零部件通用对企业竞争战略的影响

零部件通用是目前研究的一个热点问题,但是绝大多数文章都是针对零部件通用对库存水平的影响。本文从战略的角度,分析了零部件通用对企业竞争战略的影响,发现它不仅对降低费用有着积极的影响,而且对缩短新产品开发周期、制造过程重组等方面也有着积极的作用。因此,零部件的通用性已经从战术上的节约费用上升为战略上的竞争武器。     

Development and implementation of a NURBS curve motion interpolator

This paper deals with the issues of development and implementation of a real-time NURBS interpolator for a six-axis robot. Using an open-architecture controller system as a testbed, a real-time NURBS curve interpolator was developed, implemented and tested. Sample runs were conducted with the resulting trajectories measured in real-time during robot motion. The resulting trajectories are analyzed, discussed and compared with those from a commonly used point-to-point approximation technique. The real-time NURBS curve interpolator's feasibility, advantages and related issues are also discussed.     

汽车制造用切削液的技术特点及发展趋势

介绍了汽车制造用切削液的技术特点及发展趋势：主要包括环保对切削液的影响、新材料和新工艺发展对切削液使用性能的推动，以及集中供液系统的应用维护技术和管理模式等内容。     

制造资源混合粒度优化组合方案求解技术

为实现对云请求端制造需求的快速响应并提供最理想的制造云,在定义制造云服务(manufacturing cloudservice,MCS)的基础上,提出了MCS混合粒度动态优化组合方法.通过对制造服务需求的解析,将MCS按照粒度从大到小的顺序进行匹配,实现了云池资源的初次过滤;然后构建基于总服务成本、服务质量和交易期的制造云多目标优化数学模型,并引入Epsilon-SPEA2优化算法对该优化问题进行求解,从而快速高效地获得Pareto最优解;采用基于改进优劣解距离(technique for order preference by similarity to an ideal solution,TOPSIS)的动态多属性决策方法对Pareto最优解所代表的互为非支配的MCS组合方案进行评价排序,筛选出最优的MCS组合方案.最后结合某区域模具公司群所能提供的制造服务以及相应历史数据,依据客户需求进行MCS的动态优化组合仿真,验证了文中方法的可行性和实用性.     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

In-situ point cloud fusion for layer-wise monitoring of additive manufacturing

Additive manufacturing (AM) has received an increasing attention in the manufacturing sector, owing to its high-level design freedom and enhanced capability to produce parts with complex geometries. With advances in AM technologies, the role of AM has been shifting from rapid prototyping to viable production-worthy manufacturing of functional parts. However, AM processes are highly inconsistent, and the lack of quality assurance significantly hampers the broader adoption of AM. Most existing techniques for AM online monitoring focus on the detection of conspicuous defects, such as under-fills and cracks. They are limited in their ability to detect layer surface variations induced by miniature process shifts. The objective of this study is to develop a new layer-wise monitoring framework for AM quality assurance based on in-situ point cloud fusion. Specifically, online 3D structured-light scanning is used to capture the surface morphology from each printed layer. The collected point cloud is partitioned, and the morphological patterns in local regions are delineated with a new affinity measure to evaluate the conformity to the reference. A deep cascade model is further introduced to leverage the local affinities for the identification of abnormal patterns on the printed layers. Finally, a statistical control chart is constructed for process monitoring and the identification of miniature shifts. Simulation and real-world case studies using the fused filament fabrication (FFF) process are conducted, and experimental results have demonstrated the effectiveness of the developed framework. It has a great potential to be implemented in diverse AM processes with a wide variety of materials for mission-critical applications.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.