一种基于参数样条曲线的增材制造轨迹插值算法探讨

增材制造(3D打印)过程中常常会遇到不规则的连续曲线,传统的处理方法是采用微小的多段折线来逼近曲线。但在曲线过长或者过于复杂时就会产生大量的微小直线段数据,给控制系统的运行造成一定的负担。本文采用参数样条曲线插补的方式,借助参数三次多项式推导出插值曲线段的递推公式,在一定程度上克服上述插值方法的不足,提高加工效率与精度。     

应用于增材制造的连续运动捕捉方法

针对增材制造中出现的三维模型连续运动高速捕捉情况下丢帧的问题,本文提出了一种连续运动捕捉的新方法。该方法的主要内容是：首先基于多视点视频序列得到两个非同构的静态模型,用联合骨架优化方法自动提取两个模型的骨架结构;然后在最短测地线路径约束下插值出中间动作骨架;接着建立变形距离场DDF来调整表面细节过渡;最后对插值点云进行双边滤波和泊松重建。该方法能自然并准确地捕捉快速和大跨度的动作,速度快,精度高。实验的结果表明,采用该方法基本满足各种运动序列高速捕捉时的有效性和实用性。     

基于二次波的增材制造平板缺陷无损检测方法研究

在航天航空领域,增材制造板状结构因其优异特性而得到广泛应用.但是增材制造结构件在制造或者使用过程中极有可能会产生不同类型的缺陷,从而造成安全隐患.本文基于兰姆波在平板中具有多个路径的传播形式,提出了一种利用二次到达波的缺陷无损检测方法,并应用于增材制造平板的缺陷定位.首先,根据兰姆波的频散特性确定激励信号的中心频率和模...     

基于曲面的飞机机翼结构参数化设计

以某型无人机机翼结构设计为例，介绍在计算机辅助三维交互应用（Computer Aided Three-dimensional Interactive Application，CATIA）软件三维设计环境下，基于机翼理论外形曲面的机翼结构参数化设计方法。通过完整数据链的全相关结构设计，实现设计更改过程的全模型自动更新，设计周期缩短，成本降低。     

基于机器学习的增材制造用高温合金设计：研究现状与未来趋势

机器学习辅助材料设计是一种高效的合金设计方法,该方法结合高通量计算/实验技术能够极大推动材料领域的发展。然而,目前材料研究者尚未充分利用该方法。针对上述问题,详细介绍了基于机器学习的高温合金设计的基本方法,包括数据收集与预处理、模型建立与训练、合金性能预测等步骤,阐述了增材制造用高温合金的设计现存困难和亟待解决“权衡裂纹敏感性与高温性能”的关键问题,深入分析了机器学习在增材制造用高温合金设计中的必要性。从高温合金数据获取、性能预测、合金设计等方面梳理了机器学习辅助增材制造用高温合金设计的应用现状,指出了该领域目前存在的数据来源、建模范式、模型的应用与推广等核心问题,并对该领域后续的发展提供了建议。     

参数曲线曲面自由变形的多项式因子方法

为得到理想的造型效果,提出一种空间参数曲线曲面自由变形的方法.首先引入基于多项式的伸缩因子,并构造了空间变形矩阵;然后将变形矩阵或伸缩因子作用于待变形曲线曲面方程,从而得到形变效果.实验结果表明,该方法计算简单、易于控制,可得到较好的变形效果.     

基于NX喷嘴壳体的增材制造技术研究

喷嘴壳体是一种广泛应用于航空领域的极为复杂的金属零件,为了解决喷嘴壳体高强度金属难加工的问题,本文基于NX平台其自带的高级仿真模块与增材制造相结合,提出了极为简便的验证环节,对喷嘴壳体进行了拓扑优化和增材制造流程模拟,最后根据仿真分析结果进行优化。该研究极大地缩短了金属产品生产的周期,在一定程度上展现了NX平台增材制造工艺方法的应用潜力。     

嵌入参数空间的曲面控制自由变形方法

提出了一种新的通过控制参数曲面来实现物体的自由变形的方法，物体通过映射函数被嵌入到曲面的参数空间从而实现变形，同时映射函数的引入增加了变形的控制手段。实验表明该方法具有易于交互和控制的优点，适用于几何造型和计算机动画领域。     

基于整数运算的参数多项式曲面快速生成算法

本文给出了关于参数多项式曲面的一种快速逐点生成算法,并对张量积Bezier曲面给出了具体的生成算法。与已有算法相比,该算法在生成曲面的过程中,只用到整数加减法。     

Bidirectional Evolutionary Structural Optimization (BESO) based design method for lattice structure to be fabricated by additive manufacturing

Unlike traditional manufacturing methods, additive manufacturing can produce parts with complex geometric structures without significant increases in fabrication time and cost. One application of additive manufacturing technologies is the fabrication of customized lattice-skin structures which can enhance performance of products while minimizing material or weight. In this paper, a novel design method for the creation of periodic lattice structures is proposed. In this method, Functional Volumes (FVs) and Functional Surfaces (FSs) are first determined based on an analysis of the functional requirements. FVs can be further decomposed into several sub-FVs. These sub-FVs can be divided into two types: FV with solid and FV with lattice. The initial design parameters of the lattice are selected based on the proposed guidelines. Based on these parameters, a kernel based lattice frame generation algorithm is used to generate lattice wireframes within the given FVs. At last, traditional bidirectional evolutionary structural optimization is modified to optimize distribution of lattice struts’ thickness. The design method proposed in this paper is validated through a case study, and provides an important foundation for the wide adoption of additive manufacturing technologies in the industry.     

Support structure constrained topology optimization for additive manufacturing

There is significant interest today in integrating additive manufacturing (AM) and topology optimization (TO). However, TO often leads to designs that are not AM friendly. For example, topologically optimized designs may require significant amount of support structures before they can be additively manufactured, resulting in increased fabrication and clean-up costs.In this paper, we propose a TO methodology that will lead to designs requiring significantly reduced support structures. Towards this end, the concept of ‘support structure topological sensitivity’ is introduced. This is combined with performance sensitivity to result in a TO framework that maximizes performance, subject to support structure constraints. The robustness and efficiency of the proposed method is demonstrated through numerical experiments, and validated through fused deposition modeling, a popular AM process.     

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.