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

Recent advances in electroless nickel‑boron coatings

Electroless nickel‑boron coatings have been used in various industrial sectors for more than 40 years due to their excellent mechanical and tribological properties, that exceed those of hard chrome, with homogeneous surface finishing, and competitive corrosion resistance. Standard electroless nickel‑boron coatings are usually confined to use in niche applications such as saw blades, firearms, and valves in the oil and gas industry due to the presence of heavy metals from the stabilizing agent in the coated system. However, recent advances in technology have made it possible to remove heavy metals but also to improve the productivity and properties of the coatings, sometimes in a remarkable manner. This introduces the standard EN-B coatings and their properties and highlights the most recent improvements, variants in bath composition and process and applications of electroless nickel‑boron; such as the production of lead and thallium-free coatings, the effect of ultrasonics and surfactants, composite and multi-alloy coatings, but also innovative post-treatments to provide a wide panorama of the recent advances in the field. Finally, the review brings light to the new developments that are still necessary in the field to narrow the gap between research and industrial development and enlarge the niche area of utilization. 

The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials.

/pdf/adaptive-evolutionary-computation-for-nonlinear-hammerstein-3q266nx5.pdf

Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle

Abstract Around 1,000 peer-reviewed papers were selected from 3,450 articles published during 2020–2021, and reviewed as the representative advances in tribology research worldwide. The survey highlights the development in lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology, providing a show window of the achievements of recent fundamental and application researches in the field of tribology. 

/pdf/a-review-of-advances-in-tribology-in-2020-2021-2e00q3oy.pdf

A review of advances in tribology in 2020–2021

Critical micelle concentration (CMC) is an important factor to avoid the formation of micelles from monomeric surfactant molecules. Trisodium citrate stabilized electroless Ni B (ENi-B) coating on aluminium alloy (Al7075-T6) is attempted with the addition of amphoteric surfactant, 3-(N, N-Dimethylmyristylammonio) propanesulfonate (3-DMAPS), to enhance the surface finish (Ra) of the coatings. The main aim of the study is to investigate the influence of surfactant concentration on average surface roughness in ENi-B bath and determine the CMC of 3-DMAPS at minimum Ra. Mathematical models relating the concentration of amphoteric surfactant (0–0.162 g/L) as an independent variable and Ra as a dependent variable are developed using univariate regression analysis (Linear, quadratic, power, and exponential models) and back propagation neural network (BPNN) algorithm. The coefficient of determination (R2) is used to evaluate the goodness of fit between the models, and the BPNN model is found to be the best fit (R2 > 0.98). The minimum Ra of 0.171 ± 0.001 μm was achieved at the CMC of 0.049 g/L (0.135 mM) from the genetic algorithm (GA) using the validated models developed by quadratic regression analysis and BPNN as fitness functions. SEM, XRD and AFM techniques were carried out for the characterization of ENi-B coatings with and without surfactant at CMC.

Regression-BPNN modelling of surfactant concentration effects in electroless NiB coating and optimization using genetic algorithm

In this work, a novel citrate stabilised electroless bath was developed and the process parameters (concentrations of nickel, reducing agent, and stabiliser) were optimised to achieve the maximum h...

Modelling and optimisation of hardness in citrate stabilised electroless nickel boron (ENi-B) coatings using back propagation neural network – Box Behnken design and simulated annealing – genetic algorithm

Artificial neural network modelling for average surface roughness in citrate stabilised electroless nickel boron coatings

The laser cladding technique is useful for enhancing the surface performance and extending the life of many components in severe wear and corrosive environments. The aim of the present study is to develop a model relating independent variables, laser power (2200–2800 W), powder-feed rate (30–50 g/min), laser-scanning speed (800–1200 mm/min) and the focal position of the laser beam (15–35 mm), with the dependent variable, Vickers hardness number, and optimize the process parameters to achieve maximum hardness in CO2 laser cladding of Inconel 625 powder on AISI 304 SS substrate. The models were developed using curve fitting and artificial neural network (ANN) models by fitting the experimental data and comparing the predicted values from the models with the experimental values. The model developed by curve fitting method was employed to optimize the hardness using first derivative test, generalized reduced gradient (GRG) and grey-relational analysis (GRA). The maximum hardness of 374 was achieved by first derivative test at optimal laser power, powder-feed rate, laser-scanning speed, and the focal position of the laser beam of 2554 W, 999 mm/min, 30 mm and 40.5 g/min, respectively.

M. Vijayanand

Papers

Regression-BPNN modelling of surfactant concentration effects in electroless NiB coating and optimization using genetic algorithm

Modelling and optimisation of hardness in citrate stabilised electroless nickel boron (ENi-B) coatings using back propagation neural network – Box Behnken design and simulated annealing – genetic algorithm

Artificial neural network modelling for average surface roughness in citrate stabilised electroless nickel boron coatings

Process Modelling and Optimization of Hardness in Laser Cladding of Inconel® 625 Powder on AISI 304 Stainless Steel