THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Hall-Petch relationship in Mg alloys: A review

The mechanism for the high dependence of the Hall-Petch slope for twinning/slip on texture in Mg alloys

Phase transformation and dynamic recrystallization behaviors in a Ti55511 titanium alloy during hot compression

The hot formability of a near-β titanium alloy is studied near the β transus temperature to determine the mechanisms of deformation. Compression tests of Ti-5Al-5Mo-5V-3Cr-1Zr are carried out using a Gleeble®1500 device between 1036 K and 1116 K (763 °C and 843 °C) and strain rates between 0.001 and 10 s−1. The achieved flow data are used to calculate the efficiency of power dissipation, the strain rate sensitivity, and instability parameters derived from different models. Constitutive equations are built using the stress values at the strain of 0.4. Light optical microscopy and EBSD measurements are used to correlate the parameters that describe formability with the microstructure. It is found that hot deformation is achieved by dynamic recovery in the β phase by subgrain formation. Geometric dynamic recrystallization along the β grain boundaries takes place at large deformations, high temperatures, and low strain rates. On the other hand, for high strain rates, continuous dynamic recrystallization by lattice rotation already starts at a local strain of 1. Different phenomenological models are used to predict the flow instabilities, where the flow-softening parameter α
 i provides the best correlation with microstructure as well as the physical understanding. The instabilities observed in this alloy are strongly related to flow localization by adiabatic heat.

Deformation Mechanisms in the Near- β Titanium Alloy Ti-55531

On the hot working characteristics of 2014 Al-20vol% Al2O3 metal matrix composite

Grain growth in calibre rolled Mg–3Al–1Zn alloy and its effect on hardness

Effects of caliber rolling on microstructure and room temperature tensile properties of Mg–3Al–1Zn alloy

Using the test data of α2 titanium aluminide alloy Ti-24Al-11Nb with microstructural observations, studies are made to examine the flow localization concepts as well as the simplified metallurgical stability criterion for delineating the regions of flow instabilities in the processing maps. The optimum hot working conditions for the material are suggested.

Processing maps for hot deformation of α2 aluminide alloy Ti-24Al-11Nb

The microstructural features present in titanium alloys not only vary over a wide range of length scale but are also highly interdependent. It is not possible to experimentally study the variation effect of only one microstructural feature while maintaining others at a constant value. Due to this complexity, there are no physical models available to relate the microstructural features to the mechanical properties. This limitation can be overcome by using artificial neural networks (ANN) which are capable of establishing highly nonlinear and complex relationship that exists between input and output variables. A variety of architectural and training parameters affect the performance of a neural network. Currently, there are no guidelines available to select an appropriate neural network that can be used to solve a particular class of problem. An attempt is made here to circumvent this problem by using Taguchi based design of experiments (DOE) approach. A total of 8 neural network architectural and training parameters have been identified to study (1) their effect on ANN performance and (2) establish a suitable neural network that can be used to establish the complex relationship that exists between various microstructural features—mechanical properties in a near-α titanium alloy. Results obtained indicate that architectural parameters contribute 58 % while the training parameters contribute 35 % to the network performance and the network so identified can establish the complex non-linear relationship between the microstructural features and mechanical properties.

Taguchi Based Optimisation of Artificial Neural Network to Establish a Direct Microstructure: Mechanical Property Correlation in a near-α Titanium Alloy

B. P. Kashyap

Papers

On the hot working characteristics of 2014 Al-20vol% Al2O3 metal matrix composite

Grain growth in calibre rolled Mg–3Al–1Zn alloy and its effect on hardness

Effects of caliber rolling on microstructure and room temperature tensile properties of Mg–3Al–1Zn alloy

Processing maps for hot deformation of α2 aluminide alloy Ti-24Al-11Nb

Taguchi Based Optimisation of Artificial Neural Network to Establish a Direct Microstructure: Mechanical Property Correlation in a near-α Titanium Alloy