VirMIC—一个基于Internet的IC虚拟制造环境

该文展示了一个Internet环境下的集成电路虚拟制造环境VirMIC,着重于系统的三个核心模块,即电路性能仿真与成品率优化模块、工艺线仿真与调度模块和产品决策模块,电路性能仿真与成品率优化模块,建立了一个基于OO-DCE的技术CAD环境,以这个环境为依托,通过协调效益和成品率进行了工艺流程的可制造性优化设计;工艺线仿真与调度模块,以一定的工艺流程和制造系统配置为基础,建立制造系统Petri网模型,从而进行系统性能仿真和生产调度,对制造系统的整体性能进行优化;产品决策模块以前两个模块为基础,对如何选择合作伙伴,如何在合作伙伴之间分配用户提交的制造负荷,为构成“虚拟企业”提供指导性信息,以上三个模块构成了Vir-MIC系统的功能核心。     

Agent-based distributed manufacturing control: A state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.     

大数据环境下MES作业计划与调度能力云服务化研究

云制造技术给制造企业带来机遇的同时,也为其制造执行系统MES的设计与实现带来了新的挑战。为了解决单件小批MES中作业计划与调度优化问题,首先设计了一个从作业计划静态制定,到作业执行情况实时监控与主动感知,再到异常事件智能响应,最后到作业调度动态调节的闭环体系结构。接着针对异常信息实时获取与异常事件发现、异常事件智能化处理以及作业计划与调度优化算法计算能力服务化三个子问题,依次进行了问题分析并给出了技术解决方案。最后,以哈尔滨电机厂为案例对象,综合利用IEC/ISO 62264标准、大数据分析与挖掘方法以及由虚拟化、服务化和SOA等组成的云计算技术实现了单件小批MES作业计划与调度综合优化系统,验证了上述理论与方法的有效性。     

Applications of agent-based systems in intelligent manufacturing: An updated review

Agent technology has been recognized as a promising paradigm for next generation manufacturing systems. Researchers have attempted to apply agent technology to manufacturing enterprise integration, enterprise collaboration (including supply chain management and virtual enterprises), manufacturing process planning and scheduling, shop floor control, and to holonic manufacturing as an implementation methodology. This paper provides an update review on the recent achievements in these areas, and discusses some key issues in implementing agent-based manufacturing systems such as agent encapsulation, agent organization, agent coordination and negotiation, system dynamics, learning, optimization, security and privacy, tools and standards.     

CPS-enabled and knowledge-aided demand response strategy for sustainable manufacturing

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties. The effectiveness of the proposed strategy is validated through the case study on a steel powder manufacturing system. The results indicate the exceptional performance of the proposed strategy as compared to other real-time SC strategies, leading to a reduction of electricity cost up to 35.6% without sacrificing the production throughput.     

基于RMC的可重构制造系统设备布局优化研究*

为实现多品种变批量生产制造系统阵列式布局的动态重构,提出一种新的设备布局优化方法.建立了可重构制造系统(RMS)设备优化选择数学模型,设计了基于蚁群优化和阶序聚类算法的可重构制造单元(RMC)动态重构算法.以交货期内最小成本为目标,引入系统复杂度和系统响应度从系统能力角度完善了实现RMS重构的约束条件,最终完成了可重构制造系统设备布局优化.最后通过布局实例仿真验证该方法的可行性和有效性.     

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.     

Design for agility: a scheduling perspective

Agility is the ability of a company to produce a variety of products in a short time and at a low cost. This demands that products and manufacturing systems be simple, robust, and flexible to allow for quick response to the changing market. Scheduling of manufacturing systems in a changing environment is complex. This paper attempts to simplify scheduling of manufacturing systems through appropriate design of products and manufacturing systems. An attempt has been made to generate rules that allow to design products and systems for easy scheduling. Four design for agility rules are proposed in the paper. The first rule deals with decomposition of a manufacturing system. The rule simplifies the scheduling problem and reduces the total changeover cost. The second rule is concerned with design of products with robust scheduling characteristics. Product designs with robust scheduling characteristics can improve the response of a manufacturing system to the changes in the product demand and mix and reconfigurability of the system. The third rule results in a streamlined assembly line which has the type of product flow that simplifies scheduling. The fourth rule emphasizes the reduction of the number of stations in an assembly line. Examples are provided to demonstrate the benefits from using these rules. The implementation of the four rules is also discussed.     

Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine

Multiple parts in gas exchange system of 2-stroke heavy fuel aircraft engine with poppet valves lead to complicated manufacturing and inefficient assembly. Besides, real experimental optimization wastes lots of time and cost due to the increased valve parameters. To address the above issues, the paper proposes a digital twin (DT)-driven optimization method with several DT modules for the system to virtually simulate and optimize the parameters, performance and manufacturing with data interaction and recorded. The DT modules receive real-time feedback data from manufacturing measurements and performance tests to conduct the correction throughout the optimization process. The results demonstrate that the virtual engine model with feedback and correction is quite precise and credible compared with test results, and iterative calculation for optimal parameters is performed efficiently. With the guidance of virtual manufacturing, real manufacturing and assembly are arranged more reasonably and efficiency has been promoted. Real-world test found both power and gas exchange performance improved about 4% under various engine speeds and loads, which verified the effectiveness of DT-driven optimization. The study achieves the integration between virtual and real worlds of the system performance and manufacturing, which facilitates the development of aircraft engine smart manufacturing.     

区域网络化制造系统中的资源及其建模的研究

分析了区域网络化制造系统中资源的特点,根据成都、德阳、绵阳区域网络化制造系统的特点及其开发实践,提出一个三层的资源模型框架,并进一步提出一个基于BTCQS的资源描述模型,为资源的集成与管理及优化配置打下基础。以装备资源为例,采用面向对象的方法,建立了基于BTCQS的装备资源模型,并指出其优点。     

Pheromone-based coordination for manufacturing system control

A pheromone-based coordination approach, which comes from the collective behavior of ant colonies for food foraging, is applied to control manufacturing system in this paper, aiming at handling dynamic changes and disturbances. The pheromone quantum of manufacturing cell is calculated inversely proportional to the cost, which can guarantee a minimal cost to process the orders. This approach has the capacity for optimization model to automatically find efficient routing paths for processing orders and to reduce communication overhead which exists in contract net protocol in shop floor control system. A prototype system is developed, and experiments confirm that pheromone-based coordination approach has excellent control performance and adaptability to disturbances in shop floor.     

An object-oriented framework for developing distributed manufacturing architectures

Management of complexity, changes and disturbances is one of the key issues of production today. Distributed, agent-based structures represent viable alternatives to hierarchical systems provided with reactive/proactive capabilities. In the paper, approaches to distributed manufacturing architectures are surveyed, and their fundamental features are highlighted, together with the main questions to be answered while designing new structures. Moreover, an object-oriented simulation framework for development and evaluation of multi-agent manufacturing architectures is introduced.     

云制造环境下大规模产品定制技术研究

结合云制造及大规模产品定制的特点,提出了一种云制造环境下大规模产品定 制的组织模式以及相配套的需求转换模型。需求转换模型涵盖了完整的需求分解、重组和再 分配过程,对其中的基于产品结构树的需求分解、基于相似性聚类的制造批次重组都进行了 详细阐述,并构建了需求再分配的非线性规划模型,从而可以利用最优化方法实现制造需求 寻优分配。最后,以轮式装载机产品定制为示例,论述了需求转换的详细过程,验证了方法 的有效性和可行性。     

Supervisory control of automated manufacturing cells with resource failures

An important consideration in the real-time supervisory control of flexible manufacturing systems is the resource failures. These disturbances may cause pre-determined part processing sequences impossible to execute. In this paper the issue of resource failures in automated flexible manufacturing cells is addressed. Failures both during processing and in the idle mode are considered, and systematic procedures are described to re-configure the supervisor to continue operation of the system. The described supervisory controller based on finite automata formalism accommodates resource failures, while taking into consideration deadlocks and dynamic routing flexibility options when generating a set of allowable events for the real-time control of automated flexible manufacturing cells.     

柔性制造系统的托盘优化

本文讨论Job-shop型柔性制造系统的托盘优化问题,给出了求最小托盘数的算法,把托 盘优化问题化成了求解线性整数规划问题.     

机床产品制造系统能效的最优控制

本文结合机床产品制造系统的能量流特性,研究机床产品制造系统能效的最优控制.首先,利用无线传感器网络,构建制造系统能效感知网络,并设计了网络能量高效的通信协议,实时获取制造系统的能效数据.进而,利用能效感知数据,分别从单机设备局部优化与综合资源全局优化两方面,设计能效优化控制算法.根据单机设备任意两工步间空载能耗特性,给出单机设备空载能效最优控制模型.同时,建立以缩短生产周期、减少机器空转时间、提高产品合格率为优化目标的综合生产资源能效多目标优化方案.考虑到所论综合资源能效优化问题是离散组合优化问题,本文提出了文化基因支配排序粒子群算法进行求解,并采用层次分析(analysis hierarchy process,AHP)决策方法从Pareto解集中选取最优综合能效的优化策略.最后,利用实例与仿真相结合的方法,验证了文中所提方法的有效性.     

Batch production control in a computer integrated manufacturing environment

Since unit level control systems have already been widely implemented in the chemical industry, it has become abundantly clear that the next levels of application lie in supervisory control and scheduling. This is particularly true for the chemical processing industry, operating batchwise with multi-product and multi-purpose characteristics. A ‘flexible schedule’ (FLESCHE) concept providing the link between operations scheduling and the recipe-driven batch operatioon with its basic instrumentation and control system is being developed and implemented in a computer integrated manufacturing environment. The FLESCHE system incorporates an integrated decision-making framework which includes all the relevant decision functions in a multi-level hierarchy: optimal plant-wide resources coordination, demand management, plant-wide operations scheduling and plant retrofitting on the one hand, as well as master recipe adaptation, batch rerouting and schedule adaptation in response to short-time, unplanned operational disturbances, on the other. A summary of the main characteristics, methodology and present applications of the system is provided. Actual limitations and future extensions are also discussed.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Optimization of machining processes from the perspective of energy consumption: A case study

One of the primary objectives of sustainable manufacturing is to minimize energy consumption in its manufacturing processes. A strategy of energy saving is to adapt new materials or new processes; but its implementation requires radical changes of the manufacturing system and usually a heavy initial investment. The other strategy is to optimize existing manufacturing processes from the perspective of energy saving. However, an explicit relational model between machining parameters and energy cost is required; while most of the works in this field treat the manufacturing processes as black or gray boxes. In this paper, analytical energy modeling for the explicit relations of machining parameters and energy consumption is investigated, and the modeling method is based on the kinematic and dynamic behaviors of chosen machine tools. The developed model is applied to optimize the machine setup for energy saving. A new parallel kinematic machine Exechon is used to demonstrate the procedure of energy modeling. The simulation results indicate that the optimization can result in 67% energy saving for the specific drilling operation of the given machine tool. This approach can be extended and applied to other machines to establish their energy models for sustainable manufacturing.     

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.