A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.     

Effect of Initial Microstructure on Impact Toughness of 1200 MPa-Class High Strength Steel with Ultrafine Elongated Grain Structure

A medium-carbon low-alloy steel was prepared with initial structures of either martensite or bainite. For both initial structures, warm caliber-rolling was conducted at 773 K (500 °C) to obtain ultrafine elongated grain (UFEG) structures with strong 〈110〉//rolling direction (RD) fiber deformation textures. The UFEG structures consisted of spheroidal cementite particles distributed uniformly in a ferrite matrix of a transverse grain size of about 331 and 311 nm in samples with initial martensite and bainite structures, respectively. For both initial structures, the UFEG materials had similar tensile properties, upper shelf energy (145 J), and ductile-to-brittle transition temperatures 98 K (500 °C). Obtaining the martensitic structure requires more rapid cooling than is needed to obtain the bainitic structure and this more rapid cooling promote cracking. As the UFEG structures obtained from initial martensitic and bainitic structures have almost identical properties, but obtaining the bainitic structure does not require a rapid cooling which promotes cracking suggests the use of a bainitic structure in obtaining UFEG structures should be examined further.     

Fault-tolerant control of linear systems using adaptive virtual actuator

In this paper, design and development of fault-tolerant control (FTC) is investigated for linear systems subject to loss of effectiveness and time-varying additive actuator faults as well as an external disturbance using the fault-hiding approach. The main aim of this approach is to keep the nominal controller and to design a virtual actuator that is inserted between the faulty plant and the nominal controller in order to hide actuator faults and disturbances from the nominal controller, and consequently the performance of the system before and after the occurrence of actuator faults is kept to be the same. The proposed adaptive virtual actuator does not require a separated fault detection, isolation and identification (FDII) unit and both state and output feedback cases are considered. An illustrative example is given to demonstrate the effectiveness of the proposed adaptive virtual actuator in both cases.     

Dynamic mechanical characterization of aluminum: analysis of strain-rate-dependent behavior

Mechanics of Time-Dependent Materials - A significant number of materials show different mechanical behavior under dynamic loads compared to quasi-static (Salvado et al. in Prog. Mater....     

Analysis and Optimization of Ultrasound-Assisted Alkaline Palm Oil Transesterification by RSM and ANN-GA

In this study, the effects of ultrasound irradiation on transesterification process and characteristics of the synthesized biodiesel were investigated. The study was divided into two parts. In the first part, response surface methodology (RSM) and Central Composite Design (CCD) were employed to design experiments, develop the regression model, and evaluate individual and interactive impacts of five independent operational variables. The obtained results were then predicted by an optimized artificial neural network-genetic algorithm (ANN-GA) algorithm. The estimated results were compared with the experimental results. In the second part of the work, the impact of ultrasound irradiation on the main characteristics of the synthesized biodiesel was investigated. The analysis of the operating conditions indicated that reaction temperature and MeOH:oil molar ratio were the most important variables on reaction yield. The experimental results showed that there was a change in the main properties of the synthesized palm oil biodiesel with the density changed by about 0.3 kg/m³, viscosity by 0.12 mm²/s, pour/cloud point by 1–2°C, and flash point by 5°C, depending on different combinations of operational parameters. Besides, the numerical optimization technique was employed to optimize process variables in order to obtain the maximum FAME content (reaction yield) along with the best properties using both RSM and ANN-GA techniques. The maximum reaction yields of 95.2% and 95.1% were predicted by the RSM and ANN-GA models, respectively, at the optimum conditions. The conditions predicted by RSM and ANN-GA proved to be feasible for modeling and optimizing transesterfication yield with an accuracy of 99.18% and 99.14% and biodiesel properties of 98.61% and 98.28%, respectively.     

A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems

The multi-criteria group decision-making methods under fuzzy environments are developed to cope with imprecise and uncertain information for solving the complex group decision-making problems. A team of some professional experts for the assessment is established to judge candidates or alternatives among the chosen evaluation criteria. In this paper, a novel multi-criteria weighting and ranking model is introduced with interval-valued hesitant fuzzy setting, namely IVHF-MCWR, based on the group decision analysis. The interval-valued hesitant fuzzy set theory is a powerful tool to deal with uncertainty by considering some interval-values for an alternative under a set regarding assessment factors. In procedure of the proposed IVHF-MCWR model, weights of criteria as well as experts are considered to decrease the errors. In this regard, optimal criteria’ weights are computed by utilizing an extended maximizing deviation method based on IVHF-Hamming distance measure. In addition, experts’ judgments are taken into account for computing the criteria’ weights. Also, experts’ weights are determined based on proposed new IVHF technique for order performance by similarity to ideal solution method. Then, a new IVHF-index based on Hamming distance measure is introduced to compute the relative closeness coefficient for ranking the candidates or alternatives. Finally, two application examples about the location and supplier selection problems are considered to indicate the capability of the proposed IVHF-MCWR model. In addition, comparative analysis is reported to compare the proposed model and three fuzzy decision methods from the recent literature. Comparing these approaches and computational results shows that the IVHF-MCWR model works properly under uncertain conditions.     

Piezoelectric Biomaterials for Sensors and Actuators

Recent advances in materials, manufacturing, biotechnology, and microelectromechanical systems (MEMS) have fostered many exciting biosensors and bioactuators that are based on biocompatible piezoelectric materials. These biodevices can be safely integrated with biological systems for applications such as sensing biological forces, stimulating tissue growth and healing, as well as diagnosing medical problems. Herein, the principles, applications, future opportunities, and challenges of piezoelectric biomaterials for medical uses are reviewed thoroughly. Modern piezoelectric biosensors/bioactuators are developed with new materials and advanced methods in microfabrication/encapsulation to avoid the toxicity of conventional lead‐based piezoelectric materials. Intriguingly, some piezoelectric materials are biodegradable in nature, which eliminates the need for invasive implant extraction. Together, these advancements in the field of piezoelectric materials and microsystems can spark a new age in the field of medicine.     

Influence of oxygen plasma treatment parameters on the properties of carbon fiber

This study is focused on the impact of oxygen plasma treatment on properties of carbon fibers and interfacial adhesion behavior between the carbon fibers and epoxy resin. The influences of the main parameters of plasma treatment process, including duration, power, and flow rate of oxygen gas were studied in detail using interlaminar shear strength (ILSS) of carbon fiber composites. The ILSS of composites made of carbon fibers treated by oxygen plasma for 1 min, at power of 125 W, and oxygen flow rate of 100 sccm presented a maximum increase of 28% compared to composites made of untreated carbon fibers. Furthermore, carbon fibers were characterized by scanning electron microscopy (SEM), tensile strength test, attenuated total reflectance Fourier transform infrared (ATR-FTIR), and Raman spectroscopy analyses. It was found that the concentration of reactive functional groups on the fiber surface was increased after the plasma modification, as well the surface roughness, which finally improved the interfacial adhesion between carbon fibers and epoxy resin. However, high power and long exposure times could partly damage the surface of carbon fibers and decrease the tensile strength of filaments and ILSS of treated fiber composites.     

Scale formation on the wall of a mechanically stirred vessel—experimental assessment and interpretation using computational fluid dynamics

Accelerated growth of scale was studied in a baffled agitated reactor, which could be disassembled into nine sections, allowing quantitative determination of scale thickness. A coordinate measuring machine was used to determine the scale thickness on individual wall segments. The growth pattern of the scale was found to be nonuniform due to the variation of fluid velocity near the wall at various heights. Computational fluid dynamics (CFD) simulation showed that fluid flow is time‐dependent and has two distinct flow zones, one involving recirculation through the impeller in the lower part of the vessel and the other involving lower velocities due to the flow separation at the wall in the upper part. CFD simulation also showed the presence of macroinstabilities, which manifest as asymmetrical and chaotic flow structures with relatively long‐time scales. Scale growth is found to be prominent in regions where the fluid velocity and wall shear stresses are low. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3912–3922, 2018