Crystal orientation effects in scratch testing with a spherical indenter

Spherical scratch tests were conducted in individual grains of a randomly oriented polycrystalline body-centered-cubic (bcc) Ti-Nb alloy. For each grain, scratch tests were conducted at four different levels of normal load, which resulted in varying amounts of plastic strain during indentation. The results show a dependence of the horizontal load component on the crystallographic orientation and on the amount of plastic strain. The component of the horizontal force that resulted from plastic deformation was found to correlate with the active slip systems for the particular grain orientation. © 2010 Materials Research Society.

Advances in data envelopment analysis

Since its introduction in 1978, data envelopment analysis (DEA) has become one of the preeminent nonparametric methods for measuring efficiency and productivity of decision making units (DMUs). Charnes et al. (1978) provided the original DEA constant returns to scale (CRS) model, later extended to variable returns to scale (VRS) by Banker et al. (1984). These ‘standard’ models are known by the acronyms CCR and BCC, respectively, and are now employed routinely in areas that range from assessment of public sectors, such as hospitals and health care systems, schools, and universities, to private sectors, such as banks and financial institutions (Emrouznejad et al. 2008; Emrouznejad and De Witte 2010). The main objective of this volume is to publish original studies that are beyond the two standard CCR and BCC models with both theoretical and practical applications using advanced models in DEA.

A bi-objective weighted model for improving the discrimination power in MCDEA

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. © 2013 Elsevier B.V. All rights reserved.

Strengthening mechanisms in nanostructured copper/304 stainless steel multilayers

Nanostructured Cu/304 stainless steel (SS) multilayers were prepared by magnetron sputtering. 304SS has a face-centered-cubic (fcc) structure in bulk. However, in the Cu/304SS multilayers, the 304SS layers exhibit the fcc structure for layer thickness of =5 nm in epitaxy with the neighboring fcc Cu. For 304SS layer thickness larger than 5 nm, body-centered-cubic (bcc) 304SS grains grow on top of the initial 5 nm fcc SS with the Kurdjumov-Sachs orientation relationship between bcc and fcc SS grains. The maximum hardness of Cu/304SS multilayers is about 5.5 GPa (factor of two enhancement compared to rule-of-mixtures hardness) at a layer thickness of 5 nm. Below 5 nm, hardness decreases with decreasing layer thickness. The peak hardness of fcc/fcc Cu/304SS multilayer is greater than that of Cu/Ni, even though the lattice-parameter mismatch between Cu and Ni is five times greater than that between Cu and 304SS. This result may primarily be attributed to the higher interface barrier stress for single-dislocation transmission across the {111} twinned interfaces in Cu/304SS as compared to the {100} interfaces in Cu/Ni.

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.