基于制造特征的产品族零件工时定额制定方法

为了保证产品族零件工时定额制定的通用性和高效性, 提出了以零件制造特征作为工时载体的工时定额制定新方法。该方法通过建立零件的制造特征模型, 研究了基于零件制造特征的零件相似性判定, 根据零件相似性判定划分了零件组, 对每个零件组内所有零件的工时及其影响因素进行了描述, 并构建了零件组相应的工时描述平台。通过神经网络技术的运用建立了零件的工时神经网络模型, 并结合MATLAB软件对零件工时进行了估算, 实现了产品族零件工时定额的快速制定。最后, 通过案例阐述了新方法的具体实现过程。     

MC环境下基于加工特征的零件工时定额研究

工时定额是大规模定制生产模式下企业确定产品交货期和提高顾客满意度的重要参考依据。为更好地支持大规模定制生产的企业工时定额的制定,提出一种大规模定制环境下基于加工特征的零件工时定额的制定方法。在此方法中,运用面向对象方法将零件组内的加工特征进行分类、编码,建立加工特征信息模型,以便于零件加工特征的检索;采用神经网络技术,结合MATLAB软件针对编码系统中每一个最底层的加工特征建立对应的工时模型;根据零件加工特征的编码检索各个加工特征的工时模型,估算每个加工特征的加工工时,从而得到整个零件的工时定额。通过与传统的工时定额方法对比,验证了该方法的准确性和快速性。     

MC环境下基于案例推理的零件相似度与工时估算研究

在MC环境下,为了使零件工时快速且准确的被估算出,以案例推理理论为基础,研究了零件相似度与工时之间的内在关系,提出了零件相似度的工时估算新方法。首先,通过CASE检索法和零件拓扑结构相似度在规范的案例库中检索并筛选出符合要求的相似零件样本;其次,根据零件相似性机理,确定零件相似度的内涵,构建零件相似度的计算模型,即零件拓扑结构相似度计算模型和零件特性相似度计算模型;最后,通过Matlab软件确定零件相似度与工时之间的关系函数,从而实现定制零件加工时间的快速估算,并用实例验证了该方法的有效性和可行性。     

可制造性驱动的三维CAD模型相交制造特征识别方法

为了实现CAD/CAPP/CAM系统的有效集成,提出一种可制造性驱动的三维CAD模型相交制造特征识别方法.首先通过启发式规则对加工面进行可达性分析,计算加工面可行刀具轴向空间;然后采用融合制造语义的加工面聚类算法构建加工区域子图;最后以加工区域子图为制造特征痕迹,结合标注语义信息对加工区域子图进行优化合并,从而实现制造特征的识别.实验结果表明,该方法能够有效地实现复杂相交制造特征和复杂零件的制造特征识别,制造特征识别性能可满足工程应用中的需求.     

MC环境下基于零件定制程度的工时定额研究

为了解决大规模定制生产环境下定制零件工时定额制定存在的问题,提出一种基于零件定制程度的工时定额方法。此方法首先分析了影响零件工时的主要特征,采用神经网络技术得出各主要特征的权重;然后运用模糊综合评判法计算基于零件主要特征的零件定制程度;最后通过事例推理技术(case-based reasoning,CBR)检索相似零件,根据相似零件的定制程度与工时信息,运用MATLAB软件建立零件工时的数学模型,计算出定制零件的工时。以某企业模具的平面安装面的加工时间计算为例对该方法进行验证,结果证明该方法具有较高的准确性和可行性。     

制造网格中制造资源描述机制研究

为实现资源共享和协同工作,制造业对公共技术及服务平台的需求越来越强烈。将网格技术运用到制造业领域,提出了制造网格的概念。基于本体的知识集成方法,改变原有数据库集成中使用中间代理、软件接口等技术所造成的语义信息不清、信息丢失等缺陷,为企业实现制造资源共享和集成提供了解决方法和理论依据。     

支持快速扩散制造的制造资源配置平台研究*

在分析国内外网络化制造研究成果的基础上,结合武器装备快速变批量生产的需求,对武器装备快速扩散制造的概念和特征进行了介绍,并给出满足快速扩散制造要求的制造资源内涵及其概念模型。为了达到短时间内快速组织制造资源进行生产的目标,本着利用外部资源、使用内部技术的原则,提出了面向快速扩散制造的制造资源配置平台解决思路,并提炼出制造资源配置平台的体系结构和若干关键技术,从而实现问题研究范围的界定。     

并行工程下基于特征的零件可制造性及其评价方法研究*

结合并行工程的思想,对零件可制造性进行较详细的分析,提出并行工程环境下基于特征的可制造性分析方法,研究建立基于特征的可制造性零件定义模型及评价体系。阐述了基于特征的可制造性的评价方法,认为可制造性评价是一个多层次的决策问题,建立了基于制造约束规则的加工可行性定性评价和基于模糊综合评判的加工可行性定量评价两种评价方法,并给出了解法。利用上述研究方法,全面合理地对零件的可制造性进行评价和反馈,从而指导产品设计。最后以涡轮叶片榫头特征的可制造性分析为应用实例,说明了该方法的有效性和实用性。     

制造网格中制造资源描述机制研究

为实现资源共享和协同工作，制造业对公共技术及服务平台的需求越来越强烈。将网格技术运用到制造业领域，提出了制造网格的概念。基于本体的知识集成方法，改变原有数据库集成中使用中间代理、软件接口等技术所造成的语义信息不清、信息丢失等缺陷，为企业实现制造资源共享和集成提供了解决方法和理论依据。     

基于深度学习的孔特征可制造性分析方法

针对传统基于知识库及规则库的零件可制造性分析方法柔性差,以及现有基于深度学习的可制 造性分析方法无法给出零件具体不可制造原因的现状,提出一种基于深度学习的零件可制造性分析方法。首先, 通过数字化建模技术构建大量带有具体可制造性类别标签的三维 CAD 模型,并进行点云提取,从而构建深度 学习所需数据集;然后,基于 PointNet 网络结构搭建面向孔特征可制造性分析的深度学习网络,并完成网络的 调参及训练;之后通过与基于体素表示的三维卷积神经网络(3D-CNN)及已有方法进行对比,说明所搭建的点 云深度学习网络具有更好的鲁棒性和较低的算法时间复杂度;最后通过一个实例零件对网络的实际性能进行检 验,对孔特征进行可制造性分析,识别出不可制造的孔特征,并说明其原因。实验结果表明,该方法能够在保 证较高识别准确率同时得出特征不可制造的具体原因,具有更大的使用价值。     

Manufacturing cost estimation for machined parts based on manufacturing features

The purpose of this paper is to present a feature-based cost estimating system for machined parts. Since cost estimation provides designers with valuable information, it must be quick and accurate. In this paper, manufacturing features are classified into four categories. Cost estimation for all features in a category is based on manufacturing activities. Early cost estimations did not consider the manufacturing activity, hence they were not accurate. Machining cost is proportional to machining time, which includes operational time and non-operational time. Operation time includes rough cutting time and finish cutting time. Tool approach time and non-operation time are taken from past experience and approximated for modification into mathematical forms. This paper develops a cost estimation system based on manufacturing features. It is an approximation system which inputs information on machining activities.     

蚂蚁算法在面向零件的网络化制造中的应用研究*

为了有效地实现网络制造环境下资源共享与优化配置,提出了协同制造链的概念,指出协同制造链是一种围绕零件制造过程的网络化制造动态联盟.在此基础上给出了协同制造链的定义和形式化描述,并以某航空发动机关键零件为具体应用研究对象,详细分析了协同制造链的构建和演化过程;同时,对协同制造链构建和演化过程中的关键问题--协同制造链排序进行了重点研究,建立了该问题的数学描述模型,设计并实现了一个基于蚂蚁算法的协同制造链排序策略.最后通过实例验证了该策略的有效性.     

Fuzzy logic based method to measure degree of lean activity in manufacturing industry

Lean manufacturing is gaining popularity as an approach that can achieve significant performance improvement in the industry. However, the application of lean manufacturing is not an easy process. To reach the level of full implementation of lean manufacturing takes a long time and during that time the continuous improvement must be made. In the process of continuous improvement, lean manufacturing assessment is required. One form of assessment is to measure the degree of lean implementation. However, it is the complexity involved in the measure of degree of leanness. This complexity arises due to (a) the inherent multi-dimensional concept of leanness (b) unavailability manufacturing practice database that can be used as a benchmark in assessing the degree of leanness and (c) the necessity for the application of subjective human judgement on lean practices which involve vagueness and bias due to variation of evaluator's knowledge and experience. In this paper a method to deal with the multi-dimensional concept, unavailability benchmark and uncertainty, which arises from the subjective and vague human judgement for the measurement of degree of leanness, is proposed. The multi-dimensional concept involving a variety of components of lean practices is measured in order to arrive at a measure for the lean activity of a given organization. It is constructed from primary and secondary data involving a comprehensive literature review and validated with interviews with a set of sample organizations representing the entire spectrum of the industry. The vagueness of subjective human judgement on degree of application of lean practices is modelled by fuzzy number in conjunction with an additional consideration related to the length of lean practice implementation and the use of multi-evaluators. Value stream mapping is used in scoring the degree of implementation of lean so the use of benchmark is not necessary. Some results from an initial survey from a sample of respondents from the manufacturing industry in Indonesia are presented to illustrate the applicability and potential strength of the proposed method.     

A self-organizing neural network approach for the design of cellular manufacturing systems

The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.     

基于ATC和GA的可重构制造单元分级调度研究*

为了优化以零件族为单位进行生产的可重构单元内单个零件族的加工时间和协调各零件族同时完工以实现整体调度所用时间最短,提出一种分级调度算法.该算法将调度过程分为三层,即时间决策层、分配决策层和路径决策层.以时间决策层为最终优化目标,通过将时间分解至分配决策层再至路径决策层,下层时间达到最优后反馈至上层,层层优化以实现整体调度时间最优.最后通过实例验证该算法在分配各零件族的机器数量和零件的加工路径中的合理性和有效性.     

Optimization of layered manufacturing process for reducing form errors with minimal support structures

Layered manufacturing (LM) has evolved from the rapid prototyping (RP) method to a mainstream process for manufacturing high precision parts for aerospace, automotive and medical industries. LM inherits from RP several advantages including the ability to produce complex shapes, low cost and elimination of special tooling, as well as disadvantages of poor surface finish, poor dimensional accuracy, high build time and high waste. As LM is increasingly being accepted as a traditional manufacturing process, it has to consistently manufacture parts with minimal errors while keeping the material utilization to a minimum. This paper analyzes the effect of part orientation on two types of form errors, namely, cylindricity and flatness errors. An algorithm to calculate the optimal orientation for minimizing flatness and cylindricity errors is developed and tested with the help of two test cases. However, an optimal orientation for minimum form errors may result in a greater utilization of support structures which increases the material consumption in LM processes and therefore should be avoided. A voxel-based approach for calculating support structures has been developed in this paper which is then applied to minimize the volume of support structures while minimizing the cylindricity and flatness errors of the part features. This combined optimization is applied on two test parts and the results are provided.     

面向制造任务的云制造虚拟车间构造方法

针对云制造模式下快速选择和组织相关制造资源、保证制造任务执行的问题,提出一种面向制造任务的云制造虚拟车间构造方法。该方法将制造过程抽象为制造任务执行链,链中的节点对应制造设备云服务或检验云服务,链中的有向边对应物流云服务;并通过行业域、地域和类型域来组织管理云服务,以构造规模较小的候选云服务集,同时减少功能匹配、性能匹配、价格匹配和时间匹配的计算量,达到快速构建云制造虚拟车间的目的。算例分析表明,相比其他方法,该方法能够在更短的时间内完成云服务的选择,并保证所选云服务的服务质量（QoS）在相关域中是更好的。     

Digital Twin-driven machining process for thin-walled part manufacturing

Thin-walled parts are widely used in the aerospace, shipbuilding, and automotive industry, but due to its unique structure and high accuracy requirements, which leads to an increase in scrapped parts, high cost in production, and a more extended period in the trial machining process. However, to adapt to fast production cycles and increase the efficiency of thin-walled parts machining, this paper presents a Digital Twin-driven thin-walled part manufacturing framework to allow the machine operator to manage the product changes, make the start-up phases faster and more accurate. The framework has three parts: preparation, machining, and measurement, driven by Digital Twin technologies in detail. By establishing and updating the workpiece Digital Twin under a different status, various manufacturing information and data can be integrated and available to machine operators and other Digital Twins. It can serve as a guideline for establishing the machine tool and workpiece Digital Twin and integrating them into the machining process. It provides the machine operator opportunities to interact with both the physical manufacturing process and its digital data in real-time. The digital representation of the physical process can support them to manage the trial machining from different aspects. In addition, a demonstrative case study is presented to explain the implementation of this framework in a real manufacturing environment.     

A modular flexible scalable and reconfigurable system for manufacturing of Microsystems based on additive manufacturing and e-printing

Digital manufacturing technologies [1] are gaining more and more importance as key enabling technologies in future manufacturing, especially when a flexible scalable manufacturing of small medium series of customized parts is required. The paper describes a new approach for design manufacturing of complex three dimensional components building on a combination of digital manufacturing technologies such as laminated objects manufacturing, laser and e-printing technologies. The micro component is made up of stacks of functionalized layers of polymer films. The concept is currently developed further in the project SMARTLAM [2], [3], funded by the European Commission. The manufacturing system is based on a flexible, scalable and modular equipment and application features approach which enables the manufacturing of different small size batches without tool or mask making in short time. Different modules can be combined by defined hardware and software interfaces. Avoiding time consumable and difficult programming caused by manufacturing a new conceptual approach a Function-Block Runtime (FORTE) executes generated control application platform-independently and coordinates component module functionalities. The control system is designed to integrate all processes as well as the base platform with features far beyond ordinary PLC systems. One aspect is the use of process data out of the data acquisition system to simulate and optimize the processes. These results are incorporated into the main machine control system. Another aspect is the vision system for flexible quality control and closed-loop positioning control with visual servoing.The paper shows the overall concept of SMARTLAM and exemplarily demonstrates the control system as well as the modular equipment approach by the example of the control system for alignment of different stacks and inspection system.