A Study on the Estimation of Prefabricated Glass Fiber Reinforced Concrete Panel Strength Values with an Artificial Neural Network Model

In this study, artificial neural networks trained with swarm based artificial bee colony optimization algorithm was implemented for prediction of the modulus of rapture values of the fabricated glass fiber reinforced concrete panels. For the application of the ANN models, 143 different four-point bending test results of glass fiber reinforced concrete mixes with the varied parameters of temperature, fiber content and slump values were introduced the artificial bee colony optimization and conventional back propagation algorithms. Training and the testing results of the corresponding models showed that artificial neural networks trained with the artificial bee colony optimization algorithm have remarkable potential for the prediction of modulus of rupture values and this method can be used as a preliminary decision criterion for quality check of the fabricated products.     

Investigations on the effects of multi-layered coated inserts in machining Ti-6Al-4V alloy with experiments and finite element simulations

This paper presents investigations on turning Ti-6Al-4V alloy with multi-layer coated inserts. Turning of Ti-6Al-4V using uncoated, TiAlN coated, and TiAlN + cBN coated single and multi-layer coated tungsten carbide inserts is conducted, forces and tool wear are measured. 3D finite element modelling is utilized to predict chip formation, forces, temperatures and tool wear on these inserts. Modified material models with strain softening effect are developed to simulate chip formation with finite element analysis and investigate temperature fields for coated inserts. Predicted forces and tool wear contours are compared with experiments. The temperature distributions and tool wear contours demonstrate some advantages of coated insert designs.     

Simulation of serrated chip formation in micro-milling of titanium alloy Ti-6Al-4V using 2D elasto-viscoplastic finite element modeling

Finite element simulations have been utilized in analyses of machining process for several decades. In mechanical micromachining, finite element simulation can also be used for predicting cutting forces, minimal chip thickness, temperatures, and tool wear. The accuracy of results and the computational cost are highly dependent upon the assumptions which govern that particular chip formation problem. This study presents a comparison of two different material assumptions in finite element simulation of micro-milling titanium alloy Ti-6Al-4V. The same simulation was conducted by using the elasto-viscoplastic and the viscoplastic material assumptions. The predicted results are compared against the experimental observations. The results have shown that the material assumption has a major effect on the mechanism of chip formation and heat generation but a minor effect on the cutting force and tool wear prediction. In terms of computational cost, it was found that the simulation with the viscoplastic material assumption can reduce simulation time up to eight times that of required for a simulation with elasto-viscoplastic assumption.     

Novel approach with polyfluorene/polydisulfide copolymer binder for high-capacity silicon anode in lithium-ion batteries

In this study, as a novel design with the collaboration of a fluorene and sulfide-based copolymer for Li-ion battery application is presented. Polyfluorene-co-polydisulfide is prepared with desired functional groups to yield a conductivity and good adhesion. These critical and important features are performed by preparing polymers with proper functional groups. The preparation process is accomplished via Suzuki coupling process under Pd catalyst by combining separately synthesized 4,4′-dibromodiphenyl disulfide in combination with 9,9-dioctylfluorene-2,7-bis(trimethylborate). The fully obtained capacity of the silicon particles, that is, at C/10 with the capacity of 1250 mAh g⁻¹ after the 500th cycle, approves the good performance by preserving capacity stability till 600th cycles. The designed and synthesized polymer binder with different functionalities and carbon nanotube additive show better characteristics such as conductivity, high polarity, and binding adhesion. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48303.     

Determination of tool friction in presence of flank wear and stress distribution based validation using finite element simulations in machining of titanium and nickel based alloys

Tool friction plays a very important role in machining titanium and nickel-based alloys and is an important parameter in Finite Element based machining simulations. It is the source for the high amount of heat generation, and as a result, the excessive flank wear during machining these materials. The worn tool is known to create poor surface qualities with high tensile surface residual stresses, machining induced surface hardening, and undesirable surface roughness. It is essential to develop a methodology to determine how and to what extent the friction is built up on the tool. This study facilitates a determination methodology to estimate the stress distributions on the rake and flank surfaces of the tool and resultant friction coefficients between the tool and the chip on tool rake face, and the tool and the workpiece on tool flank face. The methodology is applied to various tool edge radii and also utilized in solving stagnation point location on the tool edge. Predicted friction results are further validated with comparison of predicted stress distributions from FE simulations for machining of titanium alloy Ti-6Al-4V and the nickel-based alloy IN-100. It was found that tool stresses and friction are mainly influenced by tool rake angle, edge radius, and tool flank wear and are slightly affected by the cutting conditions in the ranges that were considered in this study.     

Adsorption of some highly toxic dyestuffs from aqueous solution by chitin and its synthesized derivatives

The adsorption of indigo carmine (IC) and trypan blue (TB) by chitin was kinetically investigated as dependent on temperature and pH. Experimental data obtained at different temperatures for the adsorption of each dyestuff were applied to the pseudo first-order, the pseudo second-order and the intraparticle diffusion rate equations, and the pseudo first-order rate constants (k₁), the pseudo second-order rate constants (k₂) and the intraparticle diffusion rate constants (k_p) at these temperatures were calculated, respectively. In addition, adsorption isotherms of each dyestuff by chitin were also determined at different temperatures. It was seen that adsorption isotherms fitted the H type. Chitin was then modified via ring-opening reaction with cyclic acid anhydrides (succinic, maleic and 1,2,4-benzenetricarboxylic) in lithium chloride/N,N-dimethylacetamide. Chitin derivatives obtained were compared according to their capabilities to adsorb IC and TB. It was determined that IC and TB were more adsorbed by chitin modified with 1,2,4-benzenetricarboxylic anhydride (CBA) and chitin modified with maleic anhydride (CMA), respectively. 31.33% of IC (C_o: 300 ppm) by CBA, and 44.29% of TB (C_o: 200 ppm) by CMA were adsorbed. Then, the adsorption of IC by CBA and of TB by CMA was kinetically investigated as dependent on temperature. The constants k₁, k₂ and k_p related to the adsorption of IC by CBA and of TB by CMA were calculated as stated above.     

Process simulation using finite element method — prediction of cutting forces, tool stresses and temperatures in high-speed flat end milling

End milling of die/mold steels is a highly demanding operation because of the temperatures and stresses generated on the cutting tool due to high workpiece hardness. Modeling and simulation of cutting processes have the potential for improving cutting tool designs and selecting optimum conditions, especially in advanced applications such as high-speed milling. The main objective of this study was to develop a methodology for simulating the cutting process in flat end milling operation and predicting chip flow, cutting forces, tool stresses and temperatures using finite element analysis (FEA). As an application, machining of P-20 mold steel at 30 HRC hardness using uncoated carbide tooling was investigated. Using the commercially available software DEFORM-2D™, previously developed flow stress data of the workpiece material and friction at the chip–tool contact at high deformation rates and temperatures were used. A modular representation of undeformed chip geometry was used by utilizing plane strain and axisymmetric workpiece deformation models in order to predict chip formation at the primary and secondary cutting edges of the flat end milling insert. Dry machining experiments for slot milling were conducted using single insert flat end mills with a straight cutting edge (i.e. null helix angle). Comparisons of predicted cutting forces with the measured forces showed reasonable agreement and indicate that the tool stresses and temperatures are also predicted with acceptable accuracy. The highest tool temperatures were predicted at the primary cutting edge of the flat end mill insert regardless of cutting conditions. These temperatures increase wear development at the primary cutting edge. However, the highest tool stresses were predicted at the secondary (around corner radius) cutting edge.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Infrared and Structural Properties of Y1−xNdxBa2Cu3O7−δ (0 ≤ x ≤ 1)

Infrared and structural properties of Y_1?x Nd _x Ba₂Cu₃O_7?δ (0 ≤ x ≤ 1) were investigated using infrared absorption spectroscopy and X-ray powder diffraction. The unit cell parameters of the samples were defined using X-ray diffraction data. The resistance measurements showed that the samples revealed superconductivity in the temperature range of 80–100 K. It was observed that by the substitution of Nd to Y in YBa₂Cu₃O_{7 ? δ} IR band at 573 cm^?1 that is assigned as Cu–O axial antisymmetric stretching mode shifts to 533 cm^?1 while the band at 620 cm^?1 that is due to Cu–O symmetric stretching mode in YBa₂Cu₃O_7?δ shifts to 588 cm^?1.