bought this product. Therefore, this study focuses on the analysis of gender distribution.

Machine learning has been successfully applied to gender recognition. Saatchi et al. [4] proposed a method to identify the gender and expression of facial images through the Active Appearance Model (AAM). The features extracted using the trained AAM can be used to build support vector machine (SVM) classifiers. These classifiers are arranged in a cascade structure to optimize the overall recognition performance. Mäkinen and Raisamo [5] conducted experiments by numerically combining the results of a gender classifier. This leads to better classification accuracy. Singh et al. [6] used local binary pattern (LBP) and histogram gradient gradient (HOG) feature extraction algorithms and SVM classifiers to obtain more efficient gender classification of face images. Eidinger et al. [7] proposed a dropout support vector machine method for facial attribute estimation to avoid overfitting. This approach is inspired by a popular learning technology that leaves deep belief networks. In addition, a robust face alignment technique is proposed that specifically takes into account the uncertainty of facial feature sensors. Although the above methods can successfully achieve gender recognition, the user needs to determine the characteristics of gender images in advance. Therefore, this study will adopt a convolutional neural network (CNN) to overcome the above problems.

Recently, CNN is a type of deep neural network commonly used for visual image recognition and detection [8, 9, 10, 11]. CNN is widely used in various fields, such as object detection [12], face recognition [13, 14] and speech recognition [15]. For gender classification, Duan [8] proposed a hybrid of CNN and extreme learning machine (ELM) to incorporate the synergy of the two classifiers for gender image classification. Levi and Hassner [14] used deep evolutionary neural networks to learn representations. A significant increase in performance can be achieved on gender classification. Ozbulak et al. [16] examined the transferability of existing CNN models for age and gender classification. A common AlexNet-like architecture and a domain-specific VGG-Face CNN model were used and refined with the Adience dataset for age and gender classification in an unsupervised environment. In order to achieve a better performance of gender classification,

Design And Analysis Of Experiments

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