Antony Design of Experiments for Engineers and Scientists

2. Auflage 2014
ISBN: 978-0-08-099419-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 220 Seiten

The tools and techniques used in Design of Experiments (DoE) have been proven successful in meeting the challenge of continuous improvement in many manufacturing organisations over the last two decades. However research has shown that application of this powerful technique in many companies is limited due to a lack of statistical knowledge required for its effective implementation.Although many books have been written on this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as through using statistical methods and readers will find the concepts in this book both familiar and easy to understand.This new edition includes a chapter on the role of DoE within Six Sigma methodology and also shows through the use of simple case studies its importance in the service industry. It is essential reading for engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic. - Written in non-statistical language, the book is an essential and accessible text for scientists and engineers who want to learn how to use DoE - Explains why teaching DoE techniques in the improvement phase of Six Sigma is an important part of problem solving methodology - New edition includes a full chapter on DoE for services as well as case studies illustrating its wider application in the service industry

Jiju Antony is a professor of Industrial and Systems Engineering and certified LSS Master Black Belt in the department of Industrial and Systems Engineering at Khalifa University, Abu Dhabi, UAE. He has a proven track record for conducting internationally leading research in the field of quality management, quality engineering, continuous improvement, and operational excellence. Professor Antony has authored over 500 journal, conference, and white papers; 14 textbooks; and two conference proceedings. He is the Editor in Chief of the International Journal of Lean Six Sigma, Editor in Chief of the International Journal of Quality and Reliability Management, and Associate Editor of the TQM Journal and BE Journal. Professor Antony has worked on a number of consultancy projects with several blue-chip companies such as Rolls-Royce, Bosch, Siemens, Parker Pen, Siemens, Johnson and Johnson, GE Plastics, Ford, Scottish Power, Tata Motors, Thales, Nokia, Philips, General Electric, NHS, Glasgow City Council, ACCESS, Scottish Water, Police Scotland, university sectors, and a number of small- and medium-sized enterprises.

Autoren/Hrsg.

Antony, Jiju

Leseproben

2 Fundamentals of Design of Experiments
This chapter introduces the basic principles of Design of Experiments such as randomisation, blocking and replication. It goes on and then introduces the concept of degrees of freedom and its significance in the context of industrial experiments. This is followed by the introduction to confounding or aliasing structure and the concept of design resolution. The importance of measurement system and some of the fundamental metrology considerations are emphasised in this chapter. The last part of the chapter is focused on the selection of quality characteristics and some tips for the selection of such characteristics for making industrial experiments successful in organisations. Keywords
Process; basic principles; randomisation; replication; blocking; degrees of freedom; confounding; resolution; design resolution; measurement system capability; quality characteristics 2.1 Introduction
In order to properly understand a designed experiment, it is essential to have a good understanding of the process. A process is the transformation of inputs into outputs. In the context of manufacturing, inputs are factors or process variables such as people, materials, methods, environment, machines, procedures, etc. and outputs can be performance characteristics or quality characteristics of a product. Sometimes, an output can also be referred to as response. In the context of Six Sigma, this is often referred to as critical-to-quality characteristics. In performing a designed experiment, we will intentionally make changes to the input process or machine variables (or factors) in order to observe corresponding changes in the process output. If we are dealing with a new product development process, we will make changes to the design parameters in order to make the design performance insensitive to all sources of variation (Montgomery, 2001). The information gained from properly planned, executed and analysed experiments can be used to improve functional performance of products, to reduce the scrap rate or rework rate, to reduce product development cycle time, to reduce excessive variability in production processes, to improve throughput yield of processes, to improve the capability of processes, etc. Let us suppose that an experimenter wishes to study the influence of five variables or factors on an injection moulding process. Figure 2.1 illustrates an example of an injection moulding process with possible inputs and outputs. The typical outputs of an injection moulding process can be length, thickness, width etc. of an injection moulded part. However, these outputs can be dependant on a number of input variables such as mould temperature, injection pressure, injection speed, etc. which could have an impact on the above mentioned outputs. The purpose of a designed experiment is to understand the relationship between a set of input variables and an output or outputs. Figure 2.1 Illustration of an injection moulding process. Now consider a wave soldering process where the output is the number of solder defects. The possible input variables which might influence the number of solder defects are type of flux, type of solder, flux coating depth, solder temperature, etc. More recently, DOE has been accepted as a powerful technique in the service industry and there have been some major achievements. For instance, a credit card company in the US has used DOE to increase the response rate to their mailings. They have changed the colour, envelope size, character type and text within the experiment. In real-life situations, some of the process variables or factors can be controlled fairly easily and some of them are difficult or expensive to control during normal production or standard conditions. Figure 2.2 illustrates a general model of a process or system. Figure 2.2 General model of a process/system. In Figure 2.2, output(s) are performance characteristics which are measured to assess process/product performance. Controllable variables (represented by X’s) can be varied easily during an experiment and such variables have a key role to play in the process characterisation. Uncontrollable variables (represented by Z’s) are difficult to control during an experiment. These variables or factors are responsible for variability in product performance or product performance inconsistency. It is important to determine the optimal settings of X’s in order to minimise the effects of Z’s. This is the fundamental strategy of robust design (Roy, 2001). 2.2 Basic Principles of DOE
DOE refers to the process of planning, designing and analysing the experiment so that valid and objective conclusions can be drawn effectively and efficiently. In order to draw statistically sound conclusions from the experiment, it is necessary to integrate simple and powerful statistical methods into the experimental design methodology (Vecchio, 1997). The success of any industrially designed experiment depends on sound planning, appropriate choice of design, statistical analysis of data and teamwork skills. In the context of DOE in manufacturing, one may come across two types of process variables or factors: qualitative and quantitative. For quantitative factors, one must decide on the range of settings and how they are to be measured and controlled during the experiment. For example, in the above injection moulding process, screw speed, mould temperature, etc. are examples of quantitative factors. Qualitative factors are discrete in nature. Type of raw material, type of catalyst, type of supplier, etc. are examples of qualitative factors. A factor may take different levels, depending on the nature of the factor – quantitative or qualitative. A qualitative factor generally requires more levels when compared to a quantitative factor. Here the term ‘level’ refers to a specified value or setting of the factor being examined in the experiment. For instance, if the experiment is to be performed using three different types of raw materials, then we can say that the factor – the type of raw material – has three levels. In the DOE terminology, a trial or run is a certain combination of factor levels whose effect on the output (or performance characteristic) is of interest. The three principles of experimental design, namely randomisation, replication and blocking, can be utilised in industrial experiments to improve the efficiency of experimentation (Antony, 1997). These principles of experimental design are applied to reduce or even remove experimental bias. It is important to note that large experimental bias could result in wrong optimal settings or, in some cases, could mask the effect of the really significant factors. Thus an opportunity for gaining process understanding is lost, and a primary element for process improvement is overlooked. 2.2.1 Randomisation
We all live in a non-stationary world, a world in which noise factors (or external disturbances) will never stay still. For instance, the manufacture of a metal part is an operation involving people, machines, measurement, environment, etc. The parts of the machine are not fixed entities; they wear out over a period of time and their accuracy is not constant over time. The attitudes of the people who operate the machines vary from time to time. If you believe your system or process is stable, you do not then need to randomise the experimental trials. On the other hand, if you believe your process is unstable and without randomisation, the results will be meaningless and misleading; you then need to think about randomisation of experimental trials (Box, 1990). If the process is very unstable and randomisation would make your experiment impossible, then do not run the experiment. You may have to look at process control methods to bring your process into a state of statistical control. While designing industrial experiments, there are factors, such as power surges, operator errors, fluctuations in ambient temperature and humidity, raw material variations, etc. which may influence the process output performance because they are often expensive or difficult to control. Such factors can adversely affect the experimental results and therefore must be either minimised or removed from the experiment. Randomisation is one of the methods experimenters often rely on to reduce the effect of experimental bias. The purpose of randomisation is to remove all sources of extraneous variation which are not controllable in real-life settings (Leon et al., 1993). By properly randomising the experiment, we assist in averaging out the effects of noise factors that may be present in the process. In other words, randomisation can ensure that all levels of a factor have an equal chance of being affected by noise factors (Barker, 1990). Dorian Shainin accentuates the importance of randomisation as ‘experimenters’ insurance policy’. He pointed out that ‘failure to randomise the trial conditions mitigates the statistical validity of an experiment’. Randomisation is usually done by drawing numbered cards from a well-shuffled pack of cards, by drawing numbered balls from a well-shaken container or by using tables of random numbers. Sometimes experimenters encounter situations where randomisation of experimental trials is difficult to perform due to cost and time constraints. For instance, temperature in a chemical process may be a hard-to-change factor, making complete randomisation of...