DOCSLIB.ORG

Design of Experiments (DOE) Using the Taguchi Approach

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design of Experiments (DOE) Using the Taguchi Approach Design of Experiments (DOE) Using the Taguchi Approach This document contains brief reviews of several topics in the technique. For summaries of the recommended steps in application, read the published article attached. (Available for free download and review.) TOPICS: • Subject Overview • References Taguchi Method Review • Application Procedure • Quality Characteristics Brainstorming • Factors and Levels • Interaction Between Factors Noise Factors and Outer Arrays • Scope and Size of Experiments • Order of Running Experiments Repetitions and Replications • Available Orthogonal Arrays • Triangular Table and Linear Graphs Upgrading Columns • Dummy Treatments • Results of Multiple Criteria S/N Ratios for Static and Dynamic Systems • Why Taguchi Approach and Taguchi vs. Classical DOE • Loss Function • General Notes and Comments Helpful Tips on Applications • Quality Digest Article • Experiment Design Solutions • Common Orthogonal Arrays Other References: 1. DOE Demystified.. : http://manufacturingcenter.com/tooling/archives/1202/1202qmdesign.asp 2. 16 Steps to Product... http://www.qualitydigest.com/june01/html/sixteen.html 3. Read an independent review of Qualitek-4: http://www.qualitydigest.com/jan99/html/body_software.html 4. A Strategy for Simultaneous Evaluation of Multiple Objectives, A journal of the Reliability Analysis Center, 2004, Second quarter, Pages 14 - 18. http://rac.alionscience.com/pdf/2Q2004.pdf 5. Design of Experiments Using the Taguchi Approach : 16 Steps to Product and Process Improvement by Ranjit K. Roy Hardcover - 600 pages Bk&Cd-Rom edition (January 2001) John Wiley & Sons; ISBN: 0471361011 6. Primer on the Taguchi Method - Ranjit Roy (ISBN:087263468X Originally published in 1989 by Van Nostrand Reinhold. Current publisher/source is Society of Manufacturing Engineers). The book is available directly from the publisher, Society of Manufacturing Engineers (SME ) P.O. Box 6028, Dearborn, Michigan 48121, USA.256 Pages. 50 Illustrations. Order code: 2436-2487. For open enrollment seminar on Taguchi DOE technique, visit http://Nutek-us.com/wp-s4d.html . www.Nutek-us.com Subject Overview (The Taguchi Approach) - How much money you can save the design Design Of Experiments (DOE) is a powerful statistical - How you can determine which factor is technique introduced by R. A. Fisher in England in causing most variations in the result the 1920's to study the effect of multiple variables - How you can set up your process such that it simultaneously. In his early applications, Fisher is insensitive to the uncontrollable factors wanted to find out how much rain, water, fertilizer, - Which factors have more influence on the sunshine, etc. are needed to produce the best crop. mean performance Since that time, much development of the technique What you need to do to reduce performance has taken place in the academic environment, but did - variation around the target help generate many applications in the production floor. - How your response varies proportional to signal factor (Dynamic response) As a researcher in Electronic Control Laboratory in - How to combine multiple criteria of Japan, Dr. Genechi Taguchi carried out significant evaluation into a single index research with DOE techniques in the late 1940's. He - How you can adjust factor for overall spent considerable effort to make this experimental satisfaction of criteria an adjust factors for a technique more user-friendly (easy to apply) and system whose of evaluations applied it to improve the quality of manufactured - How the uncontrollable factors affect the products. Dr. Taguchi's standardized version of DOE, performance popularly known as the Taguchi method or Taguchi etc., approach, was introduced in the USA in the early 1980's. Today it is one of the most effective quality building tools used by engineers in all types of Advantage of DOE Using Taguchi Approach manufacturing activities. The application of DOE requires careful planning, The DOE using Taguchi approach can economically prudent layout of the experiment, and expert analysis satisfy the needs of problem solving and of results. Based on years of research and product/process design optimization projects. By applications Dr. Genechi Taguchi has standardized learning and applying this technique, engineers, the methods for each of these DOE application steps scientists, and researchers can significantly reduce described below. Thus, DOE using theTaguchi the time required for experimental investigations. approach has become a much more attractive tool to practicing engineers and scientists. DOE can be highly effective when yow wish to: - Optimize product and process designs, study Experiment planning and problem formulation - the effects of multiple factors (i.e.- variables, Experiment planning guidelines are consistent with parameters, ingredients, etc.) on the modern work disciplines of working as teams. performance, and solve production problems Consensus decisions about experimental objectives by objectively laying out the investigative and factors make the projects more successful. experiments. (Overall application goals). - Study Influence of individual factors on the Experiment layout -High emphasis is put on cost and performance and determine which factor has size of experiments... Size of the experiment for a more influence, which ones have less. You given number of factors and levels is standardized... can also find out which factor should have Approach and priority for column assignments are tighter tolerance and which tolerance should established... Clear guidelines are available to deal be relaxed. The information from the with factors and interactions (interaction tables)... experiment will tell you how to allocate Uncontrollable factors are formally treated to reduce quality assurance resources based on the variation... Discrete prescriptions for setting up test objective data. It will indicate whether a conditions under uncontrollable factors are supplier's part causes problems or not described... Guidelines for carrying out the (ANOVA data), and how to combine different experiments and number of samples to be tested are factors in their proper settings to get the best defined results (Specific Objectives). Data analysis -Steps for analysis are standardized (main effect, NOVA and Optimum)... Standard Further, the experimental data will allow you practice for determination of the optimum is determine: recommended... Guidelines for test of significance - How to substitute a less expensive part to get the and pooling are defined... same performanceNutek, Inc. Bloomfieldimprovement Hills, MI.you USA. propose Tel: 1-248-540-4827 [email protected] www.Nutek-us.com 0607 Page 2 APPROACH: ROBUST DESIGN Interpretation of results - Clear guidelines about meaning of error term... Discrete indicator about - Reduce variation without actually removing the cause of variation. Achieve consistent confirmation of results (Confidence interval)... Ability to quantify improvements in terms of dollars (Loss performance by making product/process insensitive to the influence of uncontrollable factors. function) Overall advantage - DOE using Taguchi approach attempts to improve quality which is defined as the WHAT DOES IT DO? - Optimize design, solve consistency of performance. Consistency is achieved problems, build robust products, etc. when variation is reduced. This can be done by moving the mean performance to the target as well WHAT DOES IT DO? as by reducing variations around the target. The - Optimize design, solve problems, build robust prime motivation behind the Taguchi experiment products, etc. design technique is to achieve reduced variation (also known as ROBUST DESIGN). This technique, WHY DO IT? therefore, is focused to attain the desired quality - Save cost (Reduce warranty, rejection and objectives in all steps. The classical DOE does not cost of development). specifically address quality . AREAS OF APPLICATION: "The primary problem addressed in classical - Analytical simulation (in early stages of statistical experiment design is to model the design). response of a product or process as a function of - Development testing (in design and many factors called model factors. Factors, called development). nuisance factors, which are not included in the - Process development. model, can also influence the response... The - Manufacturing. primary problem addressed in Robust Design is how - Problem solving in all areas of manufacturing to reduce the variance of a product's function in the and production. customer's environment." -Madhav Phadke, Quality Engineering using Robust Design. WHAT'S NEW? 1. NEW PHILOSOPHY - Building quality in the product design. - Measuring quality by deviation from target (not by rejection). 2. NEW DISCIPLINE - Complete planning of experiments and evaluation criteria before conducting experiments. - Determining a factor's influence by running the complete experiment. 3. SIMPLER AND STANDARDIZED EXPERIMENT DESIGN FORMAT - Orthogonal arrays for experimental design. - Outer array design for robust product design. - More clear and easier methods for analysis of results. QUALITY: DEFINITION and OBJECTIVE http://nutek-us.com/wp-txt.html - Reduced variation around the target with least cost. Nutek, Inc. Bloomfield Hills, MI. USA. Tel: 1-248-540-4827 [email protected] www.Nutek-us.com 0607 Page 3 TAGUCHI METHOD REVIEW BRAINSTORMING SUGGESTED TOPICS OF DISCUSSIONS: APPLICATION STEPS The Taguchi method is used to improve the 1. OBJECTIVES AND EVALUATION CRITERIA quality
Load more

Recommended publications
  • Contributions of the Taguchi Method
    1 International System Dynamics Conference 1998, Québec Model Building and Validation: Contributions of the Taguchi Method Markus Schwaninger, University of St. Gallen, St. Gallen, Switzerland Andreas Hadjis, Technikum Vorarlberg, Dornbirn, Austria 1. The Context. Model validation is a crucial aspect of any model-based methodology in general and system dynamics (SD) methodology in particular. Most of the literature on SD model building and validation revolves around the notion that a SD simulation model constitutes a theory about how a system actually works (Forrester 1967: 116). SD models are claimed to be causal ones and as such are used to generate information and insights for diagnosis and policy design, theory testing or simply learning. Therefore, there is a strong similarity between how theories are accepted or refuted in science (a major epistemological and philosophical issue) and how system dynamics models are validated. Barlas and Carpenter (1992) give a detailed account of this issue (Barlas/Carpenter 1990: 152) comparing the two major opposing streams of philosophies of science and convincingly showing that the philosophy of system dynamics model validation is in agreement with the relativistic/holistic philosophy of science. For the traditional reductionist/logical empiricist philosophy, a valid model is an objective representation of the real system. The model is compared to the empirical facts and can be either correct or false. In this philosophy validity is seen as a matter of formal accuracy, not practical use. In contrast, the more recent relativist/holistic philosophy would see a valid model as one of many ways to describe a real situation, connected to a particular purpose.
    [Show full text]
  • A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing
    A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing Jingbo Jiang Diego Legrand Robert Severn Risto Miikkulainen University of Pennsylvania Criteo Evolv Technologies The University of Texas at Austin Philadelphia, USA Paris, France San Francisco, USA and Cognizant Technology Solutions [email protected] [email protected] [email protected] Austin and San Francisco, USA [email protected],[email protected] Abstract—Multivariate testing has recently emerged as a [17]. While this process captures interactions between these promising technique in web interface design. In contrast to the elements, only a very small number of elements is usually standard A/B testing, multivariate approach aims at evaluating included (e.g. 2-3); the rest of the design space remains a large number of values in a few key variables systematically. The Taguchi method is a practical implementation of this idea, unexplored. The Taguchi method [12], [18] is a practical focusing on orthogonal combinations of values. It is the current implementation of multivariate testing. It avoids the compu- state of the art in applications such as Adobe Target. This paper tational complexity of full multivariate testing by evaluating evaluates an alternative method: population-based search, i.e. only orthogonal combinations of element values. Taguchi is evolutionary optimization. Its performance is compared to that the current state of the art in this area, included in commercial of the Taguchi method in several simulated conditions, including an orthogonal one designed to favor the Taguchi method, and applications such as the Adobe Target [1]. However, it assumes two realistic conditions with dependences between variables.
    [Show full text]
  • Interaction Graphs for a Two-Level Combined Array Experiment Design by Dr
    Journal of Industrial Technology • Volume 18, Number 4 • August 2002 to October 2002 • www.nait.org Volume 18, Number 4 - August 2002 to October 2002 Interaction Graphs For A Two-Level Combined Array Experiment Design By Dr. M.L. Aggarwal, Dr. B.C. Gupta, Dr. S. Roy Chaudhury & Dr. H. F. Walker KEYWORD SEARCH Research Statistical Methods Reviewed Article The Official Electronic Publication of the National Association of Industrial Technology • www.nait.org © 2002 1 Journal of Industrial Technology • Volume 18, Number 4 • August 2002 to October 2002 • www.nait.org Interaction Graphs For A Two-Level Combined Array Experiment Design By Dr. M.L. Aggarwal, Dr. B.C. Gupta, Dr. S. Roy Chaudhury & Dr. H. F. Walker Abstract interactions among those variables. To Dr. Bisham Gupta is a professor of Statistics in In planning a 2k-p fractional pinpoint these most significant variables the Department of Mathematics and Statistics at the University of Southern Maine. Bisham devel- factorial experiment, prior knowledge and their interactions, the IT’s, engi- oped the undergraduate and graduate programs in Statistics and has taught a variety of courses in may enable an experimenter to pinpoint neers, and management team members statistics to Industrial Technologists, Engineers, interactions which should be estimated who serve in the role of experimenters Science, and Business majors. Specific courses in- clude Design of Experiments (DOE), Quality Con- free of the main effects and any other rely on the Design of Experiments trol, Regression Analysis, and Biostatistics. desired interactions. Taguchi (1987) (DOE) as the primary tool of their trade. Dr. Gupta’s research interests are in DOE and sam- gave a graph-aided method known as Within the branch of DOE known pling.
    [Show full text]
  • A Primer on the Taguchi Method
    A PRIMER ON THE TAGUCHI METHOD SECOND EDITION Ranjit K. Roy Copyright © 2010 Society of Manufacturing Engineers 987654321 All rights reserved, including those of translation. This book, or parts thereof, may not be reproduced by any means, including photocopying, recording or microfilming, or by any information storage and retrieval system, without permission in writing of the copyright owners. No liability is assumed by the publisher with respect to use of information contained herein. While every precaution has been taken in the preparation of this book, the publisher assumes no responsibility for errors or omissions. Publication of any data in this book does not constitute a recommendation or endorsement of any patent, proprietary right, or product that may be involved. Library of Congress Control Number: 2009942461 International Standard Book Number: 0-87263-864-2, ISBN 13: 978-0-87263-864-8 Additional copies may be obtained by contacting: Society of Manufacturing Engineers Customer Service One SME Drive, P.O. Box 930 Dearborn, Michigan 48121 1-800-733-4763 www.sme.org/store SME staff who participated in producing this book: Kris Nasiatka, Manager, Certification, Books & Video Ellen J. Kehoe, Senior Editor Rosemary Csizmadia, Senior Production Editor Frances Kania, Production Assistant Printed in the United States of America Preface My exposure to the Taguchi methods began in the early 1980s when I was employed with General Motors Corporation at its Technical Center in Warren, Mich. At that time, manufacturing industries as a whole in the Western world, in particular the auto- motive industry, were starving for practical techniques to improve quality and reliability.
    [Show full text]
  • Applying Significance Testing to the Taguchi Methods of Quality Control. Shannon Jo Kast Louisiana State University and Agricultural & Mechanical College
    Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1997 Applying Significance Testing to the Taguchi Methods of Quality Control. Shannon Jo Kast Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Kast, Shannon Jo, "Applying Significance Testing to the Taguchi Methods of Quality Control." (1997). LSU Historical Dissertations and Theses. 6427. https://digitalcommons.lsu.edu/gradschool_disstheses/6427 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. U M I films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type o f computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UM I a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note w ill indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.
    [Show full text]
  • Applied Design of Experiments and Taguchi Methods
    APPLIED DESIGN OF EXPERIMENTS AND TAGUCHI METHODS 10 8 y 6 1 4 0 B -1 -1 0 A 1 K. Krishnaiah P. Shahabudeen Applied Design of Experiments and Taguchi Methods Applied Design of Experiments and Taguchi Methods K. KRISHNAIAH Former Professor and Head Department of Industrial Engineering Anna University, Chennai P. SHAHABUDEEN Professor and Head Department of Industrial Engineering Anna University, Chennai New Delhi-110001 2012 ` 395.00 APPLIED DESIGN OF EXPERIMENTS AND TAGUCHI METHODS K. Krishnaiah and P. Shahabudeen © 2012 by PHI Learning Private Limited, New Delhi. All rights reserved. No part of this book may be reproduced in any form, by mimeograph or any other means, without permission in writing from the publisher. ISBN-978-81-203-4527-0 The export rights of this book are vested solely with the publisher. Published by Asoke K. Ghosh, PHI Learning Private Limited, M-97, Connaught Circus, New Delhi-110001 and Printed by Baba Barkha Nath Printers, Bahadurgarh, Haryana-124507. To My wife Kasthuri Bai and Students — K. Krishnaiah To My Teachers and Students — P. Shahabudeen Contents Preface ............................................................................................................................................... xiii PART I: DESIGN OF EXPERIMENTS 1. REVIEW OF STATISTICS ........................................................................................... 3–21 1.1 Introduction ..................................................................................................................... 3 1.2 Normal Distribution.......................................................................................................
    [Show full text]
  • Optimization of the Polishing Efficiency and Torque by Using
    applied sciences Article Optimization of the Polishing Efficiency and Torque by Using Taguchi Method and ANOVA in Robotic Polishing Imran Mohsin 1,2,3 , Kai He 1,3,* , Zheng Li 4, Feifei Zhang 1,3 and Ruxu Du 5 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; [email protected] (I.M.); ff[email protected] (F.Z.) 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Shenzhen Key Laboratory of Precision Engineering, Shenzhen 518055, China 4 Department of Surgery and Chow Yuk Ho Technology Centre for Innovation Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China; [email protected] 5 S. M. Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China; [email protected] * Correspondence: [email protected] Received: 31 December 2019; Accepted: 19 January 2020; Published: 23 January 2020 Abstract: Surface finishing and polishing are important quality assurance processes in many manufacturing industries. A polished surface (low surface roughness) is linked with many useful properties other than providing an appealing gloss to the product, such as surface friction, electrical and chemical resistance, thermal conductivity, reflection, and product life. All these properties require an efficient polishing system working with the best machining parameters. This study analyzed the effects of the different input polishing parameters on the polishing efficiency and torque in the robotic polishing system for the circular-shaped workpieces (such as ring, cylinder, sphere, cone, etc.) by using the Taguchi method and analysis of variance (ANOVA). A customized rotatory passive gripper is designed to hold the watch bezel during polishing.
    [Show full text]
  • 14.5 ORTHOGONAL ARRAYS (Updated Spring 2003)
    14.5 ORTHOGONAL ARRAYS (Updated Spring 2003) Orthogonal Arrays (often referred to Taguchi Methods) are often employed in industrial experiments to study the effect of several control factors. Popularized by G. Taguchi. Other Taguchi contributions include: • Model of the Engineering Design Process • Robust Design Principle • Efforts to push quality upstream into the engineering design process An orthogonal array is a type of experiment where the columns for the independent variables are “orthogonal” to one another. Benefits: 1. Conclusions valid over the entire region spanned by the control factors and their settings 2. Large saving in the experimental effort 3. Analysis is easy To define an orthogonal array, one must identify: 1. Number of factors to be studied 2. Levels for each factor 3. The specific 2-factor interactions to be estimated 4. The special difficulties that would be encountered in running the experiment We know that with two-level full factorial experiments, we can estimate variable interactions. When two-level fractional factorial designs are used, we begin to confound our interactions, and often lose the ability to obtain unconfused estimates of main and interaction effects. We have also seen that if the generators are choosen carefully then knowledge of lower order interactions can be obtained under that assumption that higher order interactions are negligible. Orthogonal arrays are highly fractionated factorial designs. The information they provide is a function of two things • the nature of confounding • assumptions about the physical system. Before proceeding further with a closer look, let’s look at an example where orthogonal arrays have been employed. Example taken from students of Alice Agogino at UC-Berkeley A Airplane Taguchi Experiment This experiment has 4 variables at 3 different settings.
    [Show full text]
  • Application of Taguchi Method in the Optimization of Cutting Parameters for Surface Roughness in Turning
    APPLICATION OF TAGUCHI METHOD IN THE OPTIMIZATION OF CUTTING PARAMETERS FOR SURFACE ROUGHNESS IN TURNING MOHD NAIIM B YUSOFF A report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Mechanical Engineering Faculty of Mechanical Engineering Universiti Malaysia Pahang MAY 2008 viii ABSTRACT In this study, the Taguchi Method is used to find the optimal cutting parameters for surface roughness in turning operation. The orthogonal array, the signal-to-noise ratio, and analysis of variance are employed to study the performance characteristics in turning operations of AISI 1030 steel bars using TiN coated tools. Three cutting parameters namely, insert radius, feed rate, and depth of cut, are optimized with considerations of surface roughness. Experimental results are provided to illustrate the effectiveness of this approach. Ix ABSTRAK Dalam kajian mi, Kaedah Taguchi telah diguna pakai untuk mencari parameter pemotongan yang optimum bagi kekasaran permukaaan untuk proses putaran. Susunan orthogonal, nisbah signal-to-noise, dan variasi analisis telah digunakan untuk mengkaji pencirian prestasi dalam operasi putaran bagi batang besi AISI 1030 dengan menggunakan mata bersadur TiN. Tiga parameter, pemotongan iaitu, insert radius, kadar makan, kedalaman pemotongan, telah dioptimumkan dengan mengambil kira kekasaran permukaan. Keputusan eksperimen disediakan untuk mengambarkan keberkesanan kaedah mi. x TABLE OF CONTENTS CHAPTER TITLE PAGE TITLE i SUPERVISOR DECLARATION iv STUDENT DECLARATION v DEDICATION
    [Show full text]
  • Hybrid Taguchi-Differential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics
    Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2011, Article ID 514756, 14 pages doi:10.1155/2011/514756 Research Article Hybrid Taguchi-Differential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics Wen-Hsien Ho1 and Agnes Lai-Fong Chan2 1 Department of Medical Information Management, Kaohsiung Medical University, 100 Shi-Chuan 1st Road, Kaohsiung 807, Taiwan 2 Department of Pharmacy, Chi Mei Medical Center, 901 Chung Hwa Road, Yong Kang, Tainan 701, Taiwan Correspondence should be addressed to Agnes Lai-Fong Chan, [email protected] Received 5 June 2010; Revised 6 August 2010; Accepted 17 September 2010 Academic Editor: Marcelo Messias Copyright q 2011 W.-H. Ho and A. L.-F. Chan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This work emphasizes solving the problem of parameter estimation for a human immunodefi- ciency virus HIV dynamical model by using an improved differential evolution, which is called the hybrid Taguchi-differential evolution HTDE. The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve the problem of parameter estimation for an HIV dynamical model and is also compared with the traditional differential evolution DE approach and the numerical methods presented in the literature. An illustrative example shows that the proposed HTDE gives an effective and robust way for obtaining optimal solution, and can get better results than the traditional DE approach and the numerical methods presented in the literature for an HIV dynamical model.
    [Show full text]
  • Application of Taguchi Method and ANOVA in the Optimization of Dyeing Process on Cotton Knit Fabric to Reduce Re-Dyeing Process
    IOP Conference Series: Earth and Environmental Science PAPER • OPEN ACCESS Related content - A comparative study of electrochemical Application of Taguchi method and ANOVA in the machining process parameters by using GA and Taguchi method optimization of dyeing process on cotton knit fabric S K Soni and B Thomas - Application of taguchi method for selection to reduce re-dyeing process parameter bleaching treatments against mechanical and physical properties of agave cantala fiber To cite this article: Wahyudin et al 2017 IOP Conf. Ser.: Earth Environ. Sci. 109 012023 F Yudhanto, Jamasri and Heru S B Rochardjo - A Gradient Taguchi Method for Engineering Optimization Shun-Fa Hwang, Jen-Chih Wu and Rong- View the article online for updates and enhancements. Song He Recent citations - Parameter Optimization of Ball Milling Process for Silica Sand Tailing Sukanto et al This content was downloaded from IP address 170.106.35.76 on 26/09/2021 at 00:02 The International Conference on Eco Engineering Development 2017 (ICEED 2017) IOP Publishing IOP Conf. Series: Earth and Environmental Science 109 (2017) 012023 doi:10.1088/1755-1315/109/1/012023 Application of Taguchi method and ANOVA in the optimization of dyeing process on cotton knit fabric to reduce re-dyeing process 1Wahyudin, 2Angel Kharisma, 2Richard Dimas Julian Murphiyanto, 2Muhammad Kevin Perdana, 2Tota Pirdo Kasih 1Graduate Program, Universitas Mercu Buana, JL. Menteng Raya No. 29 Jakarta 10340, Indonesia 2Industrial Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, Indonesia 11480 Corresponding author: [email protected] Abstract. In the textile industry, tons of dyes are lost to effluents every year during the dyeing and finishing operations, due to the inefficient processes.
    [Show full text]
  • A First Course in Experimental Design Notes from Stat 263/363
    A First Course in Experimental Design Notes from Stat 263/363 Art B. Owen Stanford University Autumn 2020 ii © Art Owen 2020 Dec 6 2020 version. Do not distribute or post electronically without author's permission Preface This is a collection of scribed lecture notes about experimental design. The course covers classical experimental design methods (ANOVA and blocking and factorial settings) along with Taguchi methods for robust design, A/B testing and bandits for electronic commerce, computer experiments and supersaturated designs suitable for fitting sparse regression models. There are lots of different ways to do an experiment. Most of the time the analysis of the resulting data is familiar from a regression class. Since this is one of the few classes about making data we proceed assuming that the students are already familiar with t-tests and regression methods for analyzing data. There are a few places where the analysis of experimental data differs from that of observational data, so the lectures cover some of those. The opening lecture sets the scene for experimental design by grounding it in causal inference. Most of causal inference is about inferring causality from purely observational data where you cannot randomize (or did not randomize). While inferring causality from purely observational data requires us to rely on untestable assumptions, in some problems it is the only choice. When one can randomize then much stronger causal claims are possible. This course is about how to use such randomization. Several of the underlying ideas from causal inference are important even when you did randomize. These include the potential outcomes framework, conveniently depicted via a device sometimes called the science table, the Stable Unit Treatment Value Assumption (SUTVA) that the outcome for one experimental unit is not affected by the treatment for any other one, and the issue of external validity that comes up generalizing from the units in a study to others.
    [Show full text]