基于动态制造资源的产品工艺分工规划研究

针对制造企业联盟的产品工艺分工问题,研究并开发了基于动态制造资源的网络化产品工艺分工规划系统。论文阐述了工艺分工的设计方法,提出了逻辑制造单元、逻辑加工路线等新概念,并对产品工艺分工系统的体系结构与功能组成、基于动态制造资源的零件与逻辑制造单元建模方法、基于知识的逻辑加工路线设计及可执行加工路线生成方法进行了详细讨论。     

基于e-制造模式的动态工艺规划技术研究

e-制造工艺系统要求及时准确地根据资源动态信息进行工艺路线的实时决策。该文将工艺规划过程划分为零件信息输入、特征工艺选择和工艺路线的决策等3个阶段，采用神经网络BP算法解决了特征工艺链模糊匹配问题，将资源动态信息与工艺规则共同作为约束条件，采用GA算法实现基于资源动态信息的工艺路线优化选择，有效地解决了调度中的资源冲突问题。采用Java技术开发了软件原型系统，通过实例分析验证了设计方法的可行性。     

面向制造的单元建模及CAD／CAPP集成

针对CAD／CAPP的集成,提出了面向制造的单元建模方法以及零件模型的数结构,利用可制造对象建立单元库,保证了CAD设计的零件的可加工件,对工艺规划中的工序合并问题进行了论述。     

基于Agent的网络化制造资源优化配置方法研究

针对网络化异地协同制造模式下的制造资源优化配置问题,提出了一种双层次的制造资源优化配置方法,并利用Agent技术和合同网技术建立了双层次制造资源优化配置的实现框架。提出对制造任务用两个层次即LMP(由有序的LMU组成)和LMU(由工序作业组成)来描述。在第一层次,面向LMP中的LMU利用招投标技术实现PMU配置,针对LMP产生一条EMP;在第二个层次面向LMU中的工序作业利用招投标技术实现设备资源的配置,针对LMU产生由设备序列组成的加工路线。最后给出了一个实例说明该方法和实现框架的可行性和有效性。     

面向柔性作业分布式车间的分层调度模型研究

针对多车间分布式制造系统调度优化问题,结合车间实际生产情况,提出一种基于目标级联法和遗传算法的层次调度模型。模型将生产调度过程划分为生产计划层、车间调度层和零件规划层,并将整体时间最短的优化目标划分到各个层次,通过层层优化达到时间最优后反馈至上层,以实现整体调度时间最优。以3个制造车间协调调度问题为例,验证了该模型在零件分配和零件的工艺路线选择上的合理性和有效性。     

工艺驱动计算机辅助面向制造而设计的研究

面向制造而设计是并行工程的主要工具之一。本文从工艺仿真的角度出发，提出了基于知识推理的工艺仿真的计算机辅助面向制造而设计系统，给出了构造层次性规则库和零件可制造性评价的方法。     

基于隶属度函数的车间协同调度方法*

快速发现制造瓶颈是实现车间调度的有效方法。借鉴面向对象的设计思想以及基于模糊数学的最大隶属度原则,提出面向产能瓶颈单元的协同调度,进一步提出产能协同调度二维表示方法,将各类资源归一化,研究并建立了产能协同调度模型。通过实证,运用面向瓶颈的优化调度方法,解决了如何在有限资源的情况下实现复杂制造系统最优化调度的问题。     

制造单元设计中心半边图与拆分重组算法

提出了半边自组图理论,半边附属于顶点,一对半边可结合为边。用半边图来描述复杂优化问题可能解,在此基础上设计了针对图的拆分重组算法,用此算法求解了一个典型的复杂组合优化问题,制造单元设计问题。示例表明,半边图语言能够准确地表达可能解中的复杂结构和各种约束,20台机器50种零件分组实验证明,7次迭代计算即可达到优化目标。     

半边图与挤出吸入算法及制造单元设计

提出半边自组图理论,半边附属于顶点,一对半边可结合为边。用半边图来描述复杂组合优化问题的可能解,在此基础上设计了针对图的挤出吸入算法,用此算法求解了一个典型的复杂组合优化问题,制造单元设计问题。示例表明,半边图语言能够准确地表达可能解中的复杂结构和各种约束,20台机器50种零件分组实验证明,4次迭代计算即可达到优化目标。     

机器可选制造单元设计的半边图挤出吸入算法

计划路径可选的半边图划分问题是一类含有多种局部约束的复杂组合优化问题。设计了针对半边图划分问题的半边图挤出吸入算法,用此算法求解了机器可选制造单元成组设计问题。示例表明,半边图语言能够准确地表达可能解中的复杂结构和各种约束。20台机器20种零件分组实验证明,平均12.4次迭代计算即可达到优化目标。     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

面向CAPP的制造资源预配置研究

以分布式、网络化制造资源库为基础,CAPP给出的工艺信息为条件,提出了一种面向CAPP的制造资源预配置方法及其实现技术。     

The model-based product agent: A control oriented architecture for intelligent products in multi-agent manufacturing systems

The multi-agent control strategy has been previously shown to improve the flexibility of complex, dynamic manufacturing systems. One key component of this strategy is the product agent. The product agent is responsible for autonomously guiding a physical part in the manufacturing system based on its production goals. Though the product agent has been described in previous works, a fully developed software architecture for the product agent that uses a model-based optimization approach has not been proposed. In this work, a product agent architecture with the capabilities to explore the local environment, plan and schedule events based on its knowledge, and request desired actions from the resources in the system is presented and tested.     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

Online merchant resource allocation and matching for open community collaborative manufacturing (OCCM) in mass personalization model

In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods.     

Assessment of reconfiguration schemes for Reconfigurable Manufacturing Systems based on resources and lead time

Reconfigurable manufacturing equipment is developed to meet the growing demand for more agile production. Agile manufacturing technology can improve the turnover of a company if it enables fast market introduction for volume production. Modular reconfiguration, defined as changing the structure of the machine, enables larger variation of products on a single manufacturing system; these solutions are called Reconfigurable Manufacturing Systems (RMS). The quality of RMS, and the required resources to bring it to reliable production, is largely determined by a swift execution of the reconfiguration process. This paper proposes a method to compare alternatives for the ways to implement reconfiguration. Three classes of reconfiguration are defined to distinguish the impact of the proposed alternatives. The procedure uses a recently introduced index method for development of RMS process modules, based on the Axiomatic Design methodology. Weighting factors are used to calculate the resources and lead time needed to implement the reconfiguration process. Application of the method leads to quick comparison of alternatives in the early stage of development. Successful execution of the method was demonstrated for the manufacturing process of a 3D measuring probe.     

Distributed manufacturing scheduling using intelligent agents

Manufacturing scheduling is an optimization process that allocates limited manufacturing resources over time among parallel and sequential manufacturing activities. This allocation must obey a set of rules or constraints that reflect the temporal relationships between manufacturing activities and the capacity limitations of a set of shared resources. The allocation also affects a schedule's optimality with respect to criteria such as cost, lateness, or throughput. The globalization of manufacturing makes such optimization increasingly important. To survive in this competitive market, manufacturing enterprises must increase their productivity and profitability through greater shop floor agility. Agent-based manufacturing scheduling systems are a promising way to provide this optimization.     

钢铁企业生产资源平衡计划系统分析与设计

针对市场需求剧烈变动环境下,钢铁企业如何利用有限产能、平衡资源分配、优化产品组合的问题,提出了通过生产资源平衡计划系统来解决的方法.在系统功能需求分析的基础上,通过数据流图、实体关系图、用例图及时序图构建钢铁生产资源平衡计划系统模型,详细描述系统的设计目标、业务流程及子模块协作机制.该系统以基于数学优化和智能计算的优化计算引擎为核心,综合考虑盈利指标、市场需求、生产能力等因素进行资源平衡,为生产与销售提供决策支持.     

基于生产负荷均衡的多对多订单分配模型

针对产业集群外的多个制造商和产业集群内的多个供应商的订单分配问题,从合理利用供应商剩余生产能力的角度,将多供应商之间的生产负荷率均衡作为订单分配决策的主要准则,建立了多个制造商对多个供应商的订单分配模型。通过对产业集群内注塑机零部件生产企业实例仿真分别从时间及空间维度验证了模型不仅可以减小供应商的生产负荷波动,而且可以均衡各个供应商在每个周期的生产负荷。基于生产负荷均衡的分配策略在保证制造商下达的订单得以顺利完成的同时,考虑供应商群体制造资源的均衡利用,从而提升整个供应商群体的运作水平和效益,进而实现整个供应链的优化。