Design of Engineering Experiments

8 Key excerpts on "Design of Engineering Experiments"

• 

  eBook - PDF

  Robust Design for Quality Engineering and Six Sigma

  • Sung H Park, Jiju Antony;;;(Authors)
  • 2008(Publication Date)
  • WSPC

    (Publisher)

  Taguchi (1987) defined the design of experiments as a general technique for maximizing the efficiency of acquisition of technical information by experiment. From these definitions we can understand that the 88 Robust Design for Quality Engineering and Six Sigma essence of experimental design is the scientific management of information acquisition by experiment. We may view experimentation a part of the scientific iterative learning process and as one of the ways we learn about how systems or processes work. As Box, Hunter and Hunter (1978) noted, we learn through a series of activities in which we make conjectures about a process, perform experiments to generate data from the process, and then use the information from the exper-iment to establish new conjectures, which lead to new experiments, and so on. The design of experiments plays a major role in many engineering activities. For instance, the design of experiments is used for: 1. Improving the performance of a manufacturing process. The optimal values of process variables can be economically determined by application of experimental designs. 2. The development of new processes. The application of experimental design methods early in process development can result in reduced development time, reduced variability about target requirements and enhanced process yields. 3. Screening important factors. Fractional factorial designs using orthogonal arrays are often used in order to screen important factors that impact product performance. This will help to enhance the efficiency of research activities. 4. Engineering design activities such as evaluation of material alterations, comparison of basic design configurations, and selection of design parameters so that the product is robust to a wide variety of field conditions. 5. Empirical model building to find out the functional relationship between the performance variable and the influence variables (factors or design/process parameters).

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Design of Experiments for Reliability Achievement

  • Steven E. Rigdon, Rong Pan, Douglas C. Montgomery, Connie M. Borror, Laura Freeman(Authors)
  • 2022(Publication Date)
  • Wiley

    (Publisher)

  89 Part II Design of Experiments 91 4 Fundamentals of Experimental Design 4.1 Introduction to Experimental Design Investigators perform experiments in virtually all fields of inquiry, usually to discover something about a particular process or system or to confirm previous experience or theory. Each experimental run is a test. More formally, we can define an experiment as a series of tests or runs in which purposeful changes are made to the input variables of a process or system so that we may observe and identify the reasons for changes that may be observed in the output response. We may want to determine which input variables are responsible for the observed changes in the response, develop a model relating the response to the important input variables, and use this model for process or system improvement or other decision-making. This book is about planning and conducting experiments and about analyzing the resulting data so that valid and objective conclusions are obtained. Our focus is on experiments that deal with the reliability of components, products, and systems. This is an important aspect of technology commercial-ization and product realization activities. Designed experiments have extensive applications in many other areas, such as new product design and formulation, manufacturing process development, and process improvement. There are also many applications of designed experiments in a nonmanufacturing or non-product-development setting, such as marketing, service operations, and general business operations. Designed experiments are a key technology for innovation. Both breakthrough innovation and incremental innovation activities can benefit from the effective use of designed experiments. While this book focuses on reliability applications, Montgomery (2020) provides a more general introduction with applications to many of these other areas.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Design and Analysis of Experiments

  • Douglas C. Montgomery(Author)
  • 2019(Publication Date)
  • Wiley

    (Publisher)

  Each experimental run is a test. More formally, we can define an experiment as a test or series of runs in which purposeful changes are made to the input variables of a process or system so that we may observe and identify the reasons for changes that may be observed in the output response. We may want to determine which input variables are responsible for the observed changes in the response, develop a model relating the response to the important input variables, and use this model for process or system improvement or other decision-making. This book is about planning and conducting experiments and about analyzing the resulting data so that valid and objective conclusions are obtained. Our focus is on experiments in engineering and science. Experimentation 1 2 Chapter 1 Introduction plays an important role in technology commercialization and product realization activities, which consist of new product design and formulation, manufacturing process development, and process improvement. The objective in many cases may be to develop a robust process, that is, a process affected minimally by external sources of variability. There are also many applications of designed experiments in a nonmanufacturing or non-product-development setting, such as marketing, service operations, and general business operations. Designed experiments are a key technology for innovation. Both breakthrough innovation and incremental innovation activities can benefit from the effective use of designed experiments. As an example of an experiment, suppose that a metallurgical engineer is interested in studying the effect of two different hardening processes, oil quenching and saltwater quenching, on an aluminum alloy. Here the objective of the experimenter (the engineer) is to determine which quenching solution produces the maximum hardness for this particular alloy.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Statistics and Probability for Engineering Applications

  • William DeCoursey(Author)
  • 2003(Publication Date)
  • Newnes

    (Publisher)

  CHAPTER 11 Introduction to Design of Experiments This chapter is largely independent of previous chapters, although some previous vocabulary is used here. Professional engineers in industry or in research positions are very frequently respon-sible for devising experiments to answer practical problems. There are many pitfalls in the design of experiments, and on the other hand there are well-tried methods which can be used to plan experiments that will give the engineer the maximum information and often more reliable information for a particular amount of effort. Thus, we need to consider some of the more important factors involved in the design of experiments. Complete discussion of design of experiments will be beyond the scope of this book, so the contents of this chapter will be introductory in nature. We have seen in section 9.2.4 that more information can be gained in some cases by designing experiments to use the paired t-test rather than the unpaired t -test. In many other cases there is a similar advantage in designing experiments carefully. There are complications in many experiments in industry (and also in many research programs) that are not found in most undergraduate engineering laborato-ries. First, several different factors may be present and may affect the results of the experiments but are not readily controlled. It may be that some factors affect the results but are not of prime interest: they are interfering factors, or lurking factors. Often these interfering factors can not be controlled at all, or perhaps they can be controlled only at considerable expense. Very frequently, not all the factors act independently of one another. That is, some of the factors interact in the sense that a higher value of one factor makes the results either more or less sensitive to another factor. We have to consider these complicating factors in planning the set of experi-ments.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Experiment Design for Environmental Engineering

  Methods and Examples

  • Francis J. Hopcroft, Abigail Charest(Authors)
  • 2022(Publication Date)
  • CRC Press

    (Publisher)

  2 How to Design an Engineering Experiment DOI: 10.1201/9781003184249-2 The fundamental design of an experiment contains several distinct design elements. Those include the question to be answered; the variables involved; how the variables will be adjusted; the potential interferences that can occur; how the investigator intends to minimize or avoid the effects of those interferences, or to account for them in the experimental data; and what theoretical outcomes are expected. Once the data are generated, how those data are interpreted and how the results are presented will go a long way to validating the outcomes. Note that not all experiments succeed. If they did, there would be no need to do an experiment because the outcome could be accurately predicted. It is important, therefore, to recognize that failure is an acceptable component of investigation. That recognition will minimize the tendency to interpret data in a way that supports the expected outcome and to reject data that do not support that outcome. If the data are not what is expected, it should be assumed that the data are correct and that the theory is wrong or that there was an error in the experiment design or conduct. The investigator then needs to try to figure out why the theory or the experiment was wrong and how to redo the experiment to account for the new thinking. Certainly, equipment will occasionally fail, people will do things in a manner inconsistent with the planned protocol of the experiment, reagents will become contaminated, and all sorts of other things will go wrong with experiments. The data that are generated are always correct for the experiment that was done. If those data do not reflect the expected outcome, either the theory is wrong or the experiment incorporated some unknown flaw

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Statistics for Earth and Environmental Scientists

  • John H. Schuenemeyer, Lawrence J. Drew(Authors)
  • 2011(Publication Date)
  • Wiley

    (Publisher)

  Chapter 9 Design of Experiments 9.1 Introduction

  There are two broad categories of design: sampling design and design of experiments. In the earth sciences, design of experiments is often overlooked. There have been many large, costly projects for which, with minimal planning, crews have been sent into the field to collect data. Too often these efforts result in data that are less valuable than if proper planning and design had been done beforehand. Design involves selecting samples to satisfy the requirements of the problem under consideration. A principal benefit of design is to make the results of a study more generalizable and thus more useful than if samples are collected helter-skelter. Design criteria may include achieving a needed level of precision, increasing the efficiency of estimation, minimizing the cost of sampling, and/or making the results robust to departures in assumptions.

  Sampling design is typically used in surveys and field studies (also called observational studies ), where control of the factors that may influence the outcome of the experiment is not always possible. Field studies include sampling people, animals, airborne contaminants, soils, and permafrost thickness. Conversely, design of experiments usually occurs in a laboratory or pilot plant under controlled conditions. An example is the study of the effect of waves on shorelines in a wave laboratory, where amplitude, frequency, depth, and other attributes are controlled and set to fairly precise values. Many of the traditional design of experiments have been developed to study agricultural, genetic, and industrial processes.

  Data collection in most earth science applications is costly and time consuming. Often there is only one opportunity to collect the data. Thus, proper design is paramount. 9.2 Sampling Designs

  Most sampling designs in the earth sciences involve a spatial component. Indeed, this is also true in human geography, where sampling of individuals or households may be of interest. In most earth science disciplines, sampling is driven by the need to estimate some quantity over a spatial region. For example, a petroleum geologist may wish to develop a design to site wells or seismic lines to find oil. A geochemist may wish to create a map of the regional deposition of arsenic or mercury. A climatologist may need to understand patterns of precipitation in the Amazon.

  Sign up to read

  Learn more about book
• 

  eBook - ePub

  Extrusion

  The Definitive Processing Guide and Handbook

  • Harold F. Giles Jr, Eldridge M. Mount III, John R. Wagner Jr., John R. Wagner, Jr.(Authors)
  • 2004(Publication Date)
  • William Andrew

    (Publisher)

  25 Design of Experiments

  Design of experiments, referred to as DOE, is a systematic approach to understanding how process and product parameters affect response variables such as processability, physical properties, or product performance. It is a tool similar to any other tool, device, or procedure that makes the job easier. Unlike quality, mechanical, or process tools, DOE is a mathematical tool used to define the importance of specific processing and/or product variables, and how to control them to optimize the system performance while maximizing properties. DOE uses statistical methodology to analyze data and predict product property performance under all possible conditions within the limits selected for the experimental design. In addition to understanding how a particular variable affects product performance, interactions between different process and product variables are identified. Design of experiments is a technique or procedure to generate the required information with the minimum amount of experimentation, using

  • Experimental limits • Specific experimental conditions • Mathematical analysis to predict the response at any point within the experimental limits

  DOE is used to determine which factors or variables and interactions are significant in contributing to the effect being measured, and those variables and interactions that are insignificant and don’t contribute to either a particular product property or processing condition. Using DOE saves both time and money by providing a useable understanding of the properties and process. The best time to use a DOE is during new product or process development, existing product or process optimization, and while solving technical problems when more than one variable is present. Where is DOE used? For solving any technical problem when you want to fully understand the response to different process or product variables that can be changed or controlled during the experimentation. Problems in industry typically get recycled from time to time, because an idea that was originally a good idea couldn’t be accomplished because the technology was not available or the project was aborted because of lack of time and resources. Since the original work, new technology has been developed or resources are available that now make a solution possible. The advantage of using a DOE approach is systematic data are generated, summarized, and evaluated to definitively determine whether a project should be carried forward or if it is fundamentally impossible to resolve and needs to be dropped. Regardless of whether the DOE results are positive (experiment showed desired response) or negative (experiment showed undesired response), it is important to complete the project and document the results so the project will not be recycled at a later time. The DOE results provide an understanding of the processing and/or product parameters and their interactions over the experimental space studied.

  Sign up to read

  Learn more about book
• 

  eBook - PDF

  Design of Experiments

  A Modern Approach

  • Bradley Jones, Douglas C. Montgomery(Authors)
  • 2019(Publication Date)
  • Wiley

    (Publisher)

  Specifically, we usually want to deter- mine which input variables are responsible for the observed changes in response to develop a model relating the response to the important input variables and to use this model for making decisions about the system. The person conducting the experiment is called the experimenter. This book is about planning and conducting experiments as well as analyzing the resulting data so that valid and objective conclusions are obtained. Our focus is on experiments in engineering, science, and business. Experimentation plays an important role in technology commercializa- tion and product realization activities, which consist of new product design and formulation, manufacturing process development, and process improvement. The objective in many cases may be to develop a robust process, that is, a process affected minimally by external sources of variability. There are also many applications of designed experiments in a nonmanufactur- ing or non-product-development setting, such as marketing, service operations, e-commerce, and general business operations. As an example of an experiment, suppose that a biomedical engineer is designing a new pump for the intravenous delivery of a drug. The pump should deliver a constant quantity or dose of the drug over a specified period of time. The primary design parameter of the pump that the engineer is considering at this stage is the diameter of the pump cylinder. Two diameters are feasible: 3/16th in. and 1/4th in. Here, the objective of the experimenter is to determine which diameter produces the appropriate flow rate for the particular type of pump. In this type of experiment, a common approach would be to build several prototypes and subject each prototype to a standard test to determine the effect of the cylinder diameter on the drug delivery rate. The average delivery rate 1

  Sign up to read

  Learn more about book