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

In recent decades, researches on optimizing the parameter of the artificial neural network (ANN) model has attracted significant attention from researchers. Hybridization of superior algorithms helps improving optimization performance and capable of solving complex applications. As a traditional gradient-based learning algorithm, ANN suffers from a slow learning rate and is easily trapped in local minima when training techniques such as gradient descent (GD) and back-propagation (BP) algorithm are used. The characteristics of randomization and selection of the best or near-optimal solution of metaheuristic algorithm provide an effective and robust solution; therefore, it has always been used in training of ANN to improve and overcome the above problems. New metaheuristic algorithms are proposed every year. Therefore, the review of its latest developments is essential. This article attempts to summarize the metaheuristic algorithms which have been proposed from the year 1975 to 2020 from various journals, conferences, technical papers, and books. The comparison of the popularity of the metaheuristic algorithm is presented in two time frames, such as algorithms proposed in the recent 20 years and those proposed earlier. Then, some of the popular metaheuristic algorithms and their working principle are reviewed. This article further categorizes the latest metaheuristic search algorithm in the literature to indicate their efficiency in training ANN for various industry applications. More and more researchers tend to develop new hybrid optimization tools by combining two or more metaheuristic algorithms to optimize the training parameters of ANN. Generally, the algorithm’s optimal performance must be able to achieve a fine balance of their exploration and exploitation characteristics. Hence, this article tries to compare and summarize the properties of various metaheuristic algorithms in terms of their convergence rate and the ability to avoid the local minima. This information is useful for researchers working on algorithm hybridization by providing a good understanding of the convergence rate and the ability to find a global optimum.

Advances of metaheuristic algorithms in training neural networks for industrial applications

Stalk lodging (breaking of agricultural plant stalks prior to harvest) results in millions of dollars in lost revenue each year. Despite a growing body of literature on the topic of stalk lodging, the structural efficiency of maize stalks has not been investigated previously. In this study, we investigate the morphology of mature maize stalks to determine if rind tissues, which are the major load bearing component of corn stalks, are efficiently organized to withstand wind induced bending stresses that cause stalk lodging. 945 fully mature, dried commercial hybrid maize stem specimens (48 hybrids, ~ 2 replicates, ~ 10 samples per plot) were subjected to: (1) three-point-bending tests to measure their bending strength and (2) rind penetration tests to measure the cross-sectional morphology at each internode. The data were analyzed through an engineering optimization algorithm to determine the structural efficiency of the specimens. Hybrids with higher average bending strengths were found to allocate rind tissue more efficiently than weaker hybrids. However, even strong hybrids were structurally suboptimal. There remains significant room for improving the structural efficiency of maize stalks. Results also indicated that stalks are morphologically organized to resist wind loading that occurs primarily above the ear. Results are applicable to selective breeding and crop management studies seeking to reduce stalk lodging rates.

/pdf/diverse-maize-hybrids-are-structurally-inefficient-at-1xdbehtgh6.pdf

Diverse maize hybrids are structurally inefficient at resisting wind induced bending forces that cause stalk lodging.

Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

https://www.mdpi.com/1424-8220/20/6/1625/pdf

Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding

Among different weld cladding processes, gas metal arc welding (GMAW) cladding becomes a cost effective, user friendly, versatile method for protecting the surface of relatively lower grade structural steels from corrosion and/or erosion wear by depositing high grade stainless steels onto them. The quality of cladding largely depends upon the bead geometry of the weldment deposited. Weld bead geometry parameters, like bead width, reinforcement height, depth of penetration, and ratios like reinforcement form factor (RFF) and penetration shape factor (PSF) determine the quality of the weld bead geometry. Various process parameters of gas metal arc welding like heat input, current, voltage, arc travel speed, mode of metal transfer, etc. influence formation of bead geometry. In the current experimental investigation, austenite stainless steel (316) weld beads are formed on low alloy structural steel (E350) by GMAW using 100% CO2 as the shielding gas. Different combinations of current, voltage and arc travel speed are chosen so that heat input increases from 0.35 to 0.75 kJ/mm. Nine number of weld beads are deposited and replicated twice. The observations show that weld bead width increases linearly with increase in heat input, whereas reinforcement height and depth of penetration do not increase with increase in heat input. Regression analysis is done to establish the relationship between heat input and different geometrical parameters of weld bead. The regression models developed agrees well with the experimental data. Within the domain of the present experiment, it is observed that at higher heat input, the weld bead gets wider having little change in penetration and reinforcement; therefore, higher heat input may be recommended for austenitic stainless steel cladding on low alloy steel.

Effect of Heat Input on Geometry of Austenitic Stainless Steel Weld Bead on Low Carbon Steel

Cladding is a surface modification process in which a specially designed alloy is surface welded in order to enhance corrosion resistant properties. Common cladding techniques include Gas Tungsten Arc Welding (GTAW), submerged arc welding (SAW) and gas metal arc welding (GMAW). Because of high reliability, easiness in operation, high penetration good surface finish and high productivity gas metal arc welding became a natural choice for fabrication industries. This paper presents central composite rotatable design with full replication techniques to predict four critical dimensions of bead geometry. The second order regression method was developed to study the correlations. The developed models have been checked for adequacy and significance. The main and interaction effects of process variables and bead geometry were presented in graphical form. Using fmincon function the process parameters were optimized.

	 

	Key words: Gas metal arc welding (GMAW), weld bead geometry, mathematical model.

/pdf/prediction-and-optimization-of-weld-bead-geometry-in-gas-2kg0x3ahc2.pdf

Prediction and optimization of weld bead geometry in gas metal arc welding process using RSM and fmincon

This paper presents an integrated method with a new approach using experimental design matrix of experimental design techniques on experimental data available from conventional experimentation, application of neural network for predicting weld bead geometry and use of genetic algorithm for optimizing percentage of dilution. Quality of weld is affected by large number of welding parameters. Modelling of weld bead geometry is important for predicting quality of weld. In this study, an experimental work was conducted to optimize various input process parameters (welding current, welding speed, gun angle, contact tip to work distance and pinch) to get optimum dilution in stainless steel cladding of low carbon structural steel plates using Gas Metal Arc Welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. By using ANN models the welding output parameters predicted. Using Genetic Algorithm (GA) the process parameters were optimized to get optimum dilution.

Genetic algorithm for optimization of welding variables for percentage of dilution and application of ann for prediction of weld bead geometry in gmaw process

Magneto rheological elastomer (MRE) is a new kind of smart materials. Its dynamic mechanical performances can be controlled by controlling an applied magnetic field. MRE is usually used as stiffness - changeable spring in the semi-active vibration absorber. In order to get perfect vibration control effect, low dynamic damping of MRE is needed. This paper presents a new method of fabricating isotropic MREs under normal temperature and pressure conditions. In the absence of a magnetic field, a variety of MR elastomer samples were prepared using Sylard’s184 silicone elastomer along with un-annealed electrolytic iron power 500 mesh and 15 micron size. Their dynamic characteristics like fractional change in resonant frequency and fractional change in have been studied. The effects of iron particles and the applied magnetic field were investigated. This study is also expected to provide a good guide for designing and preparing new MR elastomers.

/pdf/development-and-characterization-of-isotropic-3gr0wfq3cc.pdf

Development and Characterization of Isotropic Magnetorheological Elastomer

Dynamic mechanical properties are the mechanical properties of materials as they are deformed under periodic forces. These properties can be expressed via dynamics modulus, the loss modulus and a mechanical damping or internal friction. This paper describes investigations on dynamic mechanical analysis of Magnetorheological Elastomer cured with and without magnetic field. Sylgard's 184 Silicone Elastomer has been used with 5 micron size iron particle. Viscoelastic properties such as storage modulus (E'), loss modulus (E"), damping, were compared for with and without magnetic field curing.

Dynamic mechanical analysis of Magnetorheological Elastomer

Smart materials are nowadays being used in all spheres of human life and technology. A lot of research is going on to utilize their potential in various engineering applications which may prove useful for common people. A wide variety of smart materials exist, which includes piezoelectric materials, magneto rheological materials (MR), electro rheological materials (ER), shape memory alloys, etc. In both ER and MR fluids, the change in fluid properties like viscosity can be manipulated by varying an electrical supply. By varying the strength of the electric field, the particle chains can be aligned in between the electrodes. The automotive and aerospace industries have for the first time used these smart materials for various applications. This paper highlights automobile engineering applications of the various smart materials - notably for vibration control and variable torque transmission. Nowadays, MR fluids are being developed for use in car shocks. Presently the researchers are working on use of ER fluids in automotive clutches, valves, engine mounts etc. Magneto rheological elastomers (MRE) are also being researched for various automotive applications.

Research on Smart Materials for Automotive Applications

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