A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

Surface acidity study of Mn+-montmorillonite clay catalysts by FT-IR spectroscopy: Correlation with esterification activity

A simple method for evaluating the surface acidity of different cation-exchanged montmorillonite (mont) clay catalysts, Mⁿ⁺-mont (Mⁿ⁺=Al³⁺, Fe³⁺, Cr³⁺, Zn²⁺, Ni²⁺, Cu²⁺, and H⁺), involving treatment with pyridine is described. After treating with pyridine, the samples were heated at 120 °C and the FT-IR spectra were directly recorded in the region 1650 and 1350 cm⁻¹. The data obtained show the presence of both Lewis and Brønsted acid sites. The activities of the catalysts to bring about Brønsted acid catalysed esterification of succinic acid with iso-butanol to yield di-(iso-butyl) succinate have been studied. The Brønsted acidity data obtained for Mⁿ⁺-mont correlated well with activity in the esterification reaction. The activities of the catalysts were found to decrease in the order of exchange ions Al³⁺ > Fe³⁺ > Cr³⁺ > Zn²⁺ > Ni²⁺ > Cu²⁺ > Na⁺-mont. They also correlated well with the charge to radius ratio of the cations. The catalysts exchanged with trivalent cations showed stronger absorption bands attributed to Brønsted acidity (1540 cm⁻¹) whereas those exchanged with divalent cations showed an increased Lewis acidity (1450 cm⁻¹) and reduced Brønsted acidity along with charge to radius ratio. Zn²⁺-, Cu²⁺- and Ni²⁺-exchanged clays showed an additional peak around 1605 cm⁻¹ which is attributed to the pyridine adsorption on surface sites through its π electrons. The method suggested here to evaluate the acidity is suitable for active sites which are thermally unstable such as water molecules in the hydration shell of a cation in exchanged clay.     

Mechanical,thermal, and dielectric properties of SiCf/SiC composites reinforced with electrospun SiC fibers by PIP

Electrospun unidirectional SiC fibers reinforced SiC_f/SiC composites (e-SiC_f/SiC) were prepared with ∼10% volume fraction by polymer infiltration and pyrolysis (PIP) process. Pyrolysis temperature was varied to investigate the changes in microstructures, mechanical, thermal, and dielectric properties of e-SiC_f/SiC composites. The composites prepared at 1100 °C exhibit the highest flexural strength of 286.0 ± 33.9 MPa, then reduced at 1300 °C, mainly due to the degradation of electrospun SiC fibers, increased porosity, and reaction-controlled interfacial bonding. The thermal conductivity of e-SiC_f/SiC prepared at 1300 °C reached 2.663 W/(m∙K). The dielectric properties of e-SiC_f/SiC composites were also investigated and the complex permittivities increase with raising pyrolysis temperature. The e-SiC_f/SiC composites prepared at 1300 °C exhibited EMI shielding effectiveness exceeding 24 dB over the whole X band. The electrospun SiC fibers reinforced SiC_f/SiC composites can serve as a potential material for structural components and EMI shielding applications in the future.     

Creep behavior and failure mechanisms of CVI and PIP SiC/SiC composites at temperatures to 1650 °C in air

Creep properties of 2D woven CVI and PIP SiC/SiC composites with Sylramic™-iBN SiC fibers were measured at temperatures to 1650 °C in air and the data was compared with the literature. Batch-to-batch variations in the tensile and creep properties, and thermal treatment effects on creep, creep parameters, damage mechanisms, and failure modes for these composites were studied. Under the test conditions, the CVI SiC/SiC composites exhibited both matrix and fiber-dominated creep depending on stress, whereas the PIP SiC/SiC composites displayed only fiber-dominated creep. Creep durability in both composite systems is controlled by the most creep resistant phase as well as oxidation of the fibers via cracking matrix. Specimen-to- specimen variations in porosity and stress raisers caused significant differences in creep behavior and durability. The Larson-Miller parameter and Monkman-Grant relationship were used wherever applicable for analyzing and predicting creep durability.     

Development of a noise prediction software NASEFA and its application in medium-to-high frequency ranges

For the analysis of noise problems in medium-to-high frequency ranges, the energy flow boundary element method (EFBEM) has been studied. EFBEM is numerical analysis method of energy flow analysis (EFA), and solves energy governing equations using a boundary element method in complex structures. Based on EFBEM, a noise prediction software, “noise analysis system by energy flow analysis” (NASEFA), was developed. For effective maintenance, NASEFA is composed of three main modules: the translator, the model converter, and the main solver. The translator changes the FE model to the NASEFA BE model, and the model converter changes the BE model to an EFBE model, including various data, such as structural materials, medium properties, sources, and boundary conditions. NASEFA then solves the acoustic energy density and intensity on boundary and in the field. Moreover, it analyzes interior and exterior noise problems for single and multiple domains in two and three dimensions. Finally, for the validation of the software developed, interior and exterior noise predictions of various structures were performed. The results obtained with NASEFA were compared with those of the commercial SEA program and experiment. From these comparative studies, the usefulness of NASEFA was established.     

An elastic–plastic asperity contact model and its application for micro-contact analysis of gear tooth profiles

This paper presents a continuous elastic–plastic asperity contact model with or without the consideration of friction to investigate the micro-contact properties of gear tooth profiles. The model for normal or side contact analysis is established according to Hertz contact theory and the asperity morphology feature, which yields to similar results as obtained from the model proposed by Chang W.R., Etsion I., and Bogy D.B. (CEB model) and the model proposed by Kogut L. and Etsion I. (KE model). More importantly, this model avoids the constant average contact stress as predicted by the CEB model, and the noncontinuous contact stress and deformation within the ultimate strength as given by the KE model. As a application of the present theoretical model in micro-contact analysis of rough tooth profiles, a finite element model (FE model) for elastic–plastic asperity in normal or side contact is established according to the measured surface parameters of a spur gear pair. It is shown that the extreme point of Von Mise stress of the asperities along the normal vector is ascertained by FE model, and that the extreme point is relative to the initial occurrence of the asperities plastic deformation. Compared with the present theoretical model, the similar normal contact stress along the contact radius is attained by FE model. Though the contact stress isogram in the specific plane in normal or side contact of the asperities is a circle or ellipse respectively when the plastic deformation is expanded from the inside of the asperities to their surfaces, it is in line with the distribution of elastic and plastic region of the theoretical model. Compared with CEB model, KE model, and FE model, the consistent results are attained by the present theoretical model in elastic–plastic asperity contact analysis. The results indicate that the theoretical model is applicable to the elastic–plastic asperity contact analysis on the rough surface of a spur gear drive.     

Preparation and properties of a new porous ceramic material used in clean energy field

At high temperature, the oxide redox reactions of ceria can split H₂O and CO₂ to produce H₂ and CO, so porous ceria with high temperature resistance and high specific surface area has an important foreground in clean energy applications. In this work, a reticulated porous ceria ceramic material with interconnected porous structure was prepared by the impregnation technique using organic polyurethane sponges as template. The influences of pretreated sponge, dipping time length, pore size and sintering temperature on the porosity and strength of the porous ceria ceramics were systematically studied. With the increasing sintering temperature, the glass phase occurred and led to an increase in strength, but an decrease in porosity. Eventually, we analyzed the relationships between porosity and strength of these porous materials, aiming to provide theoretical and practical references for its application in clean-energy field.     

Stochastic model reference predictive temperature control with integral action for an industrial oil-cooling process

This paper presents a stochastic model reference predictive control (SMRPC) approach to achieving accurate temperature control for an industrial oil-cooling process, which is experimentally modeled as a simple first-order system model with given long time delay. Based on this model, the stochastic model reference predictive controller with control weighting and integral action is derived based on the minimization of an expected generalized predictive control (GPC) performance criteria. A real-time adaptive SMRPC algorithm is proposed and then implemented into a stand-alone digital signal processor (DSP). Experimental results show that the proposed control method is capable of giving accurate and satisfactory control performance under set-point changes, fixed load and load changes.     

Iterative circle fitting based on circular attracting factor

An intuitive method for circle fitting is proposed. Assuming an approximate circle(CA,n) for the fitting of some scattered points, it can be imagined that every point would apply a force to CA,n, which all together form an overall effect that "draws" CA,n towards best fitting to the group of points. The basic element of the force is called circular attracting factor(CAF) which is defined as a real scalar in a radial direction of CA,n. An iterative algorithm based on this idea is proposed, and the convergence and accuracy are analyzed. The algorithm converges uniformly which is proved by the analysis of Lyapunov function, and the accuracy of the algorithm is in accord with that of geometric least squares of circle fitting. The algorithm is adopted to circle detection in grayscale images, in which the transferring to binary images is not required, and thus the algorithm is less sensitive to lightening and background noise. The main point for the adaption is the calculation of CAF which is extended in radial directions of CA,n for the whole image. All pixels would apply forces to CA,n, and the overall effect of forces would be equivalent to a force from the centroid of pixels to CA,n. The forces from would-be edge pixels would overweigh that from noisy pixels, so the following approximate circle would be of better fitting. To reduce the amount of calculation, pixels are only used in an annular area including the boundary of CA,n just in between for the calculation of CAF. Examples are given, showing the process of circle fitting of scattered points around a circle from an initial assuming circle, comparing the fitting results for scattered points from some related literature, applying the method proposed for circular edge detection in grayscale images with noise, and/or with only partial arc of a circle, and for circle detection in BGA inspection.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.