High temperature sensing using higher-order-mode rejected sapphire fiber gratings

In this paper, we report the fabrication of higher-order-mode rejected fiber Bragg gratings (FBGs) in sapphire crystal fiber using infrared (IR) femtosecond laser illumination. The grating is tested in high temperature furnace up to 1600 degree Celsius. As sapphire fiber is only available as highly multimode fiber, a scheme to filter out higher order modes in favor for the fundamental mode is theoretically evaluated and experimentally demonstrated. The approach is to use an ultra thin sapphire crystal fiber (60 micron in diameter) to decrease the number of modes. The small diameter fiber also enables bending the fiber to certain radius which is carefully chosen to provide low loss for the fundamental mode LP₀₁ and high loss for the other high-order modes. After bending, less-than-2-nm resonant peak bandwidth is achieved. The grating spectrum is improved, and higher resolution sensing measurement can be achieved. This mode filtering method is very easy to implement. Furthermore, the sapphire fiber is sealed with hi-purity alumina ceramic cement inside a flexible high temperature titanium tube, and the highly flexible titanium tube offers a robust packaging to sapphire fiber. Our high temperature sapphire grating sensor is very promising in extremely high temperature sensing application.     

Surface‐treatment effects of a sapphire substrate by He+‐, Ar+‐, and Xe+‐ion implantations for GaN epitaxial layers

Abstract— The structural, electrical, and optical properties of GaN epilayers grown on various ion‐implanted sapphire(0001) substrates by MOCVD were investigated. GaN or AlN buffer layers and pre‐treatment were indispensably introduced before GaN‐epilayer growth. The ion‐implanted substrate's surface had decreased internal free energies during the growth of the ion‐implanted sapphire(0001) substrates. The crystal and optical properties of the GaN epilayers grown in ion‐implanted sapphire(0001) substrates were improved. Also, an excessively roughened and modified surface caused by ions degraded the GaN epilayers. Not only the ionic radius but also the chemical species of implanted sapphire(0001) substrates improved the properties of the GaN epilayers grown by MOCVD. It is obvious that the ion‐implanted pre‐treatment of sapphire(0001) substrates can be an alternative pre‐treatment procedure for GaN deposition and has the potential to improve the properties of the GaN epilayers on sapphire(0001) substrates.     

CBIMS: Case-based impeller machining strategy support system

Impeller machining strategies cannot be easily formalized due to the complex, overlapping and twisted shapes that form impeller blades. Skilful machining process planners may generate appropriate machining strategies based on their experiences and previous machining data. However, in practice, most shop floor data for impeller machining is not well-structured and it cannot be used effectively by process planners to produce the required machining strategies and process plans. This paper presents the development of a case-based impeller machining strategy support system (CBIMS) that employs case-based reasoning (CBR) to obtain an efficient machining strategy for an impeller by using the existing machining strategies from the shop floor. The CBIMS generates impeller machining strategies through a stepwise reasoning process considering the similarities of the blade shapes and machining regions between existing impellers and a new one. The system can provide a process planner with machining strategies such as tool specifications, machining area partitioning, and the machining parameters including feed rate, depth of cut, RPM and machining tolerance. A case study is provided to demonstrate that the CBIMS can generate useful machining strategies while ensuring that it can be effectively used to support the process planner.     

A CAD model for microstrips on r-cut sapphire substrates

Simple and correct formulas are derived for calculation of an effective isotropic dielectric constant, which characterizes the wave propagation in microstrip lines on anisotropic substrates made of r-cut sapphire crystals. The r-plane is extensively used for epitaxial growth of high-T_c superconductor YBa₂Cu₃O₇ films as applied to microwave integrated circuits. In contradistinction to previous models developed for z-cut sapphire, the nondiagonal dielectric permittivity tensor of r-cut sapphire is used. The effective dielectric constant of a microstrip line on r-cut sapphire substrate is found as a function of the strip-line geometry and the orientation of the crystallographic axes with respect to the symmetry plane of the microstrip line. © 1994 John Wiley & Sons, Inc.     

Social network analysis for optimal machining conditions in ultra-precision manufacturing

Ultra-precision machining (UPM) technology is extensively applied to manufacture top quality products with high precision level and complicated geometry. As complicated machining factors affect the surface quality of machined components in UPM, large numbers of experiments for understanding the influences from particular machining factors are needed, leading overestimate or underestimate of significance of machining factors at certain machining conditions and raising of experimental cost. For these reasons, a crucial approach is urged to adapt for providing a fast track to an optimal machining condition. In this study, social network analysis (SNA) is introduced firstly to develop UPM network, which the network shows the relationship between dominant machining factors in UPM. A complicated UPM network containing interdependencies between each machining factor is generated by SNA. The determinations of network metrics in the UPM network support the selection of optimal machining factors under various machining conditions. Furthermore, the constructed UPM network using SNA provides the complete framework of dependencies in UPM for well predicting the machining outcomes when particular machining factors are adjusted in practical situations. The study contributes to offering a detail guideline for constructing machining strategies or experimental plans to efficiently achieve desired machining outcomes.     

A knowledge base model for complex forging die machining

Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometrical features. Technological data and topological relations are aggregated to a geometrical feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.     

A flexible and effective NC machining process reuse approach for similar subparts

NC machining process reuse is widely accepted as an effective strategy for engineers to generate the process plan with less time and lower cost. However, there has been very little research on how to reuse the NC machining process of similar subparts. As a result, most reusable NC machining process still has to remain in the repository as tacit knowledge, which can easily get lost due to oblivion. This paper proposes a novel NC machining process reuse approach for similar subparts in which existing NC machining process cases are described in association with machining features. First, a feature-based parameter-driven model is established to formalize the links between information imbedded in the machining feature and the parameters of cutting tools, drive geometries, and machining strategies. Then, the NC process parameter-driven characteristic of similar feature is revealed from the perspective of machining geometry, machining precision of the feature, and cutter geometry. Moreover, an NC process reusability assessment approach of similar pocket/subpart is presented using the pocket’s medial axis transform. Finally, the NC machining process inheritance mechanisms are explored to implement the NC machining process reuse automatically and efficiently. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.     

CAD model of effective dielectric constant of microstrip line on M-cut sapphire substrate

Simple and correct formulas in regard to frequency dispersion are derived for the calculation of an isotropic effective dielectric constant for the microstrip line on m-cut sapphire substrate. The formulas were verified by comparison to the results of full-wave analysis based on the equivalent surface impedance approach. The temperature dependence of the dielectric permittivity of sapphire is taken into account.     

一种新型电客式蓝宝石压力传感器

介绍一种高精密、高稳定、线性度佳、结构简单的新型电容式蓝宝石压力传感器.以圆形c面(0001)取向蓝宝石片作为压力传感器的膜片和基片,并且采用两种方案改善普通电容式蓝宝石压力传感器的非线性特性.第一种方法是在一个基片中央区电极内制作一个圆形无金属电极区(圆形无电极区的直径与中央区电极的直径具有特定比率),提高线性度,改...     

Machining feature recognition and tool-path generation for 3-axis CNC milling

This study develops an effective method for identifying machining features. While recognizing features, the workpiece is sliced at some assigned positions. The sectional curves of the workpiece faces and slicing plane constitute the feature profiles. Not only the isolated machining features but also the intersecting machining features can be identified by the information from these intersection profiles. Moreover, the recognized machining features can be employed for scheduling the manufacturing sequence. Different kinds of tool paths can be automatically generated for various machining features to improve the cutting efficiency.     

加工面点云数据深度学习的加工特征自动识别

加工特征识别是实现CAD/CAPP/CAM系统集成的关键技术.针对传统基于符号推理加工特征识别模式存在鲁棒性问题,提出一种基于加工面点云数据深度学习的加工特征自动识别方法;基于PointNet点云识别框架,构建了一个面向加工面点云数据的加工特征自动识别卷积神经网络;通过收集CAD模型中的加工特征面集和采样点云,构建了适...     

Machining activity extraction and energy attributes inheritance method to support intelligent energy estimation of machining process

An energy-efficient intelligent manufacturing system could significantly save energy compared to traditional intelligent manufacturing systems that do not consider energy issues. Intelligent energy estimation of machining processes is the foundation of the energy-efficient intelligent manufacturing system. This paper proposes a method for machining activity extraction and energy attributes inheritance to support the intelligent energy estimation of machining processes. Fifteen machining activities and their energy attributes are defined according to their operating and energy consumption characteristics. Activities and energy attributes are extracted mainly from NC program supplemented with blank dimensional information. An effective extraction method of activities and energy attributes is the basis for the intelligent energy calculating of machining process. Based on an investigation on the extraction procedure of activities and energy attributes, energy attributes inheritance method is further discussed. Four types of energy attribute inheritance rules are summarized according to the different inheritance characteristics. Based on these four types of inheritance rules, the energy attributes can be transmitted from activity to Therblig as effective inputs of Therblig-based energy model of machining processes. The proposed methodology is finally demonstrated through two machining cases.     

A Data-drivenParameter Planning Method for Structural Parts NC Machining

Structural parts are generallyused to compose the main load-bearing components in various mechanical products, and are usuallyproduced by NC machining where the machining parameters heavily determine the final production quality, efficiency and cost. Due to the complex structures and high precision requirements, a large amount of human interactions are usually required to modify the machining parameters generated by existing optimisation model-based or expert system-based methods, which will induce unstable machining quality and low efficiency. This paper proposes a data-driven methodfor machining parameter planning by learningthe parameter planning knowledge from thehigh-qualityhistorical processing files. An attribute graph is first defined to represent the part model. Then for each of the machining operations in the historical processing files, the machining parameters are correlated to a sub-graph that refers to the faces to be machined in this operation. By this way, a graph dataset of machining parameters could beconstructed from the historical processing files, and graph neural networks (GNN) are established to learn the planning models for machining parameters. The proposed method provides an end-to-end strategy for constructing machining parameter planning models thus human interactions can be greatly reduced and the performance of the models are able to be improved as the increase in historical processing files. In the case study, the historical processing files of aircraft structural parts machining are used to train the GNN models for planning cutting width, cutting depth and machining feedrate, and the prediction accuracies reach 95.50%, 94.79%, 95.02% respectively.     

Iso-planar piecewise linear NC tool path generation from discrete measured data points

This article presents a method of generating iso-planar piecewise linear NC tool paths for three-axis surface machining using ball-end milling directly from discrete measured data points. Unlike the existing tool path generation methods for discrete points, both the machining error and the machined surface finish are explicitly considered and evaluated in the present work. The primary direction of the generated iso-planar tool paths is derived from the projected boundary of the discrete points. A projected cutter location net (CL-net) is then created, which groups the data points according to the intended machining error and surface finish requirements. The machining error of an individual data point is evaluated within its bounding CL-net cell from the adjacent tool swept surfaces of the ball-end mill. The positions of the CL-net nodes can thus be optimized and established sequentially by minimizing the machining error of each CL-net cell. Since the linear edges of adjacent CL-net cells are in general not perfectly aligned, weighted averages of the associated CL-net nodes are employed as the CL points for machining. As a final step, the redundant segments on the CL paths are trimmed to reduce machining time. The validity of the tool path generation method has been examined by using both simulated and experimentally measured data points.     

A general model for machinable features and its application to machinability evaluation of mechanical parts

This paper is intended to reveal the rationale in the machinability evaluation and to present an effective, systematic approach to the assessment of the machined parts in the early stage of design. By examining the inherent shaping mechanism of machining processes, a geometric feature model, termed ‘machining volume’, resulting from the cutter's movement that mimics the cutter's real motion trajectory in machining, is proposed with which a set of feature derivatives can be affiliated. The geometric and topological patterns of machining volume permit to capture and convey machinability constraints on a part shape, leading to a new, simple machinability evaluation method centered on machining volume. This method is dedicated to two kinds of inspection on the geometry of a part in design: one is to search for unmachinable surfaces that are beyond the capability of machining processes, and the other to detect machining interference between a cutter and a part. It shows a more useful means to characterize machinable features that result in the machinability evaluation with ease and efficiency.     

三角网格模型分治加工中区域分割算法的研究

提出分治加工策略以保证复杂三角网格模型数控加工同时具有较高的加工效率和加工精度;针对分治加工的需求,提出一种将机械零件三角网格模型分割成具有加工意义区域的算法。算法采用半边数据结构,基于区域生长原理,以二面角结合刀轴矢量为区域生长的驱动信号,实现了三角网格模型内子加工区域的快速分割。为了避免过分割现象,实现了小区域或误判区域的优化合并处理算法。运行实例表明了该算法能够有效实现加工模型的区域分割。     

用变长度染色体遗传算法优化加工路径的方法

加工路径优化问题属于一类特殊的旅行商问题（TSP）,针对此问题将加工路径细分为点、线段、曲线段及闭合曲线等加工要素,并提出一种基于变长度染色体遗传算法的优化方法。该方法将每个点编码为一个二元组用以表示各种加工要素,其交叉和变异操作能对一些线进行分割和合并,使加工路径能得到更大程度的优化。仿真结果表明,该方法具有良好的优化效果,可以显著地缩短辅助运动路径的长度。     

On the role of complexity in machining time estimation

Early cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has only a limited correlation with machining time unless restrictive assumptions are made on part types and machining processes. The objective of the paper is to improve the estimation of machining time by combining complexity with additional parameters. For this purpose, it is first shown that three factors that influence machining time (part size, area of machined features, work material) are not fully captured by complexity alone. Then an optimal set of predictors is selected by regression analysis of time estimates made on sample parts using an existing feature-based method. The proposed parametric model is shown to predict machining time with an average percentage error of 25% compared to the baseline method, over a wide range of part geometries and machining processes. Therefore, the model is accurate enough to support comparison of design alternatives as well as bidding and make-or-buy decisions.

     

Automated surface subdivision and tool path generation for -axis CNC machining of sculptured parts

As an innovative and cost-effective method for carrying out multiple-axis CNC machining, -axis CNC machining technique adds an automatic indexing/rotary table with two additional discrete rotations to a regular 3-axis CNC machine, to improve its ability and efficiency for machining complex sculptured parts. In this work, a new tool path generation method to automatically subdivide a complex sculptured surface into a number of easy-to-machine surface patches; identify the favorable machining set-up/orientation for each patch; and generate effective 3-axis CNC tool paths for each patch is introduced. The method and its advantages are illustrated using an example of sculptured surface machining. The work contributes to automated multiple-axis CNC tool path generation for sculptured part machining and forms a foundation for further research.     

Quick segmentation for complex curved surface with local geometric feature based on IGES and Open CASCADE

Complex curved surface parts with local geometric feature are usually critical parts in high-end equipments. However, the processing for this kind of parts is usually difficult or inefficient due to the adoption of difficult-to-machine material and special structure. Current approaches cannot satisfy the rapid development of high-end equipments. Due to the existence of the local geometric feature for the parts, processing such parts with constant machining parameters is less applicative, restricting the improvement of machining efficiency. By separating the local geometric feature and generating tool path for the local geometric feature and the remaining processing area separately, the more efficient machining with variable machining parameters will be obtained for the complex curved surface with local geometric feature. In this way, the quick segmentation for the complex curved surface with local geometric feature is of great importance to the NC machining with variable machining parameters for this kind of parts, and a quick segmentation system is developed based on Initial Graphics Exchange Specification (IGES) and Open CASCADE (OCC) platform in this study. The complex curved surface model in IGES format is firstly imported into the system and then trimmed into independent surface patches. After computing the feature size of each surface patch, the segmentation for the complex curved surface is achieved by sorting and classifying the surface patches according to their feature sizes. Taking the whole impeller with small splitter blades for an example, the experimental result shows that the segmentation of small splitter blades from the whole impeller is successful and a serialized processing program could be generated, and then the whole impeller could be machined precisely and efficiently with NC equipment. In the machining experiment, it is proved that the machining with various machining parameters can improve the efficiency by 28.18% in the comparison experiment, 20.14% and 12.33% in the estimation. The research provides an important foundation for the high quality and more efficient machining of the complex curved surface with local geometric feature.