The Design of Experiments as a Methodological Framework for the Improvement of Manufacturing Processes

The Design of Experiments as a Methodological Framework for the Improvement of Manufacturing Processes

Sukey Nakasima-López (Universidad Autónoma de Baja California, Mexico), Mydory Oyuky Nakasima-López (Universidad Autónoma de Baja California, Mexico), Karla Frida Madrigal Estrada (Universidad Autónoma de Baja California, Mexico) and Erika Beltrán Salomón (Universidad Autónoma de Baja California, Mexico)
DOI: 10.4018/978-1-7998-1518-1.ch012
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Industries seek changes in manufacturing processes by designing or redesigning them, to improve the quality of products, reduce costs and cycle times, change materials, modify methods, design innovative products, among others. Facing these demands requires a powerful methodological framework known as Design of Experiments. Most of literature focuses on the application of these techniques in the areas of statistics and quality. However, the variety of problems facing engineers in industry is wide and includes different levels of complexity, ranging from the design of new products, improvement of design, maintenance, control and improvement of manufacturing processes, maintenance and repair of products, among others. This chapter provides the reader different applications of this methodology in industry, to highlight the importance and benefits of knowing and applying these techniques. It will present the application of this methodology in a general way and finally, it will discuss different case studies that use this methodology in industry.
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The manufacturing industry faces constant challenges, mainly in the manufacture of innovative and high-quality products, due to the current conditions of the global market, and the high competitiveness of the organizations. Based on the concept of manufacturing, which is defined as: “the transformation of materials and information into goods for the human needs satisfaction” (Chryssolouris, 2006), the processes for product development are increasingly complex since the products become more versatile to meet the needs of massive customization (Ong, Yuan, & Nee, 2008). Therefore, it is necessary to make important changes to the manufacturing processes by designing or re-designing them, in order to improve the quality of products, reduce costs and cycle times, change materials, modify methods, design innovative products and highly competitive in the market, among others (Gutiérres Pulido & De la Vara Salazar, 2012).

Different manufacturing phases require experimentation in order to be able to face critical factors such as optimization of available resources, of the execution time and the process performance, as well as variability control, to assure the quality of the product and/or service produced (Tanco, Viles, Ilzarbe, & Álvarez, 2007). A powerful methodological framework that allows to create a multidimensional design space and its interactions between factors that might affect the performance and quality of the process is known as Design of Experiments (DoE) (Davim, 2012). This framework provides us with objective evidence that responds to diverse questions in the different stages of process development, clarifying uncertain aspects that lead us either to the improvements implementation or problem solutions with different complexity levels (Gutiérres Pulido & De la Vara Salazar, 2012). DoE can be understood as a practical methodology, a tool that is composed of a set of techniques with well-defined application phases, which are described below (Murray et al., 2016; Weissman & Anderson, 2015):

  • 1.

    Planning, this phase begins with the recognition of a need and/or problem, as well as the definition of the objectives to be achieved, requires a good level of process knowledge, or an evaluation and comprehensive analysis that allows to identify the factors (variables) involved in the process, as well as, define the expected responses and list all possible interactions between them. This exercise contributes to the construction of an environment where the culture of decision-making is fostered based on experimental design techniques, rather than those based on trial and error, providing greater certainty to the decision making process.

  • 2.

    Design, in this phase the most appropriate DoE is chosen, based mainly on the number of factors that must be studied, the different levels of detail, as well as the objectives to be achieved.

  • 3.

    Conducting, refers to the experimentation, execution and the evaluation of the results obtained.

  • 4.

    Analysis, in this stage the results are analyzed and interpreted, in order to obtain significant conclusions for the decision making process, to reach objectives such as parametric design or to identify the process variables that critically affect its performance, the influence or sensitivity due to variability of the external components, the identification of the different parametric levels that produce an optimal performance, and finally, to identify if the improvement or implementation can be executed in a real environment.

Key Terms in this Chapter

Manufacturing Industry: The economic sector based on the fabrication or processing of products from raw materials and commodities.

Experimentation: The action or process of performing a scientific procedure in order to determine something in and object, process or study phenomenon.

Design of Experiments: Set of techniques, that do not expect the process to send the expected signals, but that it is “manipulated” to provide the information that is required for its improvement.

Process: Set of predetermined and successive phases or procedures to achieve an expected objective or result.

Critical Factors: The main factors that have an impact on the optimization or improvement in the outcome of an experiment.

Statistics: It is a science based on solid mathematical theorems that come from irrefutable logical laws. It consists of the collection, treatment, interpretation, and analysis of a set of data that allows the understanding of a phenomenon and the correct decision making.

Statistical Tools: Set of techniques that allow the process improvement and the reduction of errors for troubleshooting.

Optimization: Practice that allows establishing the right variables involved in a process or experiment to achieve the best results.

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