On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Engineering with Computers - A novel Harris hawks optimization algorithm is applied to microchannel heat sinks for the minimization of entropy generation. In the formulation of the heat transfer...     

Microstructure and Sliding Wear Behaviour of Ni(Cr)-TiB_2 Coatings Deposited by HVOF Spraying of SHS Powders

THERMAL SPRAYING provides a large range ofcoatings,which increase the wear resistance ofsubstrates[1].One of the major coating families is thecermet,composed of hard ceramic particles with ametallic binder.The most commonly used cermetcoatings in industrial applications are based on eitherthe WC-Co or the Cr3C2-Ni(Cr)systems with WC-17wt%Co and Cr3C2-25wt%Ni(Cr)being typicalcompositions[2,3].Although WC-Co deposits are hardand wear resistant at ambient temperatures their rangeof ap…     

Soft computing based on new interval-valued fuzzy modified multi-criteria decision-making method

In this paper, a new interval-valued fuzzy modified TOPSIS (IVFM-TOPSIS) method is proposed that can reflect both subjective judgment and objective information in real life situations. This proposed method is based on concepts of the positive ideal and negative ideal solutions for solving multi-criteria decision-making (MCDM) problems in a fuzzy environment. The performance rating values and weights of criteria are linguistic variables expressed as triangular interval-valued fuzzy numbers. Furthermore, we appraise the performance of alternatives against both subjective and objective criteria with multi-judges for decision-making problems. Finally, for the purpose of proving the validity of the proposed method a numerical example is presented for a robot selection problem.     

The solution and duality of imprecise network problems

Duality properties have been investigated by many researchers in the recent literature. They are introduced in this paper for a fully fuzzified version of the minimal cost flow problem, which is a basic model in network flow theory. This model illustrates the least cost of the shipment of a commodity through a capacitated network in terms of the imprecisely known available supplies at certain nodes which should be transmitted to fulfil uncertain demands at other nodes. First, we review on the most valuable results on fuzzy duality concepts to facilitate the discussion of this paper. By applying Hukuhara’s difference, approximated and exact multiplication and Wu’s scalar production, we exhibit the flow in network models. Then, we use combinatorial algorithms on a reduced problem which is derived from fully fuzzified MCFP to acquire fuzzy optimal flows. To give duality theorems, we utilize a total order on fuzzy numbers due to the level of risk and realize optimality conditions for providing some efficient combinatorial algorithms. Finally, we compare our results with the previous worthwhile works to demonstrate the efficiency and power of our scheme and the reasonability of our solutions in actual decision-making problems.     

Investigation of deep drawing concept of multi-point forming process in terms of prevalent defects

Multi-point forming is a novel flexible process that is economically suitable for both rapid prototyping and batch production of sheet metal parts. This technique is established based on altering rigid dies by matrices of adjustable punch elements. In this paper, the basic principle of this technique is implemented on deep drawing process. A reconfigurable die was constructed to investigate the multi-point deep drawing process. AA 2024-O Aluminum alloy was designated as test material. The formed specimens were evaluated in terms of dimpling defect, rupture, thickness distribution and dimensional accuracy. The onset of rupture was predicted by integrating the forming limit diagram of employed material with finite element Code. The predicted results were in a reasonable agreement with the experimental tests. It was found that for complete elimination of dimpling defect and acquiring maximum drawing depth, the proper allocation of elastic layer parameters such as thickness and hardness was crucial. The conducted investigations indicated that, in general, dimensional accuracy of formed parts was acceptable. However, for areas with sharp changes in geometry such as corners and side walls, deviation from desired geometry was evident. This phenomenon was remarkably dominant for manufactured parts utilizing softer elastic layer.     

An ant colony optimization algorithm for the bi-objective shortest path problem

Multi-objective shortest path problem (MOSP) is an extension of a traditional single objective shortest path problem that seeks for the efficient paths satisfying several conflicting objectives between two nodes of a network. MOSP is one of the most important problems in network optimization with wide applications in telecommunication industries, transportation and project management. This research presents an algorithm based on multi-objective ant colony optimization (ACO) to solve the bi-objective shortest path problem. To analyze the efficiency of the algorithm and check for the quality of solutions, experimental analyses are conducted. Two sets of small and large sized problems that generated randomly are solved. Results on the set problems are compared with those of label correcting solutions that is the most known efficient algorithm for solving MOSP. To compare the Pareto optimal frontiers produced by the suggested ACO algorithm and the label correcting algorithm, some performance measures are employed that consider and compare the distance, uniformity distribution and extension of the Pareto frontiers. The results on the set of instance problems show that the suggested algorithm produces good quality non-dominated solutions and time saving in computation of large-scale bi-objective shortest path problems.     

Combined quasi-static backward modeling and look-ahead fuzzy control of vehicles

Vehicle modeling can play an important role in vehicle power train design, control and energy management investigation. This paper presents a method for vehicle power train modeling. The key feature of the method is its presentation of the dynamic of vehicle based on the road information. This ability makes the method suitable for look-ahead energy management and fuel economy optimal control problems. With the aid of a road slope database, road geometry ahead of the vehicle is extracted. A fuzzy controller is developed that receives this information and controls the velocity of the vehicle with respect to its fuel consumption. In order to maintain the operation of the combustion engine near its efficient region, the fuzzy controller commands a continuously variable transmission. Simulations are carried out using real road data. The results are presented and discussed.     

A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process

Failure mode and effect analysis (FMEA) model is a technique used to evaluate the risk. This paper aimed to propose a new FMEA model combining technique for order of preference by similarity to ideal solution (TOPSIS) and belief structure to overcome the shortcomings of the traditional index of FMEA. In this paper, the fuzzy belief TOPSIS method is combined with FMEA to introduce a belief structure FMEA to describe the expert knowledge by a number of linguists as a grammatical phenomenon. Moreover, the weights of components in FMEA index can be different from each other. Therefore, the flexibility of assigning weight to each factor in this method is more compatible to the real decision-making situation. In other word, TOPSIS method is applied to determine the preference of alternatives versus risk criteria. Using linguistic terms in the fuzzy belief approach, the risk factors described a more meaningful value and decision-makers’ judgment is assigned with belief degrees through evaluation of factors. Finally, a numerical case study about the preference of cause failures of steel production process is provided to illustrate the process of proposed method, and then result and discussion is performed for each case.     

A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty

Organizations have recently become interested in applying new approaches to reduce fuel consumptions, aiming at decreasing green house gases emission due to their harmful effects on environment and human health; however, the large difference between practical and theoretical experiments grows the concern about significant changes in the transportation environment, including fuel consumptions, carbon dioxide (CO₂) emissions cost and vehicles velocity, that it encourages researchers to design a near-reality and robust pollution routing problem. This paper addresses a new time window pickup-delivery pollution routing problem (TWPDPRP) to deal with uncertain input data for the first time in the literature. For this purpose, a new mixed integer linear programming (MILP) approach is presented under uncertainty by taking green house emissions into consideration. The objective of the model is to minimize not only the travel distance and number of available vehicles along with the capacity and aggregated route duration restrictions but also the amount of fuel consumptions and green house emissions along with their total costs. Moreover, a robust counterpart of the MILP is introduced by applying the recent robust optimization theory. Computational results for several test problems indicate the capability and suitability of the presented MILP model in saving costs and reducing green house gases concurrently for the TWPDPRP problem. Finally, both deterministic and robust mathematical programming are compared and contrasted by a number of nominal and realizations under these test problems to judge the robustness of the solution achieved by the presented robust optimization model.     

Synthesis and process modeling of mesoporous γ-Al2O3 granules using the biopolymer chitosan via experimental design

In this study, biopolymer chitosan is presented as a template for synthesizing and shaping the mesoporous γ-Al₂O₃ macrospheres. This porous γ-Al₂O₃ granule has a high surface area (310 m²/g), high pore volume (.6148 cm³/g), and pore diameter between 2 and 10 nm. The full factorial design based on a mathematical model was implemented to study the acid concentration, chitosan amount, ammonia concentration, and aging time affecting the responses (Brunauer–Emmett–Teller surface area and pore volume). Predicted responses were found to be in satisfactory agreement with experimental values (R² = .9580 and .9109, respectively). The adequacy of the model was examined by analyzing the residual distribution plots and Pareto graph. X-ray diffraction, scanning electron microscopy (SEM), thermogravimetric analysis, and N₂ adsorption/desorption techniques are employed to characterize the structure of the prepared γ-alumina sample.