New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

基于无瞬心包络法的砂轮磨削圆弧直槽的研究

基于无瞬心包络法,提出一种使用CBN圆弧砂轮精确磨削圆弧直槽的方法。从砂轮圆弧母面与工件直槽圆弧面的空间啮合关系出发,采用无瞬心包络法,以两曲面间接触点法矢量方向相同建立啮合方程,通过啮合方程求解得到砂轮初始安装角,以磨削后的工件端截面圆弧圆度和半径误差满足精度要求为目标,设计砂轮安装角调整和控制流程,确定砂轮最终的安装位置和安装角。仿真和实际加工结果表明用圆弧砂轮磨削圆弧直槽工件,通过调整砂轮安装位置和安装角可以控制磨削精度,砂轮不用修形也能实现精确磨削。该方法实现用一种砂轮磨削不同尺寸的工件,减少砂轮的种类,提高生产效率,为注塑机螺杆转子的高效磨削提供了一种可行的方法。     

含硼改性剂对铸造用铝磷酸盐无机粘结剂热硬砂性能的影响

研究了含硼改性剂对铝磷酸盐粘结剂理化性能及热硬砂性能的影响。结果表明：含硼改性剂可有效提高铝磷酸盐热硬砂型（芯）的干强度和较低湿度下的抗吸湿性；随含硼改性剂的增加，粘结荆的密度、粘度增大，稳定性下降。     

气固二相流中粉体的运动分析

空气输送属于气固二相流的范畴.对粉体与气流的相互作用力、单个粒子的运动、管道中粒子群的运动以及静止粉体层的压力损失进行了分析并推导了运动方程.     

Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network

In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.     

The characteristics of arc beam shaping in hybrid plasma and laser deposition manufacturing

1 Introduction With the development of astronautical technology, more and more functions of the components of high-temperature materials are required. In order to meet the demand, far more advanced higher temperature materials, such as the high-temperature alloys or high-temperature melting point materials, should be first developed. In addition, the molding technological process of high-temperature materials should also be developed. Nowadays the traditional high-temperature technological pro…     

Optimization of steel casting feeding system based on BP neural network and genetic algorithm

The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus ineff icient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation(BP) neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×10~6 mm3.     

添加金刚石颗粒的Ni60涂层组织和性能研究

目的研究金刚石颗粒对Ni60涂层的组织硬度和耐磨性的影响,对比无金刚石颗粒的Ni60涂层与含20%金刚石颗粒的Ni60涂层的组织及性能差异。方法将预热后的Ni60和Ni60+20%金刚石粉末在Ar气保护下球磨10 h,对球磨后的粉末进行造粒,过筛后,选出粒度低于200目的粉末作为喷涂材料,并在钢基体表面进行火焰喷涂,制备Ni60涂层和含20%金刚石颗粒的Ni60涂层,对获得的涂层进行X射线衍射分析、扫描电镜观察、显微硬度和耐磨性测试。结果通过火焰喷涂获得了组织致密性较好的Ni60涂层和含20%金刚石颗粒的Ni60涂层。抛光后,Ni60涂层的断面整洁,无粗糙区域,而含20%金刚石颗粒的Ni60涂层断面出现大面积粗糙区域。Ni60涂层的显微硬度约为694.2HV,含20%金刚石颗粒的Ni60涂层的显微硬度约为891.8HV。在载荷6 N、转速为1000 r/min的条件下,Ni60涂层每10 min的磨损量为10.7×10-5 g/mm2,20%金刚石颗粒的Ni60涂层每10 min的磨损量为9.6×10-5 g/mm2。结论由于金刚石颗粒的硬度非常高,经过砂纸打磨和抛光后,含20%金刚石颗粒的Ni60涂层断面不会像纯金属涂层那样出现光洁、整齐的形貌,而是在涂层局部区域出现较粗糙的形貌。通过对比Ni60涂层和20%金刚石颗粒的Ni60涂层的力学性能,20%金刚石颗粒的Ni60涂层的显微硬度高于Ni60涂层,耐磨性比Ni60涂层好。在低载荷、高滑动摩擦速率的条件下,涂层以微量去除的磨损方式进行。     

液体辅助激光微孔加工研究进展

随着微孔加工技术的逐渐成熟,激光微孔加工的应用越来越广泛,但依靠单一激光束进行微孔加工仍存在一些问题,尤其是在深孔加工方面,出现了以激光束为主、多能量场辅助的复合打孔技术,并逐渐成为了热点。针对液体辅助激光微孔加工研究领域,总结了水基辅助激光打孔、水基超声振动辅助激光打孔、水基超声?磁场辅助激光打孔和电解液/水射流辅助激光打孔等方法。在水基的基础上,加入了超声、磁场和温度场,使得辅助场变得多元化,在多层面上进行复合加工。介绍了不同辅助加工方法的去除材料机理及加工后材料特性的变化,水起到冷却的作用,但在水层下会形成空化气泡,超声振动可以击溃气泡,磁场和温度场为材料残渣提供了能量,具体表现在热效应、材料去除速率、打孔深度、重铸层及裂纹等方面。影响微孔质量的因素有微孔锥度、深径比、孔的圆度、重铸层厚度、热影响区、微裂纹和粗糙度等,主要对微孔锥度、深径比及其他指标进行了分析,总结了加工方法对微孔质量的影响。     

Chaotic maps based on binary particle swarm optimization for feature selection

Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective is to remove irrelevant features in the feature space and to correctly identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully to solving feature selection problems. In this paper, two kinds of chaotic maps—so-called logistic maps and tent maps—are embedded in BPSO. The purpose of chaotic maps is to determine the inertia weight of the BPSO. We propose chaotic binary particle swarm optimization (CBPSO) to implement the feature selection, in which the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier for evaluating classification accuracies. The proposed feature selection method shows promising results with respect to the number of feature subsets. The classification accuracy is superior to other methods from the literature.