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

Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25

Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN)

Lightweight high-strength metal matrix nano-composites (MMNCs) can be used in a wide variety of applications, e.g., aerospace, automotive, and biomedical engineering, owing to their sustainability, increased specific strength/stiffness, enhanced elevated temperature strength, improved wear, or corrosion resistance. A metallic matrix, commonly comprising of light aluminum or magnesium alloys, can be significantly strengthened even by very low weight fractions (~1 wt%) of well-dispersed nanoparticles. This review discusses the recent advancements in the fabrication of metal matrix nanocomposites starting with manufacturing routes and different nanoparticles, intricacies of the underlying physics, and the mechanisms of particle dispersion in a particle-metal composite system. Thereafter, the microstructural influences of the nanoparticles on the composite system are outlined and the theory of the strengthening mechanisms is also explained. Finally, microstructural, mechanical, and tribological properties of the selected MMNCs are discussed as well.

Advanced Metal Matrix Nanocomposites

Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method

Modeling and Optimization of EDM Process Parameters on Machining of Al 7075-B4C MMC Using RSM

Application of response surface method on machining of Al–SiC nano-composites

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process.

Multi-response optimization of machining parameters in hot turning using grey analysis

Stainless steel (Type 316) workpiece was heated by the mixture of Liquid Petroleum Gas (LPG) and oxygen gas, and it was machined in a lathe under different cutting conditions to study the hot machining characteristics. The orthogonal turning operations were carried out on stainless steel (Type 316) using tungsten carbide (WC) cutting tool insert. During machining the cutting speed (Vc), feed rate (fs), depth of cut (a p ), and temperature of the workpiece were varied in the range of 200°C, 400°C, and 600°C. Turning experiments were designed based on the statistical three-level full factorial experimental design techniques. An artificial neural network (ANN) and response surface model (RSM) have been developed, which can predict the surface roughness of the machined workpiece. The experimental results concur well with the results obtained from the predictive models.

T. Senthilvelan

Papers

Modeling and Optimization of EDM Process Parameters on Machining of Al 7075-B4C MMC Using RSM

Application of response surface method on machining of Al–SiC nano-composites

Multi-response optimization of machining parameters in hot turning using grey analysis

Evaluation of Machining Parameters of Hot Turning of Stainless Steel (Type 316) by Applying ANN and RSM

Comparative Investigation on Mechanical Properties of Banana and Sisal Reinforced Polymer based Composites