Multi-response optimization of CO2 laser welding process of austenitic stainless steel

Recently, laser welding of austenitic stainless steel has received great attention in industry, due to its wide spread application in petroleum refinement stations, power plant, pharmaceutical industry and households. Therefore, mechanical properties should be controlled to obtain good welded joints. The welding process should be optimized by the proper mathematical models. In this research, the tensile strength and impact strength along with the joint operating cost of laser welded butt joints made of AISI304 was investigated. Design-expert software was used to establish the design matrix and to analyze the experimental data. The relationships between the laser welding parameters (laser power, welding speed and focal point position) and the three responses (tensile strength, impact strength and joint operating cost) were established. Also, the optimization capabilities in design-expert software were used to optimise the welding process. The developed mathematical models were tested for adequacy using analysis of variance and other adequacy measures. In this investigation the optimal welding conditions were identified in order to increase the productivity and minimize the total operating cost. Overlay graphs were plotted by superimposing the contours for the various response surfaces. The process parameters effect was determined and the optimal welding combinations were tabulated

Blind Multiclass Ensemble Classification

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.Comment: To appear in IEEE Transactions in Signal Processin

Determination of the Joint Confidence Region of Optimal Operating Conditions in Robust Design by Bootstrap Technique

Robust design has been widely recognized as a leading method in reducing variability and improving quality. Most of the engineering statistics literature mainly focuses on finding "point estimates" of the optimum operating conditions for robust design. Various procedures for calculating point estimates of the optimum operating conditions are considered. Although this point estimation procedure is important for continuous quality improvement, the immediate question is "how accurate are these optimum operating conditions?" The answer for this is to consider interval estimation for a single variable or joint confidence regions for multiple variables. In this paper, with the help of the bootstrap technique, we develop procedures for obtaining joint "confidence regions" for the optimum operating conditions. Two different procedures using Bonferroni and multivariate normal approximation are introduced. The proposed methods are illustrated and substantiated using a numerical example.Comment: Two tables, Three figure

Validation of a model of regulation in the tryptophan operon against multiple experiment data using global optimisation

This paper is concerned with validating a mathematical model of regulation in the tryptophan operon using global optimization. Although a number of models for this biochemical network are proposed, in many cases only qualitative agreement between the model output and experimental data was demonstrated, since very little information is currently available to guide the selection of parameter values for the models. This paper presents a model validating method using both multiple experimental data and global optimization