Concurrent cell formation and layout design using scatter search

Two key decisions in designing cellular manufacturing systems are cell formation and layout design problems. In the cell formation problem, machine groups and part families are determined while in the facility layout problem the location of each machine in each cell (intra-cell layout) and the location of each cell (inter-cell layout) are decided. Owing to the fact that there are interactions between two problems, cell formation and layout design problem must be tackled concurrently to design a productive manufacturing system. In this research, two problems are investigated concurrently. Some important and realistic factors such as inter-cell layout, intra-cell layout, operations sequence, part demands, batch size, number of cells, cell size, and variable process routings are incorporated in the problem. The problem is formulated as a mathematical model. Three different methods are described to solve the problem: multi-objective scatter search (MOSS), non-dominated genetic algorithm (NSGA-II), and the ε-constraint method. The methods are employed to solve nine problems generated and adopted from the literature. Sensitivity analysis is accomplished on the parameters of the problem to investigate the effects of them on objective function values. The results show that the proposed MOSS algorithm performs better than NSGA-II and produces better solutions in comparison to multi-stage approaches.     

Evaluation of hydrogen permeation through standalone thermally sprayed coatings of AISI 316L stainless steel

This research evaluates hydrogen permeation and its diffusion characteristics through standalone thermally sprayed coatings of AISI 316L stainless steel. The effects of various charging currents and other parameters on hydrogen diffusion coefficient were scrutinized using electrochemical hydrogen permeation tests. Hydrogen permeation through the thermally sprayed coatings displayed anomalous behavior such that a maximum pinnacle was observed in the permeation curves, attributed to heavily trapped hydrogen atoms in the delayed surface cracks. Therefore, new diffusion parameters were defined for modeling of the anomalous permeation curves. The fitted diffusion parameters were consistently identified, and hence, the model perfectly explained experimental data. The results showed that the increase in charging current caused fast activation and development of surface cracks. The measured diffusion coefficient of hydrogen in the stainless steel thermally sprayed coating was relatively high because the microstructure of the coating contained some ferritic phases and dense dendritic structure, which configure fast diffusion paths.     

Age-induced multi-stage transformation in a Ni-rich NiTiHf alloy

Abnormal multi-stage transformations have already been studied in binary Ni-rich NiTi alloys. In this research, this kind of transformation was investigated in a low supersaturated Ni-rich ternary NiTiHf high-temperature shape memory alloy by aging at intermediate temperatures for various durations. Meticulous examinations of the results of differential scanning calorimetry tests demonstrated the heterogeneous precipitation of (Ti,Hf)₃Ni₄ particles and the three-stage transformation (one-stage R and two-stage B19′) in the aged alloy. Aging provided a significant rise in transformation temperatures (TTs) until they reached their equilibrium states, corresponding to the equilibrium Ni content at each aging temperature. Equilibrium TTs were higher when aging was performed at a lower temperature. The remarkable increase in TTs was compared with those in aged Ni-rich NiTi alloys, and discussed based on the variation in valence electron concentration. A model was also proposed for the microstructural evolution during aging. Furthermore, aging provided enhanced hardness and strain recovery for the alloy. In particular, aging at a lower temperature resulted in a considerable improvement in hardness and shape recovery, which was discussed based on the microstructural changes in the aged alloy. Equilibrium Ni content at each aging temperature proved to be a crucial parameter in controlling the alloy properties, even in heterogeneous precipitations.     

Copper recovery improvement in an industrial flotation circuit: A case study of Sarcheshmeh copper mine

Obtaining high recovery in copper flotation plants has been always under investigation in recent years. Misreported copper into tailing dramatically declines copper recovery. This study aims to find the possible reasons of copper loss to tailing and present the methods to improve the performance of the Sarcheshmeh copper plant flotation circuit. This work was performed in two phases. In the first stage, two surveys were carried out in an industrial plant for evaluation of floatability of two different types of copper ore (sulfide and mixed copper ores comprising sulfide and oxide minerals) and investigation of misreported copper into tailing. In the second stage, influence of particle size distribution in different levels, the collector (Z11 + R407), and sulfidisation agent (NaHS) dosages were investigated on the floatability of mixed copper ore, and possible improvement of copper recovery ores was evaluated by a series of batch flotation experiments.

Almost 95% of overall copper loss in final tailing took place in the rougher circuit. The overall recovery of mixed copper ores was obtained 8% less than the other survey. It was found that an important factor of recovery reduction was due to increasing oxide copper proportion in rougher feed. Results of size-by-size recovery analysis showed that the highest sulfide copper loss occurred in coarse particles (>74 µm) and the highest oxide copper loss happened in fine particles (<9 µm). From batch flotation experiments, it was found that the ultimate recovery increased to a certain value with increasing the collector dosage (50 g/t) and thereafter reduced. Increasing the NaHS dosage to 800 g/t resulted in 4.5% increase in recovery from 79.3% to 83.8% with a significant increase in grade. It is observed that size reduction of coarse particles (>74 μm) of rougher tailing and then their flotation have significant effect on increasing overall recovery from 79.3% to 82.2%. Experimental studies showed that the use of the proposed methods with considering the optimized conditions in the plant can be led to an increase in copper recovery from 79% to 87% without any decrease in grade.     

Theoretical estimation of the apparent rate constants for ozone decomposition in gas and aqueous phases using ab initio calculations

An ab initio study, using the coupled cluster calculations (CCSD) method was conducted to investigate the kinetics of the ozone degradation in gas and aqueous phases considering the reaction of ozone with the hydroperoxyl radical. Two potential transition state paths, oxygen and hydrogen transfer, are studied and compared. It was revealed by the ab initio quantum chemical calculations that the calculated overall rate constant in the gas phase differs by approximately an order of magnitude from measured values. However, the calculated selectivity (branching fraction), which was measured directly with isotope studies of hydrogen atom transfer, is almost exactly equal to the experimental value at 298.15 K. The sensitivity analysis showed that adding the reaction between ozone and hydroperoxyl radical to the kinetic model accelerates the decomposition process by more than four times in the aqueous phase (pH = 7–8.5), and for an order of magnitude change in the rate constant of this reaction, the decomposition half-life changes by 20–45 %. This result might affect our understanding of atmospheric ozone chemistry.     

Mapping urban land cover based on spatial-spectral classification of hyperspectral remote-sensing data

In this article, an innovative classification framework for hyperspectral image data, based on both spectral and spatial information, is proposed. The main objective of this method is to improve the accuracy and efficiency of high-resolution land-cover mapping in urban areas. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MMSF) algorithm. A pixel-based support vector machine (SVM) algorithm is first used to classify the hyperspectral image data, then the enhanced MMSF algorithm is applied in order to increase the accuracy of less accurately classified land-cover types. The enhanced MMSF algorithm is used as a binary classifier. These two classes are the low-accuracy class and remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. In the proposed approach, namely MSF-SVM, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithms, and are then used to build the MSF. Three benchmark hyperspectral data sets are used for the assessment: Berlin, Washington DC Mall, and Quebec City. Experimental results demonstrate the superiority of the proposed approach compared with SVM and the original MMSF algorithms. It achieves approximately 5, 6, and 7% higher rates in kappa coefficients of agreement in comparison with the original MMSF algorithm for the Berlin, Washington DC Mall, and Quebec City data sets, respectively.     

Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways

The scour below spillways can endanger the stability of the dams. Hence, determining the scour depth downstream of spillways is of vital importance. Recently, soft computing models and, in particular, artificial neural networks (ANNs) have been used for scour depth prediction. However, ANNs are not as comprehensible and easy to use as empirical formulas for the estimation of scour depth. Therefore, in this study, two decision-tree methods based on model trees and classification and regression trees were employed for the prediction of scour depth downstream of free overfall spillways. The advantage of model trees and classification and regression trees compared to ANNs is that these models are able to provide practical prediction equations. A comparison between the results obtained in the present study and those obtained using empirical formulas is made. The statistical measures indicate that the proposed soft computing approaches outperform empirical formulas. Results of the present study indicated that model trees were more accurate than classification and regression trees for the estimation of scour depth.     

Constrained Portfolio Selection using Particle Swarm Optimization

This paper presents a novel heuristic method for solving an extended Markowitz mean–variance portfolio selection model. The extended model includes four sets of constraints: bounds on holdings, cardinality, minimum transaction lots and sector (or market/class) capitalization constraints. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio is equal to a predefined number. The sector capitalization constraints reflect the investors’ tendency to invest in sectors with higher market capitalization value to reduce their risk of investment.The extended model is classified as a quadratic mixed-integer programming model necessitating the use of efficient heuristics to find the solution. In this paper, we propose a heuristic based on Particle Swarm Optimization (PSO) method. The proposed approach is compared with the Genetic Algorithm (GA). The computational results show that the proposed PSO effectively outperforms GA especially in large-scale problems.     

Prediction model for determining the optimum operational parameters in laser forming of fiber-reinforced composites

Composite materials are widely employed in various industries, such as aerospace, automobile, and sports equipment, owing to their lightweight and strong structure in comparison with conventional materials. Laser material processing is a rapid technique for performing the various processes on composite materials. In particular, laser forming is a flexible and reliable approach for shaping fiber-metal laminates (FMLs), which are widely used in the aerospace industry due to several advantages, such as high strength and light weight. In this study, a prediction model was developed for determining the optimal laser parameters (power and speed) when forming FML composites. Artificial neural networks (ANNs) were applied to estimate the process outputs (temperature and bending angle) as a result of the modeling process. For this purpose, several ANN models were developed using various strategies. Finally, the achieved results demonstrated the advantage of the models for predicting the optimal operational parameters.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00304-3