Virtual hybrid-FDM system to enhance surface finish

Many rapid prototyping systems which produce prototypes by layer-by-layer material deposition are now commercially available. The layer-by-layer deposition process leads to a stepped surface known as staircase. Staircase formation is a geometric constraint of the layered manufacturing, which can not be eliminated. The presence of staircase on the surface of a prototype detracts from the surface finish and hence restricts functionality of prototypes. It is realized that there is a need to make modifications in RP (rapid prototyping) systems so that prototypes with better surface finish can be produced without incurring high production costs. A virtual hybrid fused deposition modelling system (hybrid-FDM) is proposed in the present work that uses both layer-by-layer deposition and machining. In this system, CAD model is sliced adaptively using limited centre line average (Ra) value as a criterion (Pandey et al. 2003a). Hot cutter machining/ploughing (HCM) (Pandey et al. 2003b) is recommended to machine the build edges (staircase) of ABS material. Numerically controlled x-y traversing mechanism is proposed as an attachment to move hot cutters along the periphery of slices to machine build edges. In this paper, geometrical designs of cutters are proposed. A process planning system to decide the number of layers to be deposited and then machined in order to access intricate features of a part is implemented. The developed system simulates surface roughness, before and after hot cutter machining. An experimental study is carried out by machining the build edges of an axisymmetric FDM part on lathe machine to form a basis for a hybrid-FDM system.     

Intelligent layout planning for rapid prototyping

Significant savings in cost and time can be achieved in rapid prototyping (RP) by manufacturing multiple parts in a single setup to achieve efficient machine volume utilization. This paper reports the design and implementation of a system for the optimal layout planning of 3D parts for a RP process. A genetic algorithm (GA) based search strategy has been used to arrive at a good packing layout for a chosen set of parts and RP process. A two stage approach has been proposed to initially short-list acceptable orientations for each part followed by the search for a layout plan which optimizes in terms of final product quality and build time. The GA uses a hybrid objective function comprising of the weighted measures like part build height, staircase effect, volume and area-of-contact of support structures. In essence it captures the key metrics of efficiency and goodness of packing for RP. The final layout plan is produced in the form of a composite part CAD model which can be directly exported to a RP machine for manufacturing. Design methodology of the system has been presented with some representative case studies.     

Investigation of the accuracy and roughness in the laser powder bed fusion process

Laser powder bed fusion (L-PBF) process has had a rapid growth in the industrial fields because of the capability to manufacture metallic complex shapes. The purpose of this study is to investigate the accuracy and surface roughness of parts manufactured by L-PBF in the AlSi10Mg alloy. The results showed that the choice of parameters of conversion from the CAD model to STL file and the setting of process parameters can affect the accuracy. In the L-PBF process, the staircase effect, inherent in additive manufacturing technologies due to the layered nature of the process, is not visible due to the melting of thin layers of metal powder. The surface roughness is mainly caused by the process parameters, orientation and position of the part with respect to the recoating blade and by the presence of partially fused particles that adhere to the molten part.     

基于成形工具姿态调整的适应性积层制造技术研究

当前的积层制造技术主要采用2.5D切片。成形中台阶效应不可避免，本文提出了一种新的基于3D切片的适应性成形方法。通过增加成形工具的运动自由度，使之能根据实体的几何外形自动调整姿态，从而有效地克服了台阶效应对成形精度的影响。在保持同等成形精度的前提下。采用适应性斜切成形技术可以使用更厚的层片。提高成形效率，实现了成形精度和速度的兼得。     

Deposition of CeO2 films including areas with the different orientation and sharp border between them

Epitaxial films from one material, with sharp borders between contacting regions having different film orientation are grown on one surface of the substrate for the first time. The main reason for the deposition of thin ceria layers with mixed (001) and (111) orientations on a (1 02) sapphire substrate is determined. We suggest that this is related to the availability of surface defects which, in thin near-surface layers, deviate from stoichiometric composition. This in turn is connected with the loss of oxygen.

A technique for influencing CeO₂ film orientation is demonstrated. This involves specific preliminary processing of the substrate, and the selection of oxygen partial pressure during the deposition process.

High quality thin (30–50 nm) “protective” (001) CeO₂ epitaxial layers are prepared on (1 02) Al₂O₃. Structures comprising two epitaxial protective CeO₂ layers, orientations (001) and (111), are made on the base of (0001) and (1 02) sapphire substrates. The interface between the epitaxial layers is <1 000 nm.

Preliminary results using this method are described, and the possibility of creating a “bi-epitaxial” transition in thin YBa₂Cu₃O_7−x layers is explored.     

Enhancing the efficiency of removing support material from rapid prototype parts

The optimal process parameters for removing support material from rapid prototype (RP) part using the Taguchi method are investigated. Four parameters of pH value, solvent temperature, depth of the RP, and distance of the RP part are considered as control factors. The experimental trials based on L₉ (3⁴) orthogonal array are carried out. Based on the results of analysis of variance, the most significant factor is the solvent temperature having a percentage contribution of 83.32%. Through the confirmation experiment, the savings in the removing time can exceed 70% using the optimal parameters. The mechanism of removing support material from RP part is presented in this work.     

基于G-S-G的混凝土结构裂缝识别及监测方法

为解决复杂约束条件下现代混凝土结构裂缝的精准监测问题,提出基于G-S-G混凝土结构裂缝智能识别及监测方法。将灰度共生矩阵理论(GLCM)和自组织特征映射神经网络(SOM)模型结合,并通过数字图像处理技术(DIP)及数字特征筛选法(DFS)辅助分析,研究提高混凝土结构裂缝识别及监测精度的智能方法;并基于工程实例(柱的偏心受压试验),验证方法的可行性及准确性。结果表明,在有限的样本空间下,基于GLCM-SOM的裂缝识别模型,通过构建的标准特征样本集(角二阶矩(ASM)、熵(ENT)等)排除环境因素及孔洞、凹陷等缺陷的干扰,获得较高的识别精度;基于SOM-GLCM的裂缝监测数据显示,筛选出的相关(COR)和聚类阴影(CLS)损伤特性指标与裂缝的发展情况具有良好的线性关系,可作为裂缝延展趋势的敏感特性指标。提出的G-S-G裂缝检测方法,充分结合GLCM与SOM各自的独特优势,建立起精准识别裂缝损伤的网络模型,并对裂缝的发展趋势进行有效监测。研究有助于实现现代混凝土结构裂缝损伤的高精度智能化健康监测。     

一种新型的机器人柔性腕力传感器的研究

介绍了一种新型的机器人柔性腕力传感器,其主要特点在于不仅可以进行快速的被动柔顺,同时可以测量传感器的运动部件相对于固定部件的运动状态以及受力情况。由于被动柔顺降低了定位精度,因而设计了锁紧装置完成传感器刚性和柔性两种状态的切换以适应不同的任务的需要。定义了柔性腕力传感器的运动学问题,并且推导了逆运动学问题。针对正运动学的解析表达式的复杂性,我们利用神经网络的非线性映射能力,采用BP神经网络对正运动学问题进行了求解,并且使用迭代方法提高了数值解的精度。最后,建立了包括上位机在内的信号检测与数据处理系统,将基于神经网络正运动学算法集成在硬件底层实现,减轻了上位机的负担。     

Effect of inter-ply sliding on the quality of multilayer interlock dry fabric preforms

Forming thick, complex shapes with several layers is needed in high technology fields. During forming, defects can occur and have to be taken into account because they can significantly affect the mechanical performance of the part. This experimental study shows that, when working with dry fabric forming, the type and number of defects is a function of the punch geometry, the process parameters, the orientation of the fabric with respect to the punch and the inter-ply friction. Inter-ply friction has a huge effect on the quality of the preform when inter-ply sliding occurs. This inter-ply friction leads to several overhanging yarn shocks that generate high tangential forces, which inhibit the relative sliding of plies. In addition, to reduce the number and amplitude of defects, the layers subjected to severe defects can be placed in the inner position where they are subjected to the compression applied by the upper layers.     

正弦波电压差分测量与多周期策略——阶梯波研究之三

将片段采样概念应用于正弦波电压的差分测量,把多周期策略施加于被测正弦波的频率扩展,通过实验演示上述测量的具体过程,验证方法的可行性。分析了阶梯波差分测量的特点。为适应这个特点,提出了限幅片段采样的概念及相应的方法。由此提出将交流量子电压基准的准确度提高一个数量级至10^-8水平的建议,叙述了它的原理及具体的方法,指出了所需要的条件。     

An intelligent parameter selection system for the direct metal laser sintering process

As one of the promising Rapid Prototyping (RP) processes, the Direct Metal Laser Sintering (DMLS) technique is capable of building prototype parts by depositing and melting metal powders layer by layer. Metal powder can be melted directly to build functional prototype tools. During fabrication, four important resulting properties of interest to the users are: the processing time, mechanical properties, geometric accuracy and surface roughness. By adjusting an identified set of process parameters, these properties can be properly controlled. The process parameters involve: the laser scan speed, laser power, hatch density, layer thickness and scan path. But the relationships between these parameters and their resulting properties are quite complicated. In many cases, the effects of different parameters on the resulting properties contradict one another. In this paper, an intelligent system to assist the RP user to choose the optimal parameter settings based on different user requirements is presented. For the accurate prediction of the resulting properties of the laser-sintered metal parts, a method based on the feed-forward neural network (NN) with backpropagation (BP) learning algorithm is described. Through experiments, some input–output data pairs have been identified. After continuous training by using the data pairs, this NN constructs a good mapping relationship between the process parameters and their resulting properties. The system developed can determine the most suitable parameter settings containing the process parameters and predict resulting properties from the database built based on different process requirements automatically. It is very useful to RP users for saving material cost and reducing processing time.     

Determination of the optimal part orientation in layered manufacturing using a genetic algorithm

Several important factors must be taken into consideration to maximise the efficiency of rapid prototyping (RP) processes. The ability to select the optimal orientation of a build direction is one of the most critical factors in using RP processes, since it affects the quality of the prototyped part, the support structure and the build time. This study aims to determine the optimal part orientation that improves the average weighted surface roughness (AWSR) generated from the stair stepping effect. It also minimises the build time including the structure of the support in fabricating a completely freeform part. To avoid pre-selection operation, which is often troublesome and time-consuming, the genetic algorithm, that considers the fuzzy weight for surface roughness and build time, is used to determine the optimal orientation. The validity of the proposed algorithm is demonstrated by several examples using different RP systems and compared with previous works. The algorithm can help RP users select the best orientation of the part and carry out efficient process planning.     

Analysis of Robot Accuracy Assessed via its Joint Clearance by Virtual Prototyping Method

This paper discusses how joint clearance influences robot end effector positioning accuracy and a robot accuracy analysis approach based on a virtual prototype is proposed. First, a 5-DOF( Degree of freedom) neurosurgery robot was introduced. Then we built its virtual prototype, made movement planning and measured the manipulator tip accuracy, through which this robot accuracy portrait was obtained. Finally,in order to validate the robot accuracy analysis approach which is based on a virtual prototype, the result was compared with that from a model built by robot forward kinematics and robot differential kinematics.The robot accuracy analysis approach presented in this paper gives a new way to enhance robot design quality, and help to optimize the control and programming of the robot.     

Friction stir welding characteristics of different heat-treated-state 2219 aluminum alloy plates

Friction stir welding (FSW) of 2219-O and 2219-T6 aluminum alloys was performed to investigate the effects of the base material conditions on the FSW characteristics. The experimental results indicated that the base material condition has a significant effect on weld morphologies, weld defects, and mechanical properties of joints. In the 2219-O welds, no discernible interface exists between the stir zone (SZ) and the thermal-mechanically affected zone (TMAZ), and weld defects are liable to form in the lower part of the weld. In the 2219-T6 welds, there is visible interface between the SZ and the TMAZ, and a weld nugget with an “onion ring”-like morphology clearly exists. The defects are liable to form in the upper part of the weld. The strength efficiency of 2219-O joints is 100%, while that of 2219-T6 joints is only up to 82%. In addition, the two types of joints have different fracture location characteristics.     

On Surface Finish and Dimensional Accuracy of FDM Parts after Cold Vapor Treatment

Fused deposition modeling (FDM) process is an additive manufacturing technology where objects are manufactured in layers. In the present days, FDM is commercially used to build prototypes, functional components; however, these parts majorly suffer from poor surface quality and dimensional accuracy even for basic part geometries. In the present paper, first the effect of part deposition orientation on surface finish and dimensional accuracy of FDM parts are studied. The part selected for this study is designed in such a way that different primitive geometric features at different directions are present. The parts are built at different orientations (0°, 15°, 30°, 45°, 60°, 75°, 90°) using acrylonitrile butadiene styrene P430 material, and surface finish and dimensional accuracy are measured at different surfaces. Next, the FDM parts are postprocessed by cold vapor treatment of dimethylketone (acetone) and improvement in surface finish and change in dimensional accuracy are investigated. The results show that surface finish of the components is greatly improved by this vapor treatment process with minimal variations in part geometric accuracy after the treatment.     

Analysis of Volatile Bread Aroma for Evaluation of Bread Freshness Using an Electronic Nose (E-Nose)

This paper presents the use of a tin-oxide sensor array and self-organized map (SOM)-based E-nose for analysis of volatile bread aroma and explores its ability to cluster bread odor data according to the freshness of bread. A low cost tin-oxide sensor array based electronic nose system has been used for the classification of state of freshness of bread. The sensor data was acquired for a period of 3 weeks, and an unsupervised self-organizing map (SOM) model was trained using this data to correlate the sensor response to classify the bread as fresh and stale. A comparative evaluation of 3 week' of bread data was carried out using the SOM. The results suggest that the system developed is able to predict the state of bread as fresh and stale up to 98% accuracy if the test bread data sets are of the same week. The classification accuracy reduces to 75-85% if test bread data sets are from different weeks. The model is also applied on three different brands of bread and similar classification results are obtained.     

不规则零件优化排样的神经网络混合优化算法

 提出一种利用人工神经网络求解不规则件排样问题的混合优化方法.该方法首先把排样和制造工艺联系起来,将多边形各边向外扩充,为零件预留加工余量;然后采用自组织特征映射模型（SOM）和Hopfield人工神经网络相结合的方法,运用SOM神经网络对初始在板材内随机排布的不规则零件进行平移,逐步减小不规则零件之间的重叠面积,求得各零件的最优位置,再运用Hopfield神经网络对平移后的零件旋转,进行迭代运算,当能量函数达到稳定状态时,得到各排样零件的最优旋转角度组合,实现自动排样.算法可以解决不规则件和矩形件在规则板材以及不规则板材上的排样问题,实例证明了该算法的有效性和实用性.     

基于阶梯波的周期非正弦电压精密测量——阶梯波研究之四

经典傅里叶变换要求采样数据必须布满整个周期。以周期非正弦波分析为例,提出一种利用规则分布的部分采样数据仍可进行傅里叶变换的新方法。以阶梯波为参考的差分测量用于周期非正弦波,在每个阶梯波台阶上丢弃起始和结束部分的采样以克服过渡过程和吉布斯现象后,高频分量的傅里叶变换将产生明显偏差。为克服这种偏差,将傅里叶变换的基函数(三角函数)作同样的离散,组成相应的影响矩阵,其逆矩阵将能恢复准确结果。由于影响矩阵的阶次是最高谐波次数的2倍,一般情况下求逆十分困难,并需要较大存储空间。提出了使影响矩阵成为稀疏因而简化运算的条件。进一步讨论了基于阶梯波的差分采样所需要的限幅条件在周期非正弦电压下的表现及其应对方法。模拟和演示实验结果表明,该方法可极大减少计算量,仍具有较高准确度。最后探讨了该方法扩展到一般采样缺失情况下的可能性。     

Atomic Structure and Electrical Activity of Grain Boundaries and Ruddlesden–Popper Faults in Cesium Lead Bromide Perovskite

To evaluate the role of planar defects in lead‐halide perovskites—cheap, versatile semiconducting materials—it is critical to examine their structure, including defects, at the atomic scale and develop a detailed understanding of their impact on electronic properties. In this study, postsynthesis nanocrystal fusion, aberration‐corrected scanning transmission electron microscopy, and first‐principles calculations are combined to study the nature of different planar defects formed in CsPbBr₃ nanocrystals. Two types of prevalent planar defects from atomic resolution imaging are observed: previously unreported Br‐rich [001](210)∑5 grain boundaries (GBs) and Ruddlesden–Popper (RP) planar faults. The first‐principles calculations reveal that neither of these planar faults induce deep defect levels, but their Br‐deficient counterparts do. It is found that the ∑5 GB repels electrons and attracts holes, similar to an n–p–n junction, and the RP planar defects repel both electrons and holes, similar to a semiconductor–insulator–semiconductor junction. Finally, the potential applications of these findings and their implications to understand the planar defects in organic–inorganic lead‐halide perovskites that have led to solar cells with extremely high photoconversion efficiencies are discussed.