Global shared-layer blending method for stacking sequence optimization design and blending of composite structures

A global shared-layer blending (GSLB) method is proposed for obtaining manufacturable stacking sequence of composite structures with blending and design rules. The method combines the traditional SLB technique with an evaluation algorithm of spatial variation of panels, where the manufacturability of laminates is enhanced by identifying and minimizing the ply-drops, and controlling the laminate transition drop boundaries. In addition, a blended design scheme is also proposed, which is achieved by using the stacking sequence table technique. A composite wing structure is selected to validate the efficiency and accuracy of the proposed method. Results show that the GSLB method can be used for generating more manufacturable designs of large-scale composite structure with multiple engineering constraints.     

某新型运载火箭助推器热试车吊装与固定方案设计

针对某新型运载火箭助推器的热试车试验,开展吊装固定方案设计,通过关键技术攻关,成功实现中国首次最大推力火箭子级热试车试验的吊装对接与固定,并且吊装固定方案安全可靠、简单易行、经济性好,为后续该新型火箭助推器发射场合练试验积累了宝贵经验。     

Modification of the wood-plastic composite for enhancement of formaldehyde clearance and the 3D printing application

As the formaldehyde is one of the main indoor pollutants, the purpose of this study is to effectively remove indoor formaldehyde pollution by using environmentally friendly 3D printing ornaments. The wood 3D printing filaments cellulose/polylactic acid composite (Cellu/P) was selected as the starting material, and 3-aminopropyltriethoxysilane (APTES) was used for chemical modification to obtain a series of cellulose composite materials with amino groups. The modified composite materials (APTES@Cellu/P) were characterized by Fourier transform infrared, X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy, thermogravimetric analysis, and mechanical tests, and a formaldehyde removal experiment was performed. The feasibility of 3D printing was evaluated, and the process of 3D printing-functionalized customized ornaments was proposed, and then a school emblem was used for modeling, printing, and surface modification. Compared with the commercially traditional activated carbon, 3D printing-customized ornaments of APTES@Cellu/P material has a better formaldehyde removal effect, and can even avoid the secondary pollution that is common to the activated carbon.     

Modeling dynamic construction work template from existing scheduling records via sequential machine learning

Today, construction planning and scheduling is almost always performed manually, by experienced practitioners. The knowledge of those individuals is materialized, maintained, and propagated through master schedules and look-ahead plans. While historical project schedules are available, manually mining their embedded knowledge to create generic work templates for future projects or revising look-ahead schedules is very difficult, time-consuming and error-prone. The rigid work templates from prior research are also not scalable to cover the inter and intra-class variability in historical schedule activities. This paper aims at fulfilling these needs via a new method to automatically learn construction knowledge from historical project planning and scheduling records and digitize such knowledge in a flexible and generalizable data schema. Specifically, we present Dynamic Process Templates (DPTs) based on a novel vector representation for construction activities where the sequencing knowledge is modeled with generative Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Our machine learning models are exhaustively tested and validated on a diverse dataset of 32 schedules obtained from real-world projects. The experimental results show our method is capable of learning planning and sequencing knowledge at high accuracy across different projects. The benefits for automated project planning and scheduling, schedule quality control, and automated generation of project look-aheads are discussed in detail.     

A novel deep generative model based on imaginal thinking for automating design

Automating design faces a thorny problem: insight modeling based on knowledge and experience. In particular, it is difficult for artificial intelligence to perform incomplete conditional reasoning. The deep generative model (DGM) is an emerging approach of machine learning, which typically uses deep networks to learn from various data sets and synthesize new designs. This paper proposes a novel DGM based on imaginal thinking to realize the creative leap from the invisible functional domain to the concrete physical domain. An experiment is conducted to verify the effectiveness of the proposed model in designing wheels for mobile robots in granular media.     

Stochastic model predictive control for remanufacturing system management

Uncertainties in the quality, quantity, and operational time of used products pose a challenge to the management of remanufacturing systems. In addition, it becomes a necessity to optimize the operation of the remanufacturing system to balance the quality of products, remanufacturing efficiency, and service level. In this study, a stochastic discrete-time dynamical model is proposed to represent a remanufacturing system, where the relationship between the market satisfaction, inventory status, and operational actions is explicitly modeled. This includes production and inventory planning, resource allocation and acquisition. To handle uncertainties, a stochastic model predictive control approach is proposed to plan the actions that optimize the remanufacturing efficiency. Our results in the simulation examples show that: (a) without supplies, the remanufacturing system has better stability and robustness than a conventional manufacturing system with the same initial stocks; and (b) with insufficient initial stocks, the remanufacturing system demands fewer and more gradual supplies, thereby keeping the system stable. Finally, a sensitivity analysis is conducted for testing the performance of the remanufacturing system. By changing the operational action capacity, different state equilibria are discovered, which correspond to distinct system response characteristics. The study reveals notable managerial insights and effects of product commonality, demand patterns, and operational actions scheduling on the efficiency of the remanufacturing system.     

Stress relaxation properties of an epoxy-based shape-memory polymer considering temperature influence: experimental investigation and constitutive modeling

Mechanics of Time-Dependent Materials - Epoxy-based shape-memory polymer (ESMP), belonging to viscoelastic thermosets, exhibits strong temperature-dependent relaxation property caused by the glass...     

复合材料L型端框的失效行为

本文将某典型复合材料结构舱体的L型端框简化为单点连接的L型端框试件,通过试验和仿真分析,对试件拉伸载荷工况下的失效形式和承载能力进行了研究。结果表明,拉伸载荷工况下,L型端框失效形式以分层和基体破坏为主,试验与仿真分析承载力偏差为10.1%,具有良好的一致性。依据研究结果,对复合材料L型端框设计进行改进,通过试验发现,连接点局部粘接金属垫片可以有效提升复合材料L型端框的拉伸承载能力。本文的研究结果可作为复合材料L型端框设计的参考。     

基于特征相对贡献度对加权Mel倒谱的改进

在声纹识别系统的搭建过程中,提高识别率的一个重要做法是使语音信号中能够提取出的特征尽可能包含更多的说话人个性特征。为了探究特征参数各分量对识别系统性能的影响,文章基于高斯混合-通用背景模型（GaussianMixture Model-Universal Background Model,GMM-UBM）基线系统,研究了在无噪环境中各维特征组合下的识别率,利用增减分量法定量计算出各维特征分量对识别率的相对贡献程度,并根据贡献度的强弱对各维特征分量进行合理加权,得到了贡献度拟合权重系数,将此系数用于改进梅尔倒谱系数（Mel Frequency Cepstrum Coefficient,MFCC）特征参数。仿真结果表明,对特征参数进行贡献度拟合权重系数加权后,声纹识别的正确率得到了提升。