Doe) Applied to Pharmaceutical and Analytical Quality by Design (Qbd
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Design of Experiments Application, Concepts, Examples: State of the Art
Periodicals of Engineering and Natural Scinces ISSN 2303-4521 Vol 5, No 3, December 2017, pp. 421‒439 Available online at: http://pen.ius.edu.ba Design of Experiments Application, Concepts, Examples: State of the Art Benjamin Durakovic Industrial Engineering, International University of Sarajevo Article Info ABSTRACT Article history: Design of Experiments (DOE) is statistical tool deployed in various types of th system, process and product design, development and optimization. It is Received Aug 3 , 2018 multipurpose tool that can be used in various situations such as design for th Revised Oct 20 , 2018 comparisons, variable screening, transfer function identification, optimization th Accepted Dec 1 , 2018 and robust design. This paper explores historical aspects of DOE and provides state of the art of its application, guides researchers how to conceptualize, plan and conduct experiments, and how to analyze and interpret data including Keyword: examples. Also, this paper reveals that in past 20 years application of DOE have Design of Experiments, been grooving rapidly in manufacturing as well as non-manufacturing full factorial design; industries. It was most popular tool in scientific areas of medicine, engineering, fractional factorial design; biochemistry, physics, computer science and counts about 50% of its product design; applications compared to all other scientific areas. quality improvement; Corresponding Author: Benjamin Durakovic International University of Sarajevo Hrasnicka cesta 15 7100 Sarajevo, Bosnia Email: [email protected] 1. Introduction Design of Experiments (DOE) mathematical methodology used for planning and conducting experiments as well as analyzing and interpreting data obtained from the experiments. It is a branch of applied statistics that is used for conducting scientific studies of a system, process or product in which input variables (Xs) were manipulated to investigate its effects on measured response variable (Y). -
Design of Experiments (DOE) Using JMP Charles E
PharmaSUG 2014 - Paper PO08 Design of Experiments (DOE) Using JMP Charles E. Shipp, Consider Consulting, Los Angeles, CA ABSTRACT JMP has provided some of the best design of experiment software for years. The JMP team continues the tradition of providing state-of-the-art DOE support. In addition to the full range of classical and modern design of experiment approaches, JMP provides a template for Custom Design for specific requirements. The other choices include: Screening Design; Response Surface Design; Choice Design; Accelerated Life Test Design; Nonlinear Design; Space Filling Design; Full Factorial Design; Taguchi Arrays; Mixture Design; and Augmented Design. Further, sample size and power plots are available. We give an introduction to these methods followed by a few examples with factors. BRIEF HISTORY AND BACKGROUND From early times, there has been a desire to improve processes with controlled experimentation. A famous early experiment cured scurvy with seamen taking citrus fruit. Lives were saved. Thomas Edison invented the light bulb with many experiments. These experiments depended on trial and error with no software to guide, measure, and refine experimentation. Japan led the way with improving manufacturing—Genichi Taguchi wanted to create repeatable and robust quality in manufacturing. He had been taught management and statistical methods by William Edwards Deming. Deming taught “Plan, Do, Check, and Act”: PLAN (design) the experiment; DO the experiment by performing the steps; CHECK the results by testing information; and ACT on the decisions based on those results. In America, other pioneers brought us “Total Quality” and “Six Sigma” with “Design of Experiments” being an advanced methodology. -
Application of a Central Composite Design for the Study of Nox Emission Performance of a Low Nox Burner
Energies 2015, 8, 3606-3627; doi:10.3390/en8053606 OPEN ACCESS energies ISSN 1996-1073 www.mdpi.com/journal/energies Article Application of a Central Composite Design for the Study of NOx Emission Performance of a Low NOx Burner Marcin Dutka 1,*, Mario Ditaranto 2,† and Terese Løvås 1,† 1 Department of Energy and Process Engineering, Norwegian University of Science and Technology, Kolbjørn Hejes vei 1b, Trondheim 7491, Norway; E-Mail: [email protected] 2 SINTEF Energy Research, Sem Sælands vei 11, Trondheim 7034, Norway; E-Mail: [email protected] † These authors contributed equally to this work. * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +47-41174223. Academic Editor: Jinliang Yuan Received: 24 November 2014 / Accepted: 22 April 2015 / Published: 29 April 2015 Abstract: In this study, the influence of various factors on nitrogen oxides (NOx) emissions of a low NOx burner is investigated using a central composite design (CCD) approach to an experimental matrix in order to show the applicability of design of experiments methodology to the combustion field. Four factors have been analyzed in terms of their impact on NOx formation: hydrogen fraction in the fuel (0%–15% mass fraction in hydrogen-enriched methane), amount of excess air (5%–30%), burner head position (20–25 mm from the burner throat) and secondary fuel fraction provided to the burner (0%–6%). The measurements were performed at a constant thermal load equal to 25 kW (calculated based on lower heating value). Response surface methodology and CCD were used to develop a second-degree polynomial regression model of the burner NOx emissions. -
Recommendations for Design Parameters for Central Composite Designs with Restricted Randomization
Recommendations for Design Parameters for Central Composite Designs with Restricted Randomization Li Wang Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Statistics Geoff Vining, Chair Scott Kowalski, Co-Chair John P. Morgan Dan Spitzner Keying Ye August 15, 2006 Blacksburg, Virginia Keywords: Response Surface Designs, Central Composite Design, Split-plot Designs, Rotatability, Orthogonal Blocking. Copyright 2006, Li Wang Recommendations for Design Parameters for Central Composite Designs with Restricted Randomization Li Wang ABSTRACT In response surface methodology, the central composite design is the most popular choice for fitting a second order model. The choice of the distance for the axial runs, α, in a central composite design is very crucial to the performance of the design. In the literature, there are plenty of discussions and recommendations for the choice of α, among which a rotatable α and an orthogonal blocking α receive the greatest attention. Box and Hunter (1957) discuss and calculate the values for α that achieve rotatability, which is a way to stabilize prediction variance of the design. They also give the values for α that make the design orthogonally blocked, where the estimates of the model coefficients remain the same even when the block effects are added to the model. In the last ten years, people have begun to realize the importance of a split-plot structure in industrial experiments. Constructing response surface designs with a split-plot structure is a hot research area now. In this dissertation, Box and Hunters’ choice of α for rotatablity and orthogonal blocking is extended to central composite designs with a split-plot structure. -
Effect of Design Selection on Response Surface Performance
NASA Contractor Report 4520 Effect of Design Selection on Response Surface Performance William C. Carpenter University of South Florida Tampa, Florida Prepared for Langley Research Center under Grant NAG1-1378 National Aeronautics and Space Administration Office of Management Scientific and Technical Information Program 1993 Table of Contents lo Introduction ................................................. 1 1.10uality Qf Fit ............................................. 2 1.].1 Fit at the designs .................................... 2 1.1.2 Overall fit ......................................... 3 1.2. Polynomial Approximations .................................. 4 1.2.1 Exactly-determined approximation ....................... 5 1.2.2 Over-determined approximation ......................... 5 1.2.3 Under-determined approximation ........................ 6 1,3 Artificial Neural Nets ....................................... 7 1 Levels of Designs ............................................. 1O 2.1 Taylor Series Approximation ................................. 10 2,2 Example ................................................ 13 2.3 Conclu_iQn .............................................. 18 B Standard Designs ............................................ 19 3.1 Underlying Principle ....................................... 19 3.2 Statistical Concepts ....................................... 20 3.30rthogonal Designs ........................................ 23 ........................................... 24 3.3.1.1 Example of Scaled Designs: .................... -
Concepts Underlying the Design of Experiments
Concepts Underlying the Design of Experiments March 2000 University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Contents 1. Introduction 3 2. Specifying the objectives 5 3. Selection of treatments 6 3.1 Terminology 6 3.2 Control treatments 7 3.3 Factorial treatment structure 7 4. Choosing the sites 9 5. Replication and levels of variation 10 6. Choosing the blocks 12 7. Size and shape of plots in field experiments 13 8. Allocating treatment to units 14 9. Taking measurements 15 10. Data management and analysis 17 11. Taking design seriously 17 © 2000 Statistical Services Centre, The University of Reading, UK 1. Introduction This guide describes the main concepts that are involved in designing an experiment. Its direct use is to assist scientists involved in the design of on-station field trials, but many of the concepts also apply to the design of other research exercises. These range from simulation exercises, and laboratory studies, to on-farm experiments, participatory studies and surveys. What characterises the design of on-station experiments is that the researcher has control of the treatments to apply and the units (plots) to which they will be applied. The results therefore usually provide a detailed knowledge of the effects of the treat- ments within the experiment. The major limitation is that they provide this information within the artificial "environment" of small plots in a research station. A laboratory study should achieve more precision, but in a yet more artificial environment. Even more precise is a simulation model, partly because it is then very cheap to collect data. -
The Theory of the Design of Experiments
The Theory of the Design of Experiments D.R. COX Honorary Fellow Nuffield College Oxford, UK AND N. REID Professor of Statistics University of Toronto, Canada CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. C195X/disclaimer Page 1 Friday, April 28, 2000 10:59 AM Library of Congress Cataloging-in-Publication Data Cox, D. R. (David Roxbee) The theory of the design of experiments / D. R. Cox, N. Reid. p. cm. — (Monographs on statistics and applied probability ; 86) Includes bibliographical references and index. ISBN 1-58488-195-X (alk. paper) 1. Experimental design. I. Reid, N. II.Title. III. Series. QA279 .C73 2000 001.4 '34 —dc21 00-029529 CIP This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. -
Statistical Process Control Tools: a Practical Guide for Jordanian Industrial Organizations
Volume 4, Number 6, December 2010 ISSN 1995-6665 JJMIE Pages 693 - 700 Jordan Journal of Mechanical and Industrial Engineering www.jjmie.hu.edu.jo Statistical Process Control Tools: A Practical guide for Jordanian Industrial Organizations Rami Hikmat Fouad*, Adnan Mukattash Department of Industrial Engineering, Hashemite University, Jordan. Abstract The general aim of this paper is to identify the key ingredients for successful quality management in any industrial organization. Moreover, to illustrate how is it important to realize the intergradations between Statistical Process Control (SPC) is seven tools (Pareto Diagram, Cause and Effect Diagram, Check Sheets, Process Flow Diagram, Scatter Diagram, Histogram and Control Charts), and how to effectively implement and to earn the full strength of these tools. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialized in producing steel. Flow process chart was constructed, Check Sheets were designed, Pareto Diagram, scatter diagrams, Histograms was used. The vital few problems were identified; it was found that the steel tensile strength is the vital few problem and account for 72% of the total results of the problems. The principal aim of the project is to train quality team on how to held an effective Brainstorming session and exploit these data in cause and effect diagram construction. The major causes of nonconformities and root causes of the quality problems were specified, and possible remedies were proposed. © 2010 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved Keywords: Statistical Process Control, Check sheets, Process Flow Diagram, Pareto Diagram, Histogram, Scatter Diagram, Control Charts, Brainstorming, and Cause and Effect Diagram. -
La Brea and Beyond: the Paleontology of Asphalt-Preserved Biotas
La Brea and Beyond: The Paleontology of Asphalt-Preserved Biotas Edited by John M. Harris Natural History Museum of Los Angeles County Science Series 42 September 15, 2015 Cover Illustration: Pit 91 in 1915 An asphaltic bone mass in Pit 91 was discovered and exposed by the Los Angeles County Museum of History, Science and Art in the summer of 1915. The Los Angeles County Museum of Natural History resumed excavation at this site in 1969. Retrieval of the “microfossils” from the asphaltic matrix has yielded a wealth of insect, mollusk, and plant remains, more than doubling the number of species recovered by earlier excavations. Today, the current excavation site is 900 square feet in extent, yielding fossils that range in age from about 15,000 to about 42,000 radiocarbon years. Natural History Museum of Los Angeles County Archives, RLB 347. LA BREA AND BEYOND: THE PALEONTOLOGY OF ASPHALT-PRESERVED BIOTAS Edited By John M. Harris NO. 42 SCIENCE SERIES NATURAL HISTORY MUSEUM OF LOS ANGELES COUNTY SCIENTIFIC PUBLICATIONS COMMITTEE Luis M. Chiappe, Vice President for Research and Collections John M. Harris, Committee Chairman Joel W. Martin Gregory Pauly Christine Thacker Xiaoming Wang K. Victoria Brown, Managing Editor Go Online to www.nhm.org/scholarlypublications for open access to volumes of Science Series and Contributions in Science. Natural History Museum of Los Angeles County Los Angeles, California 90007 ISSN 1-891276-27-1 Published on September 15, 2015 Printed at Allen Press, Inc., Lawrence, Kansas PREFACE Rancho La Brea was a Mexican land grant Basin during the Late Pleistocene—sagebrush located to the west of El Pueblo de Nuestra scrub dotted with groves of oak and juniper with Sen˜ora la Reina de los A´ ngeles del Rı´ode riparian woodland along the major stream courses Porciu´ncula, now better known as downtown and with chaparral vegetation on the surrounding Los Angeles. -
Argus: Interactive a Priori Power Analysis
© 2020 IEEE. This is the author’s version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: xx.xxxx/TVCG.201x.xxxxxxx/ Argus: Interactive a priori Power Analysis Xiaoyi Wang, Alexander Eiselmayer, Wendy E. Mackay, Kasper Hornbæk, Chat Wacharamanotham 50 Reading Time (Minutes) Replications: 3 Power history Power of the hypothesis Screen – Paper 1.0 40 1.0 Replications: 2 0.8 0.8 30 Pairwise differences 0.6 0.6 20 0.4 0.4 10 0 1 2 3 4 5 6 One_Column Two_Column One_Column Two_Column 0.2 Minutes with 95% confidence interval 0.2 Screen Paper Fatigue Effect Minutes 0.0 0.0 0 2 3 4 6 8 10 12 14 8 12 16 20 24 28 32 36 40 44 48 Fig. 1: Argus interface: (A) Expected-averages view helps users estimate the means of the dependent variables through interactive chart. (B) Confound sliders incorporate potential confounds, e.g., fatigue or practice effects. (C) Power trade-off view simulates data to calculate statistical power; and (D) Pairwise-difference view displays confidence intervals for mean differences, animated as a dance of intervals. (E) History view displays an interactive power history tree so users can quickly compare statistical power with previously explored configurations. Abstract— A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to calculate the statistical power of a given experiment design. -
Design of Experiments and Data Analysis,” 2012 Reliability and Maintainability Symposium, January, 2012
Copyright © 2012 IEEE. Reprinted, with permission, from Huairui Guo and Adamantios Mettas, “Design of Experiments and Data Analysis,” 2012 Reliability and Maintainability Symposium, January, 2012. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of ReliaSoft Corporation's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. 2012 Annual RELIABILITY and MAINTAINABILITY Symposium Design of Experiments and Data Analysis Huairui Guo, Ph. D. & Adamantios Mettas Huairui Guo, Ph.D., CPR. Adamantios Mettas, CPR ReliaSoft Corporation ReliaSoft Corporation 1450 S. Eastside Loop 1450 S. Eastside Loop Tucson, AZ 85710 USA Tucson, AZ 85710 USA e-mail: [email protected] e-mail: [email protected] Tutorial Notes © 2012 AR&MS SUMMARY & PURPOSE Design of Experiments (DOE) is one of the most useful statistical tools in product design and testing. While many organizations benefit from designed experiments, others are getting data with little useful information and wasting resources because of experiments that have not been carefully designed. Design of Experiments can be applied in many areas including but not limited to: design comparisons, variable identification, design optimization, process control and product performance prediction. Different design types in DOE have been developed for different purposes. -
Cause and Effect Diagrams
Online Student Guide Cause and Effect Diagrams OpusWorks 2019, All Rights Reserved 1 Table of Contents LEARNING OBJECTIVES ....................................................................................................................................4 INTRODUCTION ..................................................................................................................................................4 WHAT IS A “ROOT CAUSE”? ......................................................................................................................................................... 4 WHAT IS A “ROOT CAUSE ANALYSIS”?...................................................................................................................................... 4 ADDRESSING THE ROOT CAUSE .................................................................................................................................................. 5 ROOT CAUSE ANALYSIS: THREE BASIC STEPS ........................................................................................................................ 5 CAUSE AND EFFECT TOOLS .......................................................................................................................................................... 6 FIVE WHYS ...........................................................................................................................................................6 THE FIVE WHYS ............................................................................................................................................................................