3D numerical analysis of drilling process: heat,wear, and built-up edge

In this study, a 3D finite element model is developed to investigate the drilling process of AISI 1045 steel, and particularly, the heat and wear on the drill faces. To model drill wear, a modified Usui flank wear rate is used. Experiments are used for the verification of the simulated model and the evaluation of the surface roughness and built-up edge. A comparison of the predicted and experimental thrust forces and flank wear rates revealed that the predicted values had low errors and were in good agreement with the experimental values, which showed the utility of the developed model for further analysis. Accordingly, a heat analysis indicated that approximately half the generated heat in the cutting zone was conducted to the drill bit. Furthermore, material adhesion occurred in localized heat areas to a great extent, thus resulting in wear acceleration. A maximum flank wear rate of 0.026 1 mm/s was observed when the rotary speed and feed rate were at the lowest and highest levels, respectively. In the reverse cutting condition, a minimum flank wear rate of 0.016 8 mm/s was observed.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-018-0223-z     

Establishing a Mathematical Model to Predict the Tensile Strength of Friction Stir Welded Pure Copper Joints

This investigation was undertaken to predict the tensile strength of friction stir welded pure copper. Response surface methodology based on a central composite rotatable design with four welding parameters, five levels, and 31 runs was used to conduct the experiments and to develop the mathematical regression model by means of Design-Expert software. Four welding parameters considered were tool profile design, rotational speed, welding speed, and axial force. Analysis of variance was applied to validate the predicted model. Confirmation experiments including microstructural characterization and conducted tensile tests showed that developed models are reasonably accurate. The results showed that the joints welded using the square and triangular tools had higher tensile strength compared to the joints welded using other tools. The increase in tool rotational speed, welding speed, and axial force resulted in increasing the tensile strength of the joints up to a maximum value. Also, the developed model showed that the optimum parameters to get a maximum of tensile strength were rotational speed, welding speed, and axial force of 942 rpm, 84 mm/min, and 1.62 kN, respectively.     

Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks

Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.     

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models

Magnetic Resonance Materials in Physics, Biology and Medicine - Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However,...     

Ionic Liquid-Mediated Biopolymer Extraction from Coffee Fruit

Ionic liquids (ILs) are effective solvents for biomass. Refined cellulose is commonly used; however, recent interest has grown to consider woody and herbaceous biomass and industrial crop residues like fruit peels. Here, the authors report results on the dissolution and shaping of cotton and coffee fruit (cascara) in 1,8-diazabicyclo[5.4.0]undec-7-inium acetate ([DBUH][OAc]). Cascara is a high-volume, low-value crop residue that poses an environmental and economic burden in producer countries leading to a high-value proposition if recoverable. Fruit/ionic liquid solutions (0–15% w/w dissolved at 60 °C) were characterized with respect to biomass concentration and shaped into fibers and films coagulated in DI water and methanol. Rheology was assessed using parallel plate rheometry. Cotton/IL mixtures formed better fibers compared to cascara-based solutions, which required 2.6× more biomass in solution for viable fiber formation. At 10% loading, fibers could be produced from untreated coffee fruit with diameters ranging from 18–100 microns. Coffee fruit residues and their precipitated films show that both cellulose and lignin dissolve in pure [DBUH][OAc]. Chemical characterization of cascara and cascara films demonstrates the conveyance of high concentrations of lignin and extracts that result in weaker mechanical properties. Further purification of cascara is required for effective use in structural applications.     

Influence of the longest ethylene and isotactic propylene sequences on crystallization elution fractionation of ethylene/propylene copolymers

Crystallization elution fractionation (CEF) is the newest crystallization-based technique for estimating the chemical composition distribution of ethylene/1-olefin copolymers. Understanding the separation mechanism of CEF for ethylene/propylene copolymers over their full compositional range is challenging because the crystallizabilities of the copolymer chains depend on the longest ethylene sequence and on longest isotactic propylene sequence. We developed a mathematical model to describe the CEF mechanism for ethylene/propylene copolymers over the entire compositional range using population balances for the crystallization and dissolution stages. The joint distribution of longest ethylene and isotactic propylene sequences determines how the copolymer populations crystallize and dissolve. The model was validated with experimental CEF profiles of ethylene/propylene copolymers varying from pure ethylene to propylene homopolymers.     

Application of computer vision technique for on-line monitoring of shrimp color changes during drying

The effects of drying temperature and drying medium velocity on color change kinetics of shrimp viz. lightness (L^∗), redness (a^∗), yellowness (b^∗), total color difference (ΔE), chroma (CH), hue angle (H°), and browning index (BI) were on-lineally investigated. Drying experiments were carried out on dryer equipped with computer vision systems using hot air drying (HAD) temperatures of 50–90 °C and superheated steam drying (SSD) temperatures of 110–120 °C at drying medium velocities of 1–2 m/s. Zero-, first-order, and fractional conversion models were utilized to describe the color changes of shrimps and the fractional conversion model successfully tracked the experimental data. The results showed that the color parameters were significantly influenced by the studied parameters. Lightness of the samples decreased, while other color parameters increased as drying proceeded. Generally, increasing drying medium temperature decreased L^∗ and H°, whereas increased other color parameters. The color characteristic of the SSD finished products were acceptable than the HAD processed samples. Finally, dimensionless moisture content of shrimps during drying was accurately correlated to the color parameters and drying time using a quadratic regression model. Moisture ratio had strong relationship with the lightness change compared with the redness and yellowness variations.