Design and validation of robotic bionic eye with multiple flexible ropes parallel mechanism inspired by oculomotor law

Producing a stable and agile bionic eye for visual image acquisition in robotics is a challenging task. In this paper, we design a bionic eye with mirror-symmetric distribution and cross-connection of flexible ropes. This mechanism is based on oculomotor law and the physiological structure of the extraocular muscles (EOMs). Specifically, the basic structural parameters are determined by Listing’s law, and the unique connection of the flexible ropes can realize the functions of the recti and oblique muscles. Furthermore, to mimic the trochlea structure, a pulley mechanism is constructed to permit the free movement of the flexible ropes. Through simulation and physical experiments, it is demonstrated that the bionic eye mechanism can move with agility under the structural parameters. The experimental results indicate that the proposed bionic eye mechanism has a superior motion accuracy of 2.798 mm, which is 6.7% of the maximum motion distance, and the repeatable accuracy of the mechanism can up to 0.210 mm.     

Adsorption of anionic dye on eco-friendly synthesised reduced graphene oxide anchored with lanthanum aluminate: Isotherms,kinetics and statistical error analysis

In the present study we made an effort to deploy eco-friendly synthesized reduced graphene oxide/Lanthanum Alluminate nanocomposites (RGO-LaAlO₃) and Lanthanum Alluminate (LaAlO₃) as adsorbents to remove dye from the synthetic media. XRD, SEM, BET surface area and EDX have been used to characterize the above-mentioned adsorbents. The impacts of different factors like adsorbent dosage, the concentration of adsorbate and PH on adsorption were studied. The best fit linear and nonlinear equations for the adsorption isotherms and kinetic models had been examined. The sum of the normalized errors and the coefficient of determination were used to determine the best fit model. The experimental data were more aptly fitted for nonlinear forms of isotherms and kinetic equations. Pseudo-second-order and Freundlich isotherm model fits the equilibrium data satisfactorily. Methyl orange (MO) has been used as model dye pollutant and maximum adsorption capacity was found to be 469.7 and 702.2 mg g^?1 for LaAlO₃ and RGO-LaAlO₃, respectively.     

Minimization of the Theoretical Error of Input Parameters for a Vapor Permeation Apparatus

All input experimental errors for membrane flux determination were analyzed. The overall error in pressure did not largely influence the determination of membrane flux. The main contribution to errors was ascribed to flow meters and membrane thickness. The choice of an optimal operational range for input parameters led to elimination of at least 2/3 of the theoretical relative error for subsequent evaluation of the permeability of individual species. This is especially valuable for low‐permeable membranes, whereas a very low relative theoretical error can be obtained in the case of highly permeable materials.     

基于GRA-GRNN模型的露天金属矿爆破后矿岩界线位移分析

为了避免露天金属矿爆破后导致爆堆边缘矿石品位贫化损失,需要根据最低品位阈值重新计算矿岩边界,而影响矿岩边界发生改变因素众多,需要确定主要影响因素。因此,利用爆堆爆破前地形方向和爆堆地质数据,构建灰色关联-广义回归神经网络模型(GRA-GRNN)分析爆堆矿岩边界变化主要影响因素。首先对爆堆钻孔品位数据使用析取克里金法进行空间插值,并根据矿山工艺最低品位阈值提取爆破前的矿岩边界;其次,将爆破前后的数字DEM模型进行求差,求得爆破后的爆堆数字DEM模型,并构建爆破前后爆堆数字DEM模型空间分布椭圆,从而确定爆堆爆破后的水平形变方向;对影响爆堆爆破后形变的可能因素进行提取,并应用GRA-GRNN模型选取主要影响因素及对其强度进行分析,并将其结果与BP神经网络模型预测结果进行了对比。从实验结果可知,影响爆堆爆破后形变强度排前三的因素为:爆破前地形方向、爆孔排距和列距,强度分别为0.880、0.760和0.755,预测结果优于BP模型。     

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

非球面超精密磨削误差建模与补偿研究

为提高课题组自研的超精密磨床加工精度,基于多体系统理论,运用齐次坐标变换原理,分析该超精密磨床37项几何误差来源,对非球面超精密磨削的综合误差建模。超精密磨床的多项几何误差元素已在制造阶段标定、补偿,取砂轮对刀误差和砂轮轮廓半径磨损误差作为主要面形误差来源,分别推导其对综合误差的传递函数,分析误差辨识方法,建立误差修正补偿模型,提出基于直接补偿的点补修正法。试验结果表明：建立的综合误差模型正确,根据误差辨识方法和修正补偿模型,修正误差后面形误差显著降低,有效提高面形精度。     

基于干涉原理的机床摆动式回转轴位置精度测量

摆动式回转轴线的角位置精度是五轴机床的关键精度参数之一,由于机床结构限制,采用传统的测量方法很难实现高效、准确的测量。以雷尼绍的带分度工作台的激光角度干涉仪为研究对象,在分析回转轴转角误差干涉法测量原理的基础上,采用同步控制回转轴及线性轴的运动方式,对其应用到摆动式回转轴角位置精度测量的工作原理及测量过程的关键技术进行了详细探讨,获得了典型摆动式回转轴位置精度测量过程中的运动关系模型。在某车铣复合中心的B轴上进行实验,并分析影响测量精度的主要因素,提出了控制措施的建议。结果表明该方法可作为通用方法,推广至其他同类型的激光角度干涉仪,用于测量多种结构五轴机床的回转轴角位置精度,可大幅度提高测量的便携性和高效性。     

基于可补偿误差项的机床几何误差建模与补偿技术

提出一种可分离数控机床刀具与工件之间可补偿与不可补偿几何误差源的方法;依据可补偿误差项建立误差补偿模型;采用激光干涉仪+直角镜的垂直度直接辨识方法检测出几何误差,导出补偿量计算式,选用西门子840DNC的几何误差补偿模块进行补偿实验,其中8项指标补偿后的几何误差减小比例在16.5%～92%,补偿效果显著。实践证明本文作者所提出的机床可补偿几何误差的建模与补偿方法是有效和可行的,其补偿方法同样可以用于不同类型的机床。