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Understanding the “real” structure of a nucleating agent (NA) inducing polymer crystallization is the basis for establishing the structure–nucleation performance relationship. N,N′-Dicyclohexyltere...

This study reported novel eco-friendly Ziegler-Natta catalysts containing bio-derived maleic rosinate tri-n-butyl and maleic rosinate tri-n-octyl as internal electron donors for propylene polymeriz...

There is a big error in the traditional method to extract the position and attitude information of the robot. In the process of obtaining the target attitude, a method of extracting the target attitude information of robot arm based on RGB-D data is proposed. The position and attitude of the manipulator target are acquired by depth image processing, and the detected target position is sent to the manipulator control node, and the feature points of the manipulator are extracted. The 3-D mapping is carried out on the acquired RGB image, and the depth and RGB values of feature points, as well as position and attitude information are calculated by using the Gauss mixture model. Finally, the target is extracted by combining the covariance matrix of feature points. The experimental results show that the co-ordinate error and angle error of the robot arm extracted by this method are small. The maximum extraction error is only 28%, which is much lower than the traditional method, which shows that the proposed method is more applicable.

Aiming at the task of capturing the docking ring of non-cooperative target in space, a method for object detection of non-cooperative target based on dual-arm manipulation is proposed. In addition, this paper proposed a system based on visual servo using two arms to capture the docking ring. In this system, the construction of robots formed by two UR manipulators provides services for non-cooperative satellites and meets the basic requirements of space non cooperative target acquisition task. By using Eye-in-Hand cameras and feature-based object detection algorithm, the active measurement of uncooperative spacecraft is realized. The experimental results show that Eye-in-Hand cameras and visual algorithm can be used to extract and track the target features for the non-cooperative target without hook and mark. This active visual guidance method based on features is proved to be feasible.

This paper focuses on space cooperative target identification and ranging using monocular CMV4000 CMOS sensor and Xilinx Zynq-7000 Soc based platform without the operating system that adopts Histograms of Oriented Gradients (HOG) descriptor and Support Vector Machine (SVM) for classification. With Vivado High-Level Synthesis to accelerate IP creation by enabling C specifications to be directly targeted into devices, this implementation allows for two high-resolution images ($2048 \times2048$ pixels) processing per second. The hardware and software co-design of Xilinx Zynq7000 promises the accuracy of space target classification and distance measurement and thus meets the requirement of real-time space application.

In this contribution, novel bio-derived rosin acid ester compounds have been developed and used as internal electron donors to prepare MgCl2 supported titanium catalysts for propylene polymerization. The results of the polymerization showed that tri-n-amyl maleate, triiso-amyl maleate and tri-n-heptyl maleate as internal electron donors in Ziegler-Natta catalysts all have high polymerization activity and stereoregularity. In particular, the Ziegler-Natta catalyst with tri-n-amyl maleate as the internal electron donor has the highest activity (up to 33.4 kgPP·gCat−1·h−1) for propylene polymerization and the isotacticity of polypropylene is as high as 96.8% determined by 13C NMR. Simultaneously, the obtained polypropylene with a relatively broad molecular weight distribution of 9.0 and melt flow rate of 160.3 g/10 min, which is beneficial to processing and has practical industrial application value when used as the basic material of melt blown cloth for medical mask.

For most autonomous landing based on ground cooperation markers, the design of ground cooperation markers is too simple. These markers can provide position-related information when UAV is in a high position, but when UAV is in a low position, it can easily cause the UAV to lose its target and result in landing failure due to the small size of the ground marker and the narrowness of camera's field of view. To solve the above problems, this article focuses on the precise landing of UAV. An improved ground cooperation marker which can be detected in different heights is designed, a relative position coordinate system is established for pose estimation, and a height-adaptive autonomous UAV landing strategy is proposed. Simulation done in the Gazebo environment verifies that the proposed ground marker and control strategy can improve the accuracy of the vision-based autonomous landing of the UAV.

Aiming at the mission requirements of the space-borne platform approaching and measuring rotating non-cooperative target satellites under complex illumination, a method for identifying and measuring rotating satellites based on monocular vision is proposed. This method realizes the recognition and segmentation of the target satellite with the machine learning method of HOG+SVM. Besides, a method of histogram specification based on average illumination judgment is proposed to extract the inherent features on the target, and the relative position of the target satellite is estimated by PnP based on these features. Experimental results show that this method can achieve better performance in recognition and measurement of rotating target satellites under complex illumination with robustness to influencing factors such as changes of pose, scale and lighting.

We propose a method of 3D reconstruction of small-sized object based on Kinect V2 RGB-D camera and turntable, which eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Identification and detection of a QR code are used to calibrate the system, and on this basis, point cloud coordinate conversion and background removal are realized. Our coarse registration algorithm uses the fixed rotation angle of the turntable to construct the rotation matrix between frames. Combined with ICP (Iterative Closest Point) algorithm for precise registration, the object point cloud model is obtained. We achieve a cost-effective, convenient and practical 3D reconstruction process for small-sized objects. Experimental results show that the method can stably and effectively obtain 3D models of objects that are small and difficult to extract features, which has certain application value in product display.

The smart product represents a new type of products, which contains software and hardware components. Many obsolete smart products are required to be recycled each year. In the developing countries, the inappropriate treatment of obsolete smart products contaminates the natural environment. This study aims to coordinate the abilities of social recycling organisations and improve the recovery rate of waste smart products. A framework of the recycling service for smart products is proposed based on the concept of cloud manufacturing. In the framework, a smart product ontology is designed to integrate the lifecycle data generated by the smart product. To decide the recycling choice of the components of smart products with the lifecycle data, a fuzzy-rule-based decision method is proposed. In cloud-based recycling service, the abilities of different recycling factories are virtualised as a pool of recycling resources. The virtualisation is achieved by the recycling resource ontology. In order to selec...

To ensure the reliability of carbon fiber material products and eliminate all kinds of possible defects, it is necessary to take effective means to check their quality. X-ray based nondestructive testing technology combined with image recognition algorithm is considered to be a fast and efficient solution. However, the surface of the processed material is often accompanied by labels which contains various information. These labels are confused in the image and make most of the defect detection methods easy to misidentify. This paper mainly studies the recovery method of product label noises based on image features and application in defect detection. What’s more, the proposed method can effectively and efficiently eliminate noises without affecting the rest of the image information which ensures the correct identification of defects in materials.

A kind of mesoporous hydroxyapatite (HA) with high ordered structure was first synthesized in molecular self-assembly method. In this study, HA nanoparticles with an average diameter of about 12 nm were dissociated into HA molecules in a weak acidic buffer solution (NaH2PO4–Na2HPO4, PBS), and the molecules were assembled to form a HA polymerized framework on the surface of the micelles constructed by cetyltrimethylammonium bromide (CTAB, a template) under hydrothermal treatment. The mesoporous structure was finally formed by calcination to remove the template. High-resolution transmission electron microscopy (HRTEM) observed that there were regular and ordered channels in the mesoporous HA. The nitrogen sorption results of mesoporous HA indicated the specific surface area, total pore volume and maximum pore size distribution of mesoporous HA were 48.81 m2/g, 0.38 cm3/g and 1.93 nm, respectively. The porous material was found to have high adsorption capacities to some metal ions, such as Co2+ (299.46 mg/g), Ni2+ (309.35 mg/g), Cu2+ (248.03 mg/g), Zn2+ (276.11 mg/g) and Cd2+ (192.93 mg/g), and ion exchange and recrystallization of the metal salts were found to occur on the mesoporous material to give high adsorption capacities. The efficient and environment-friendly characteristics of materials open the doors for potential usage in real industrial processes.

Purpose This paper aims to develop methods for generating disassembly tasks for selective disassembly. The disassembly task contains the disassembly information, namely, disassembly direction, disassembly tool and selective disassembly sequence. Design/methodology/approach Ontology is adopted to represent the product, and ontology rules are used to represent the disassembly knowledge. A product ontology model (POM) is introduced on the basis of material, connection matrix and interference matrix. Two types of disassembly knowledge are taken into account, one is the disassembly knowledge of disassembly tool selection and the other is the disassembly knowledge of special connections. Based on the POM and the disassembly knowledge, decision support methods are designed to generate disassembly tasks. Findings A centrifugal pump is used to demonstrate the proposed methods, and the result shows that the methods work well. Research limitations/implications The methods developed in this study are fundamental approaches. The ontology and the ontology rules can be extended with more disassembly knowledge. Originality/value The main contribution of this research is the development of methods for representing disassembly knowledge based on ontology rules and the decision support methods for generating disassembly tasks.

The preparation method of nano-scaled polymeric materials was described in this work. Nano-scaled novel composites of elastomeric ter-polypropylene blended with isotactic polypropylene (iPP) were investigated. The composites were synthesized by zirconium complex/methylaluminoxane(MAO) catalyst system through in-situ polymerization. This novel materials consisted of different lengths of crystallizable iPP segments and rubbery amorphous blocks that polymerized by 1-octene, propylene and ethylene in varying proportions. The mechanical properties of the composites were desired that the iPP segments contributed to the strength and the rubbery amorphous blocks contributed to the flexibility. The result showed that the composites can be used as a kind of elastomer.

In the present work, the synergistic toughening effect of olefin block copolymer (OBC) and a highly effective β nucleating agent (NA)-Calcium salt of pimelic acid (CaPim) on polypropylene random copolymer (PPR) was studied. Mechanical tests showed that with the introduction of 0.1 wt % CaPim, there was almost no change in the low-temperature (below 0 °C) toughness of PPR. Although introduction of OBC could obviously improve the toughness at 23 °C, high content is needed to effectively toughen PPR at low temperature. By coadding OBC and CaPim, PPR/OBC/NA blends showed not only great enhancement of toughness over the temperature range tested but also lower OBC content of undergoing brittle-ductile transition compared with PPR/OBC blends. The crystalline structure, crystallization behavior and phase morphology were investigated to explore the possible synergistic toughening mechanism. The high β-crystal content formed in PPR matrix and the finer distribution of OBC phase might be responsible for the superior...

Urban residents work indoors for a long time, and indoor air quality is becoming an important indicator that residents care about in their work and life. Based on the Internet of Things, this paper designs an indoor air monitoring system, which can monitor and collect various parameters of indoor air in real time, upload them to the data center for analysis, and push them to the user end for display. Meanwhile, a prediction model is established to predict the air quality indicator in the next few hours by taking CO2 as an example, and construct a complete air quality evaluation to provide to users, so that other intelligent terminals or users can adjust the doors and windows, to make ensure the excellent indoor air quality.

The incorporation of fluorine into polyolefin can dramatically improve its own mechanical properties and compatibility with other polymers, which has revolutionized the development of polyolefin industry. However, there were no fluorinated isotactic polypropylene (iPP) on the market which remains a formidable challenge. Here, a novel approach is presented that fluorinated iPP was prepared by free radical reaction using N-fluorobenzenesulfonimide (NFSI) as fluorine transfer reagent and benzoyl peroxide (BPO) as initiator in solution reaction. By conducting the tests of water contact angle (CA), DSC and TGA, the results indicated that the surface property and the thermal stability of fluorinated iPP (F-iPP) were greatly improved. In this contribution, a direct and economical route to fluorinate polyolefin was provided.

The memory effect of semi-crystalline polymers is usually related to the heterogeneity in self-nucleated (SN) melts. In this study, the strong memory effects of syndiotactic polypropylene (sPP) and the heterogeneity in SN melts were systematically investigated in a wide temperature range even above its equilibrium melting temperature (Tmo). The rheological measurements revealed that the SN melts exhibited thermorheologically complex behaviors, indicating that the presence of the heterogeneity in the SN melts. From the rheological plots of phase angle (δ) with the complex modulus (G*), the parameter Δ ln G* δ=45° values were determined and used to compare the change of the degree of thermorheological complexity in the SN melts during the heating process. It was found that the memory effect's efficiency to promote the crystallization process improved as the increase of Δ ln G* δ=45° values, which suggested that the memory effect depended on the heterogeneity in the SN melts. After purifying the sample, the strong memory effect still existed, which indicated that it was not caused by the stabilization effect of impurities. The results of wide/small-angle X-ray scattering and infrared spectroscopy revealed that there were no partial residual crystallites or regular chain conformation in our investigated SN melts. Based on the complex chain structure of our investigated sPP, we speculated that the heterogeneity in sPP melts was caused by the separation between the long crystallizable sequence and the atactic sequence.

In this work, the toughening effect of olefin block copolymer (OBC) on polypropylene random copolymer (PPR) at room and low temperature (0 °C) was investigated. Mechanical tests showed that OBC is an effective impact modifier for PPR. By addition of only 5 wt% of OBC, the notched Izod impact strength of resulting blends at room temperature and 0 °C are 66.5, 9.27 kJ/m2, which are increased 135.8 and 43.9%, respectively. This value for blends containing 10 wt% OBC at 0 °C is 60.2 kJ/m2, which is approximately eight times higher than that of pristine PPR at same temperature. Due to the high melting point of OBC, the decline of rigidity caused by its addition is rather slight. The tensile strength and flexural modulus for blends containing 10 wt% OBC is only 8.3 and 11.3% lower than that of pristine PPR. The investigation of morphology, crystallization and rheological behaviors suggested that the great improvement of toughness might be attributed to the decrease of PPR spherulites size and the excellent compatibility between PPR matrix and OBC dispersed phase. This work offers a simple and effective approach to produce PPR with stiffness–toughness balance. Keywords: Polypropylene random copolymer, Olefin block copolymer, Mechanical properties, Stiffness–toughness balance

A series of heterogeneous catalysts by loading metallocenes on surface of α-Ti(HPO4)2, a kind of solid acid, has been synthesized. Polymerization of alkenes, including ethylene and propylene, based on participation of the heterogeneous catalysts were studied and the results were compared to metallocenes supported on silica gel, α-Zr(HPO4)2 and clay. Higher catalytic activity, larger polymer molecular weight and narrow distribution of polymer molecular weight were obtained. Acidic strength of the support and its influence to metallocenes were studied to discover intrinsic factors in the polymerizations.

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.

Proper recovery of electromechanical products can prevent pollution, improve materials recycling rate and make economic benefits as well. However, a major obstacle is that the product lifecycle information essential for the recovery decision-making is difficult to be collected. This article shows how radio frequency identification RFID can be utilised to record and trace the lifecycle information, hence optimising disassembly process and improving recovery efficiency. The key factors of the integrated method are demonstrated: 1 an assembly–disassembly model is constructed purposefully due to characteristics of RFID; 2 a flexible encoding and storage approach using XML format is proposed uniquely for a universal usage of the limited memory space in RFID tags; and 3 a detailed iteration process for the automatic generation of a near-optimum disassembly sequence is illustrated. In addition, a set of related application programmes are developed, and the whole integrated method is verified by a case study. This integrated method is highly extensible, and it can be applied to various types of electromechanical products.

In the process of industrial automation production and daily life, the flow is an important parameter that often needs to be measured and controlled. In order to ensure the accurate measurement of flow meter, the flowmeter needs to be calibrated before production, during use and after maintenance. However, most of the existing calibration methods are complicated in the calibration process, and there are many human interventions. The functions of the intelligent flowmeter are not fully utilized, and the unified management requirements for equipment during large-scale deployment cannot be met. This paper provides a method for automatic calibration of intelligent turbine flowmeter, which can be applied to a variety of intelligent instruments. In the calibration phase, the flowmeter automatically completes the calibration data collection at each calibration point set by the user. After all calibrations are completed, the calibration data will be stored in the flowmeter inside and it also will be uploaded to the host computer through the wireless network. At the same time, the configuration information can be received remotely. In the measurement phase, according to the internal calibration data and the K value fitting method selected by the user, the flowmeter can realize the non-linear correction of flow measurement and improve the accuracy of measurement results. User also can self-configure the instrument parameters. Compared with the existing calibration and correction methods of flowmeter, it simplifies the calibration process, has a high degree of automation, improves the accuracy of measurement results, and gives full play to the functions of intelligent instruments.

For the nonlinear characteristics of turbine flow sensor and the influence of medium temperature, the traditional compensation method is difficult to apply. This paper uses BP neural network based method to improve measurement accuracy. Firstly, the reason of nonlinearity and the influence of temperature on flow measurement are analyzed theoretically. Then the nonlinear correction and temperature compensation scheme based on neural network is proposed, and different optimization algorithms are used for training. After simulation experiments and analysis, the results show that neural network the maximum reference error of the sensor after compensation is 0.683%. Compared with the traditional least square fitting, the accuracy is greatly improved, which can effectively solve the influence of nonlinearity and temperature change on flow measurement, expand the measurement range of turbine flow meter, and improve the measurement accuracy so as to meet the requirements of industrial use.

Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

The example of the preparation of nano- and micro-scaled, coral-shaped and core-shell topological morphology of copolyethylene particles promoted by the novel heterogeneous non-metallocene catalyst (m-CH3PhO)TiCl3/carbon nanotubes (CNTs) was reported. Mass fraction of titanium component of the catalyst was 4.0 wt% determined by ICP analysis. The catalyst system can effectively catalyze polymerization of ethylene and copolymerization of ethylene with 1-hexene. Morphological examination of the obtained polymer particles was carried out by scanning electron microscope (SEM) and high resolution transmission electron microscope (HR-TEM) technique. The results revealed that the morphology of the nascent copolyethylene particles looked like coral shape with size in micro-scaled and featured the core-shell structure consisting of CNTs as the core and copolyethylene as the shell.

A series of titanium and zirconium complexes with ligands based on di-isopropyl phosphorus-phenylamine and their derivatives were synthesized and characterized. These catalysts were utilized to catalyze the copolymerization of ethylene with N-acetyl-O-(dec-9-enyl)-L-tyrosine ethyl ester with high catalytic activity of 6.63 × 104 g P (mol Ti)−1 h−1 after activation by methylaluminoxane (MAO). The effects of ligand structure, metal atoms (Ti, Zr) and polymerization conditions were investigated in detail. The obtained polymers were characterized by 13C-NMR, DSC, FT-IR, and GPC. The results showed that the obtained copolymer had a high comonomer incorporation rate of 2.56 mol % within the copolymer chain. The melting temperature of the copolymer was up to 138.9 °C, higher than that of the obtained homopolyethylene.

The series of acenaphthylene-1-[2,6-bis(bis(4-fluorophenyl)methyl)-4-methylphenylimino]-2-arylimine derivatives and their dichloronickel complexes were synthesized and fully characterized as well as the single-crystal X-ray diffraction of representative nickel complexes, revealing a distorted tetrahedral geometry. Upon activation with either MAO or Et2AlCl, all nickel complexes showed high activities in ethylene polymerization; moreover, their catalytic systems showed better thermal stabilities on being manipulated at 80 °C as the industrial operating temperature.