Martin Arvidsson

A review of practices for robust design methodology

Robust design methodology (RDM) comprises systematic efforts to achieve insensitivity of products or processes to sources of unwanted variation. In this article, the literature is reviewed and practices that facilitate industrial use of RDM by providing concrete ideas to generate robust designs are identified. To date the literature has focused mainly on statistical techniques useful for creating robust designs, that is, solutions that are insensitive to sources of unwanted variation, while scope and overall framework have been less emphasised, causing an ambiguity in these respects. One practice identified for insensitivity to variation sources is to exploit non-linearities (between response and control factors) and interactions (between noise and control factors), and suitable tools for accomplishing this can be design of experiments or simulation techniques. As systematic RDM efforts are based on an awareness of variation and are beneficial in all design stages, the review also focuses on these two aspects of RDM.

Use and knowledge of robust design methodology: a survey of Swedish industry

In this paper, a survey concerning the knowledge and use of robust design methodology in 87 Swedish manufacturing companies is presented. The results show that only 28% of the companies in the study are familiar with robust design methodology, and 17% use the methodology. However, methods that can be useful in robust design methodology are often applied in Swedish industry. The results of the survey also reveal that application of robust design methodology, involvement in a Six Sigma program, QS 9000 certification, and the size of the company seem to be correlated with an increased use of these methods.

A Robustness Approach to Reliability

Reliability of products is here regarded with respect to failure avoidance rather than probability of failure. To avoid failures, we emphasize variation and suggest some powerful tools for handling failures due to variation. Thus, instead of technical calculation of probabilities from data that usually are too weak for correct results, we emphasize the statistical thinking that puts the designers focus on the critical product functions. Making the design insensitive to unavoidable variation is called robust design and is handled by (i) identification and classification of variation, (ii) design of experiments to find robust solutions, and (iii) statistically based estimations of proper safety margins. Extensions of the classical failure mode and effect analysis (FMEA) are presented. The first extension consists of identifying failure modes caused by variation in the traditional bottom–up FMEA analysis. The second variation mode and effect analysis (VMEA) is a top–down analysis, taking the product characteristics as a starting point and analyzing how sensitive these characteristics are to variation. In cases when there is sufficient detailed information of potential failure causes, the VMEA can be applied in its most advanced mode, the probabilistic VMEA. Variation is then measured as statistical standard deviations, and sensitivities are measured as partial derivatives. This method gives the opportunity to dimension tolerances and safety margins to avoid failures caused by both unavoidable variation and lack of knowledge regarding failure processes.

Identification of Factors Influencing Dispersion in Split-Plot Experiments

As split-plot designs are commonly used in robust design it is important to identify factors in these designs that influence the dispersion of the response variable. In this article, the Bergman-Hynén method, developed for identification of dispersion effects in unreplicated experiments, is modified to be used in the context of split-plot experiments. The modification of the Bergman-Hynén method enables identification of factors that influence specific variance components in unreplicated two-level fractional factorial splitplot experiments. An industrial example is used to illustrate the proposed method.

Early interactions between leukocytes and three different potentially bioactive titanium surface modifications

The aim of the present study was to compare the early interactions between leukocytes and three different surface modifications, suggested as bioactive. Blasted titanium discs were modified by alkali and heat treatment, sodium fluoride treatment, or hydroxyapatite coating. A number of these discs were also immersed in simulated body fluid (SBF) for a week, a treatment which yielded high levels of calcium and phosphate on each surface type. The specimens were exposed for human venous blood for 32 minutes and the respiratory burst response was measured in terms of reactive oxygen species with a luminometer, and coverage of viable cells with a fluorescence microscope after staining steps. The topography, morphology, and chemistry of the surfaces were evaluated with optical interferometry and scanning electron microscopy/energy dispersive X-ray analysis (SEM/EDX). A high respiratory burst response was found for HA coated titanium in comparison with the other surface groups (p < 0.0005). The SBF immersion resulted in an increased respiratory burst response (p < 0.0005) and removed statistically significant differences between the surface groups. Thus, the results in the present study indicate that different titanium surface modifications influence the early inflammatory response differently, and that calcium phosphate compounds increase the inflammatory response.