集装箱船分段制造无修割装配数据关联分析与应用

以集装箱船分段制造修割数据为研究对象,进行数据采集和关联关系提取。精准发掘分段制造中的修割位置、修割数量、修割长度、修割原因、修割耗时和间接影响平台周期等关键数据的关联逻辑。根据数据的完备性、精准性和创造性,为集装箱船分段制造无修割装配精度控制提供可行的管理目标,并确定无修割装配技术优化方案,可增强集装箱船分段制造精度控制策划的综合水平。     

汽车焊接质量检查数字化研究

文章介绍了汽车焊接质量检查数字化的研究方案、数字化监控的网络架构、技术的创新及难点、实现的功能等。焊接质量检查数字化研究能在焊接质量检查方面达到高效、快捷、智能的目的;通过网络技术、大数据以及工业互联网技术等技术搭建焊接质量检查数字化平台;焊接质量检查员工通过此平台能够便捷输入、智能查询,得到焊接质量大数据,分析提升焊接质量;结合先进的智能制造技术,对实际焊接质量检查数字化进行研究,从大数据的角度加强对焊接质量问题的汇总分析和问题解决,提升汽车整体的焊接质量和智能制造能力。     

船体T型材智能装焊系统开发

在传统船舶建造中,T型材安装定位劳动强度高、工作效率低且存在安全隐患.以船体T型材智能装焊系统为开发对象,介绍系统构成,阐述系统关键部件功能,设计系统工艺流程,实现T型材的自动安装定位和焊接.基于自动化定位、抓取、放置和焊接,该系统可大幅降低劳动强度、提高生产效率.     

基于精益生产的制造企业管理创新模式探讨

面对市场竞争日益剧烈,对于制造业企业而言,做好精益生产工作至关重要。本文从精益生产视角切入,以制造企业管理为研究对象,详实阐述精益生产的基本内涵与基本特征,深刻论述精益生产对制造企业发展的重要意义,深入剖析制造企业精益生产改善的实施流程,并尝试从不同层面提出提高企业精益生产水平的发展策略,以期可以为制造企业创新发展提供些许有益思考。     

汽车护板注塑模具三维设计及制造

本文分析汽车护板注塑模具的设计和制造,详细介绍了以UG注塑模为基础的三维设计方法,并通过数控加工对注塑模具的三维制造过程进行了阐述。设计和制造三维注塑模,可以有效地改进模具的设计和生产效率,缩短模具研制时间,在一定程度上帮助了技术人员。     

基于汽车生产制造过程的质量管控措施

对于汽车产品顾客关注的主要是产品价格、配置、质量、售后服务、市场口碑等,而在这诸多因素中客户最为关注的还是产品质量。本文就制造过程质量管理的目标、环节、要素和方法来对汽车制造过程质量管理进行分析,结合生产现场对制造过程中力矩管理、工艺参数管理、关键/特殊工序管理、工艺纪律管理、典型质量问题管理5个方面的管理方法和手段进行论述。     

锂离子电池在新能源汽车中的应用与发展探讨

在当今汽车产业发展技术的革新和能源紧张已经成为世界车企共同面对的问题,各个国家在替代传统燃料汽车的新能源汽车的开发都取得一定的成绩,在应用范围最广的就是锂离子电池。随着我国新能源汽车政策的发展支持,汽车新能源得到了前所未有的发展前景,而随之而来的新能源汽车行业的上游材料也在这种政策利好形势下迎来了发展的最好机遇,世界知名的电池生产企业纷纷在中国设厂。持续增长的新能源汽车市场带动了电池产业发展,但是同时存在无论是纯电动的动力电池,还是以氢材料等燃料电池都存在续航里程和成本控制的挑战。在此形势下,锂离子电池的需求以每年15.7%的速度增长。中国科学院深圳先进技术研究所材料研究中心团队在传统的双碳结构的双离子电池基础上研制出一体化电极锂离子电池,在此基础上得到了研发制造。锂离子电池具有高工作电压、低成本、环保且便于回收等优势很快得到新能源汽车生产厂家的青睐并被应用到车辆中。     

船舶分段制造装备智能化需求分析

针对国内造船企业船舶分段制造装备自动化、智能化应用水平较低的现状，以作业工序的零件打磨、理料、小组立焊接、中组立焊接及喷涂等方面为切入点，分别从路径规划、信息感知、协同作业、智能决策等方面提出船舶分段制造装备的智能化需求，为船舶分段智能制造装备的研制提供必要的目标图像。     

Prediction of the vibratory properties of ship models with realistic structural configurations produced using additive manufacturing

The use of flexible ship models to determine the dynamic behaviour of full-scale ships in waves and to compare the accuracy of numerical predictions has increased in the past few years. Segments attached to a flexible uniform backbone of suitable but simple cross section is the preferred solution. Although such models are relatively easy to manufacture with conventional processes, they do not represent accurately the structural detail, for example, of a container ship. The limitations of conventional manufacturing constraints can be potentially overcome by use of modern technologies such as additive manufacturing. Designing detailed elastic ship models requires the determination of dynamic material properties, in addition to the manufacturer mechanical properties.In this investigation, a detailed but easy-to-implement method is developed, and applied to a uniform container ship-like model, to identify the material properties that are relevant to the calculation of the natural frequencies of 3D printed thin-walled structures. It is demonstrated that modal testing of 3D printed specimens, combined with FEAmodelling, can be used to accurately predict the natural frequencies of much more complex thin-walled structures. This method allows investigators to acquire all information necessary during the design stage of 3D printed structures without having to resort to full material characterisation.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.