Fatigue in bamboo

This paper describes the results of an experimental programme to determine the fatigue behaviour of bamboo. Bamboo is subjected to cyclic loading, both in the plant itself and subsequently when the material is used in load-bearing applications in the construction industry. However, there is currently no data in the literature describing fatigue in this material. We found that sections of bamboo culm loaded parallel to the culm axis did not undergo fatigue failure: samples either failed on the first loading cycle, or not at all. By contrast, fatigue was readily apparent in samples loaded in compression across the diameter of the culm. The number of cycles to failure increased as the cyclic load range decreased in a manner similar to that found in many engineering materials: fatigue occurred at applied loads as small as 40% of the ultimate strength. Two different species of bamboo were tested and found to have different ultimate strengths but similar high-cycle fatigue strengths. Finite element analysis was used to help understand the progression of fatigue damage and the effect of stress concentration features. Some tentative design rules are proposed to define stress levels for the safe use of bamboo, taking fatigue into account.     

Bamboo biochar-catalytic degradation of lignin under microwave heating

Abstract

Lignin biochar-catalytic depolymerization using biochar Fe-600, Fe-800, Ni-600, Ni-800 catalysts under microwave-heating (180?°C for 30?min) was explored in an ethanol/formic acid (1:1) media. Non-catalyst depolymerization was also studied and compared with the biochar-catalysts results. Characteristics of the bio-char catalysts were analyze by BET, XRD, and FT-IR. GPC, FT-IR, and MALDI-TOF MS spectrometry were also used to characterize the depolymerization products. The experimental results showed that the S_BET, V_t, and V_mec and average pore diameter of the biochars are considerably dependent on the preparation temperature and type of cation (Ni²⁺ or Fe³⁺). The maximum yield of bio-oil product was obtained as 85?wt% with the addition of biochar Ni-600 and the total amount of oligomers or monomers with a molecular weight of 164 to 446 reaches 80.4%.     

Robotics in the factory of the future: AI in manufacturing: The NBS AMRF as an intelligent machine

Within its Automated Manufacturing Research Facility (AMRF), the U.S. National Bureau of Standards (NBS) is addressing research issues in interface standards for the fully automated factory of the future for the production of small batches of discrete parts. Consisting of robots, machine tools and computers, the AMRF is being integrated using a real-time, sensory-feedback, data-driven hierarchical control architecture. As such, the AMRF is a research tool for investigating the manufacturing enterprise as a system of intelligent machines. This paper: (1) describes the AMRF in terms of its real-time control system architecture; (2) notes the role of symbolic languages, knowledge-representation, sensory-processing and other aspects of artificial intelligence in its development; and (3) speculates on further application of AI in future intelligent manufacturing systems similar in form to the AMRF.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

Introducing autonomous aerial robots in industrial manufacturing

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahía de Cádiz (CBC) Airbus D&S factory in fully working conditions.     

设计模式在地质统计学建模软件的应用

针对构建具有自主知识产权的地质统计学建模软件的需要,设计该软件的基本结构.通过定义对象管理树、被管理时象和管理对象等概念,将设计模式的方法引入到该软件的设计和实现中,并给出了工厂方法的具体实现.该研究可以提高建模软件的代码复用、可靠性和可扩展性.     

基于工厂建模的冶金质量模型

描述一种基于工厂建模的冶金质量模型,其核心是以建模的方式创建冶金行业的物理工厂、生产工序、冶金产品等行业库。针对每种钢铁产品,以质量建模的方式关联具体的各类工艺要求,目的是既要保证产品质量稳定,又要保证产品符合标准和用户特殊要求。这种模型提升了冶金制造执行系统(MES系统)中产品规范和冶金规范的管理模式,形成了一套可供积累和扩充的行业库以及模型化的"桌面动态工厂",从而使工厂的生产过程更透明、更可控,可配置性更强,为冶金企业高效生产、提高核心竞争力提供设计思路和平台支撑。     

永宏PLC在自动三维弯管机系统中的应用

本文着重介绍了利用永宏FBs系列可编程控制器实现自动三维弯管机控制系统的设计。重点阐述了该系统的工作原理、硬件结构和软件设计，实践证明该弯管机系统实用性强、使用方便。     

工厂车间通风、空调系统的优化与改造

工厂车间通风、空调系统的运行效果会直接影响室内空气的成分和性质,进而影响产品质量和生产效率.本文对河南轮胎厂子午胎车间通风、空调系统存在的问题进行了分析和探讨,并结合现场测试结果,提出了优化和改造方案,经过运行调试,达到了满意的效果.