Improving the practice of experimental design in manufacturing engineering

Design of Experiments (DOE) is a powerful technique for understanding, characterising and modelling products and processes and improving their performance. Whilst the bulk of its literature revolves around how it should be applied, little attention, if any, is devoted to the manner in which it is being implemented in practice particularly in manufacturing. One objective of this study was to bridge this gap by reviewing practical applications in three manufacturing journals. This revealed not only limited use but also multiple deficiencies. Many of these concerned a lack of familiarity with the concept of aliasing; the use of fractional factorial designs and pooling methods to analyse unreplicated trials; and a misunderstanding of the concepts underpinning the use and interpretation of p-values and factorial effects’ importance measures. With respect to aliasing, a novel simple method for generating its pattern is proposed. Besides its ease of application, it can be linked to the three main criteria for measuring the degree of aliasing (maximum resolution, minimum aberration and generalised minimum aberration) in a manner devoid of mathematical complications. Regarding the use of fractional factorial designs and pooling methods, simulation experiments were used to assess the performance of certain experimentation strategies to arrive at the same conclusions had a full factorial trial been performed. In the context of two-level designs, the L\(_{16}\) together with the Pooling Up method or the Half Normal Probability plot yielded a satisfactory performance. Similarly, the strategy of using the Best Subset selection procedure in conjunction with the L\(_{18}\) design was the best among the examined three-level ones. To attain a robust performance, it was found that the use of small designs such as the L\(_8\) and the L\(_9\) should, as far as possible, be avoided. The concepts concerning the use of the p-values and the effect’s importance measures are clarified and to facilitate communication between Engineers, Managers and Statisticians, an importance measure that can be related to three quality engineering techniques is suggested.