Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm

Material selection, one of the mostly encountered decision problems in material science literature, is still an onerous task for manufacturing organisations. Achieving accurate solution to this issue, the paper developed a model selection interface to enable analytical solutions to different problem concepts in material selection under multiple attribute decision-making (MADM) environments. Specifically, the generic framework of the fuzzy axiomatic design-model selection interface (FAD-MSI) is modified and successfully applied to the different material selection problem concepts. Consequently, the derived problem-model sets can be referred to accomplish further proposals on this era.     

The role of growth parameters on structural,morphology and optical properties of sprayed ZnS thin films

ZnS thin films were prepared by spray pyrolysis technique using aqueous of zinc chloride and thiourea at molar ratio of 1:1, 1:2, and 2:1. The depositions were carried out on substrates heated from 400 to 520 °C The films were then annealed under sulphur atmosphere for 90 min at 450 and 550 °C. The crystallites exhibit preferential orientation along the [002]_α or [111]_β directions. The films were characterized by XRD and SEM. The structure and morphology of sprayed films are controlled by both, the substrate temperature and the precursors molar ratio in the solution. The values of optical bandgap have been determined from the absorption spectra.     

IGNITION DELAY CHARACTERISTICS OF SOME TURKISH VEGETABLE OIL-DIESEL FUEL BLENDS

Vegetable oils have chances to be used in Diesel engines as alternative fuels contributing to the solution of some agricultural, environmental and economical problems. Direct use of them has some technical problem yet but as blended fuels with diesel fuel or esters they have places on the application area. In this paper the effect of the compression ratio on ignition delay is investigated in an ASTM-CER engine working with four different types of vegetable oil of Turkish origin (sunflower, corn, soybean, and olive oil) blended with grade No.2-D diesel fuel at a ratio of 20/80 (v/v) and the results are compared with baseline diesel fuel. Longer ignition delay periods have generally been obtained for blend fuels ranking from olive oil to sunflower oil as compared to diesel fuel.     

Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms

This paper presents a method of tuning Proportional Integral (PI) controller coefficients in the off-line control of a nonlinear system. In this method, the first step is the identification of the system via Artificial Neural Networks (ANNs), using maximum overshoot and settling time obtained from the application circuit for different K_p-K_i pairs. With this in mind, multi-layer ANN, which uses back-propagation of the error algorithm, was used as the learning algorithm. In the second step, the purpose is to find the optimum controller coefficients using the ANN model as the objective function via Genetic Algorithms (GAs). A Digital Signal Processor (DSP-TMS320C50) was used to carry out control applications. The C++ language was used for ANN and GA, and and the Assembly language was used for the DSP. It is determined that maximum overshoot and settling time are very small if the system is controlled by control parameters obtained from the optimization process that uses GA.     

Optimum design of slurry pipelines by genetic algorithm

Various applications of slurry transportation through pipelines exist. A transportation problem is formulated to determine the pipe diameters and amounts of transported slurry from the production to the consumption points. The minimisation of the cost consisting of the pipe and energy cost terms is considered as the objective function. Pipe cost is given as the function of pipe diameters and the energy cost is defined as the function of pipe diameters and slurry amounts. Energy cost is obtained by using the relation that is previously determined after the experimental studies have been made for the magnetite ore. Genetic algorithm is used as the optimisation method and to apply this method a commercially available software written in the C language is used and modified. The proposed methodology to solve this non-linear programming problem is applied to a transportation system and it is found that the proposed methodology has made the complex, labour-intensive solution process very convenient for the users.     

Voltage-mode multifunction filter with mutually independent Q and \omega _0 control feature using VDDDAs

The paper focuses on the application possibilities of the newly presented voltage differencing active building block called voltage differencing differential difference amplifier. Using this active element, a multifunction frequency filter is designed featuring the possibility of mutually independent control of quality factor Q and characteristic frequency \(\omega _0\) by means of active elements. The structure of the filter is based on the idea of the Akerberg-Mossberg (AM) filter, i.e. the integrators in the structure are always realized only by two active elements. This fact results in better phase compensation for the filter. Compared to the AM opamp based filter, the newly proposed structure features high-impedance inputs, low-impedance output, and all basic frequency responses. The performance of the proposed structure has been verified by SPICE simulations using the TSMC \(0.18\,\upmu \hbox {m}\) level-7 SCN018 CMOS process parameters with \(\pm 0.9\,\hbox {V}\) supply voltage.     

Bio‐Hybrid Cell‐Based Actuators for Microsystems

As we move towards the miniaturization of devices to perform tasks at the nano and microscale, it has become increasingly important to develop new methods for actuation, sensing, and control. Over the past decade, bio‐hybrid methods have been investigated as a promising new approach to overcome the challenges of scaling down robotic and other functional devices. These methods integrate biological cells with artificial components and therefore, can take advantage of the intrinsic actuation and sensing functionalities of biological cells. Here, the recent advancements in bio‐hybrid actuation are reviewed, and the challenges associated with the design, fabrication, and control of bio‐hybrid microsystems are discussed. As a case study, focus is put on the development of bacteria‐driven microswimmers, which has been investigated as a targeted drug delivery carrier. Finally, a future outlook for the development of these systems is provided. The continued integration of biological and artificial components is envisioned to enable the performance of tasks at a smaller and smaller scale in the future, leading to the parallel and distributed operation of functional systems at the microscale.     

Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming

Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with Al₂O₃ particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones.     

Computational investigation of testing parameter effects on abrasive wear behaviour of AL2O3 particle-reinforced MMCS using statistical analysis

The wear rate model of 7.3?vol.% Al₂O₃ particle-reinforced aluminium alloy composites with 16 and 66???m particle sizes fabricated by molten metal mixing method was developed in terms of applied load, particle size of reinforcement, abrasive grain size and sliding distance based on the Taguchi method. The two-body abrasive wear behaviour of the specimens was investigated using a pin-on-disc abrasion test apparatus where the sample slid against different SiC abrasives under the loads of 2 and 5?N at the room conditions. The orthogonal array, signal-to-noise ratio and analysis of variance were employed to find out the optimal testing parameters. The test results showed that particle size of reinforcement was found to be the most effective factor among the other control parameters on abrasive wear, followed by abrasive grain size. Moreover, the optimal combination of the testing parameters was determined and predicted. The predicted wear rate results were compared with experimental results and found to be quite reliable.