A piezoelectric model based multi-objective optimization of robot gripper design

The field of robotics is evolving at a very high pace and with its increasing applicability in varied fields, the need to incorporate optimization analysis in robot system design is becoming more prominent. The present work deals with the optimization of the design of a 7-link gripper. As actuators play a crucial role in functioning of the gripper, the actuation system (piezoelectric (PZ), in this case) is also taken into consideration while performing the optimization study. A minimalistic model of PZ actuator, consisting different series and parallel assembly arrangements for both mechanical and electrical parts of the PZ actuators, is proposed. To include the effects of connector spring, the relationship of force with actuator displacement is replaced by the relation between force and the displacement of point of actuation at the physical system. The design optimization problem of the gripper is a non-linear, multi modal optimization problem, which was originally formulated by Osyczka (2002). In the original work, however, the actuator was a ‘constant output-force actuator model’ providing a constant output without describing the internal structure. Thus, the actuator model was not integrated in the optimization study. Four different cases of the PZ modelling have been solved using multi-objective evolutionary algorithm (MOEA). Relationship between force and actuator displacement is obtained using each set of non-dominated solutions. These relationships can provide a better insight to the end user to select the appropriate voltage and gripper design for specific application.     

Phosphorus- and silicon-containing amino curing agent for epoxy resin

Iranian Polymer Journal - Current research work focuses on the synthesis of phosphorus- and silicon-containing amine curing agent (PSA) for epoxy resins. PSA was synthesized using phenyl phosphonic...     

Optimization of the ironmaking-steelmaking sequence in an integrated steel plant having non-linear and distributed elements

A mathematical technique is proposed for the optimization of large scale metallurgical operations whose mathematical models contain linear, nonlinear, and possibly distributed elements. The computational procedure consists of optimization of the nonlinear or distributed subsystems using special purpose techniques. These results are then incorporated into a linear program representing the system as a whole. The overall scheme involves an iterative procedure, through which the discrepancies are reconciled between the subsystem and total system optimization. It is thought that the technique is a practical means for optimizing actual large scale metallurgical operations. To illustrate this fact an example is given dealing with the optimization of the primary end of an integrated steelplant.     

Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states

We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans. A motif is defined as a particular sequence of postures that recurs frequently. The animal''s changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a probabilistic behavioural annotator, the eigenshape annotator (ESA). Probabilistic annotation avoids rigid threshold values and allows classification uncertainty to be quantified. We apply eigenshape annotation to both larval Drosophila and C. elegans and produce a good match to hand annotation of behavioural states. However, we find many behavioural events cannot be unambiguously classified. By comparing the results with ESA of an artificial agent''s behaviour, we argue that the ambiguity is due to greater continuity between behavioural states than is generally assumed for these organisms.     

Solution-processed white graphene-reinforced ferroelectric polymer nanocomposites with improved thermal conductivity and dielectric properties for electronic encapsulation

The recent surge in graphene research has stimulated interest in the investigation of various two-dimensional (2D) nanomaterials, including 2D boron nitride (BN) nanostructures. Among these, hexagonal boron nitride nanosheets (h-BNNs; also known as white graphene, as their structure is similar to that of graphene) have emerged as potential nanofillers for preparing thermally conductive composites. In this work, hexagonal boron nitride nanoparticles (h-BNNPs) approximately 70 nm in size were incorporated into a polyvinylidene fluoride (PVDF) matrix with different loadings (0–25 wt.%). The PVDF/h-BNNP nanocomposites were prepared by a solution blending technique and characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), polarized optical microscopy (POM) and scanning electron microscopy (SEM). In addition, the thermal conductivity and dielectric properties of the nanocomposites were investigated. The incorporation of h-BNNPs in the PVDF matrix resulted in enhanced thermal conductivity. The highest value, obtained at 25 wt.% h-BNNP loading, was 2.33 W/mK, which was five times that of the neat PVDF (0.41 W/mK). The thermal enhancement factor (TEF) at 5 wt.% h-BNNP loading was 78%, increasing to 468% at 25 wt.% h-BNNP loading. The maximum dielectric constant of approximately 36.37 (50Hz, 150 °C) was obtained at 25 wt.% h-BNNP loading, which was three times that of neat PVDF (11.94) at the same frequency and temperature. The aforementioned results suggest that these multifunctional and high-performance nanocomposites hold great promise for application in electronic encapsulation.     

Temperature prediction of heat sources using machine learning techniques

This paper explores the use of machine learning algorithms, such as XGBoost, random forest regression, support vector machine regression, and artificial neural network (ANN), which are employed for predicting temperatures of rectangular silicon heaters with dummy elements. A combination of these machine learning algorithms can predict better results over individual algorithm. Silicon heaters are equipped on an FR4 substrate board for cooling under forced convection in a horizontal channel. COMSOL Multiphysics 5.4 software is used for all the three-dimensional numerical simulations. Heat transfer at the solid and fluid interface is studied using a module based on conjugate heat transfer and nonisothermal fluid flow. Dummy elements are coupled with heated sources to evaluate heat transfer and analyze the flow of fluid. The study is performed with 2.5 m/s velocity and a uniform heat flux of 5000 W/m². The study is aimed at predicting and comparing results of support vector regression (SVR), ensemble learning with ANN to explore optimal configuration. Results indicate an agreement of less than 10% between the simulated and predicted temperatures. It is also found that SVR has given the best results compared with XG Boot and ANN when analyzed individually. The programming for these algorithms is performed using the Python programming language.     

A −40-dB EVM 20-MHz subsampling multistandard receiver architecture with dynamic carrier detection,bandwidth estimation,and EVM optimization

In this work, a reconfigurable multistandard subsampling receiver with dynamic carrier frequency detection and system-level EVM optimizations is proposed. Ideal software defined radio (SDR) receivers promise complete flexibility at the expense of high-performance analog-to-digital converters (ADCs) that are challenging to implement in current technologies for low-power applications. This scenario leads to the research of digital intensive sampling receivers with discrete-time signal processing (DTSP) implemented in analog domain. This approach makes it feasible to move channel selection filtering and dynamic gain adaptability from analog to digital domain. The proposed receiver employs subsampling down-conversion along with subband filters to dynamically detect the carrier frequency of the incoming signal, estimate its bandwidth, and identify if the signal is present in one of the target standard bands. This carrier detection provides a unique capability to reconfigure the receiver dynamically. Additionally, in this work, system-level EVM optimization is proposed considering frequency synthesizer phase noise, IQ mismatch, sampling frequency selection and block-level gain, noise, and nonlinearity. The RF front end of the proposed receiver is modeled in Verilog-AMS whereas the digital signal processing is implemented in Simulink-Matlab. The complete receiver has been verified to detect and process three different bands belonging to three different standards (GSM, UMTS, and WLAN) with the carrier frequency ranging from 0.9 to 2.5 GHz. Test signals with 4-QAM modulation, maximum bandwidth of 20 MHz, and input-dynamic range from –109 to –20 dBm is utilized to demonstrate the receiver performance including an EVM of –40 dB.     

Experimental exergy and entransy analyses on designed and fabricated crossflow heat exchanger

A crossflow heat exchanger (CFHEx) is designed and fabricated in a workshop. For designing this heat exchanger (HEx), the number of passes, frontal areas, HEx volumes, heat transfer areas, free-flow areas, ratios of minimum free-flow area to frontal area, densities, mass flow rates of flowing fluids, maximum/minimum heat capacities, heat capacity ratio, outlet temperatures of hot/cold fluids, average temperatures, mass velocities, Reynolds numbers, and convective heat transfer coefficients are evaluated by considering Colburn/friction factors. After fabrication of the HEx, effectiveness, exergy destruction, entransy dissipation, entransy dissipation-based thermal resistance, entransy dissipation number, and entransy effectiveness for hot/cold fluids sides are found at different flow rates and inlet temperatures of fluids. By experimental results, optimum operating conditions are found, which gives maximum effectiveness and entransy effectiveness but minimum rates of exergy destruction, entransy dissipation, entransy dissipation-based thermal resistance, and entransy dissipation number for the fabricated CFHEx. This study is concluded as follows: minimum exergy destruction and entransy dissipation rates (ie, 3.061 kJ/s·K and 1125.44 kJ·K/s, respectively) are found during experiment 2. Maximum entransy effectiveness of hot/cold fluids (ie, 0.689/0.21) is achieved in experiment 1. Moderate values of entransy dissipation number (ie, 4.689), entransy dissipation-based thermal resistance (ie, 0.04 s·K/J), exergy destruction (ie, 3.845 kJ/s·K), and entransy dissipation (ie, 1374.04 kJ·K/s) rates are found during experiment 1. Maximum effectiveness (ie, 0.4) for the fabricated HEx is also obtained through experiment 1. After comparative analyses, it is found that experiment 1 provides optimum results, which shows the best performance of the fabricated HEx.