Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.     

改进的人工免疫算法在图像配准参数优化中的应用

借鉴禁忌搜索的思想改进了人工免疫网络算法(aiNet),提出一种禁忌人工免疫网络算法(TS-aiNet).在算法中引入禁忌表,禁忌在网络迭代中亲和力不再增加的细胞,通过特赦准则赦免一些被禁忌的优良状态;增加记忆表,保存成熟的记忆细胞;重新定义高斯变异方式,保证多样化的搜索.利用Markov链分析了该算法的全局收敛性,通过对典型系统的仿真实验分析了该算法的性能,并与克隆选择算法和opt-aiNet算法进行了比较,最终将改进的算法运用到红外与可见光图像配准中,像素级配准精度可以达到0.5像素.实验结果表明,该算法在多模态搜索空间中具有更好的全局收敛性、稳定性和发现极值点能力,能够克服早熟现象,提高图像配准的速度和精度,是一种有效的全局优化方法.     

面向并行工程的CAD系统研究

本文在ＣＩＭＳ环境下，以非回转体零件特征造型的基础上，其非回转体零件－－箱体对对象，应用并行工程技术，开发基于特征的三产体制造型系统。在生成工程图纸的同时更有效地与后续工序ＣＡＰＰ、ＣＡＭ进行信息集成。提供了一个更高层次的二次开发系统。     

A green productivity based process planning system for a machining process

In the metal cutting industry, manufacturers have strived to increase energy efficiency and to reduce environmental burdens through the use of dust collectors and waste disposers. It is more beneficial and efficient to apply the front-of-pipe technology that prevents the sources of pollutants and minimises energy use through the redesign of products and the change of process planning and machining operations. In particular, process planning for the environment, called eco-process planning, is central to increasing energy efficiency and reducing environmental burdens because process planning decisions greatly influence machining performance. At present, greenability, a term used to indicate environmental friendliness, has been little considered as a major concern in the process planning stage because process planning decisions have focused on improving productivity aspects that include speed, cost and quality. Thus, it is essential to develop an eco-process planning approach that enables the harmonisation and enhancement of greenability performance while improving productivity performance, termed green productivity (GP). This paper presents the development of a GP-based process planning algorithm that enables the derivation of process parameters for improving GP in machining operations. The core mechanism of the algorithm is the realisation of the process improvement cycle that measures GP performance by the collection of machining data, quantifies this performance by categorical representation and predicts the performance through prediction models. To show the feasibility and applicability of the proposed algorithm, we have conducted an experiment and implemented a prototype system for a turning machining process.     

More MILP models for integrated process planning and scheduling

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.     

被动免疫算法在模拟电路故障诊断中的应用研究

基于生物免疫系统的信息处理机理,介绍了被动免疫算法的实现过程,并将其应用到某装备电路板的故障诊断之中。该算法具有边检测边学习的动态调整功能,仿真和实验实例表明,该算法适合于电路板的故障诊断,有较高的故障诊断率。     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

Enterprise resource planning (ERP) operations support system for maintaining process integration

A successfully adopted and implemented enterprise resource planning (ERP) system does not automatically guarantee full benefits. It is important that ERP is operated as planned and thus provides the real-time information with a desired level of process integration. Any ERP system pushes a company toward full process integration and solves the fragmentation of information. However, the tight process integration involves operational issues that must be carefully managed. Thus, a conjecture can be made that a centrally coordinated support system is required to assist ERP users and administrators find problems, perform tedious validation and verification, and maintain the process integration of ERP with great consistency. This paper proposes an ERP operations support system (EOSS) that aims to achieve and maintain the process integration of ERP. EOSS monitors the operational status of process integration, prevents anomalies as early as possible and conducts repairs as soon as possible.     

Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach

In this paper a mathematical model for the batch sequencing problem in a multistage supply chain is developed by taking into account three practically important objectives, viz. minimization of lead time, blocking time and due date violation. Attribute dependent operation time, sequence dependent setup time, different due dates, different lot sizes for batches and variable time losses due to interaction among several stages like waiting, idling, and blocking are also considered in the model. The problem is combinatorial in nature and complete enumeration of all its possibilities is computationally prohibitive. Therefore, a metaheuristic, artificial immune system (AIS) is employed to find an optimal/near optimal solution. In order to test the efficacy of AIS in solving the problem, its implementation on four different problems has been studied. Further, the comparative analysis of the results obtained by implementing AIS, genetic algorithm (GA) and simulated annealing (SA) on the proposed model reveals that AIS outperforms GA and SA in solving the underlying problem.     

Allocation of manufacturers through internet-based collaboration for distributed process planning

The pursuit of lower cost, shorter time-to-market, and better quality has led to a shift toward global production in today's competitive business environment. This shift however, forces manufacturing enterprises to have separate design houses and manufacturing facilities. In general, design houses are located in the same regions as customers to enable them to respond to the rapidly changing demands of customers. By contrast, manufacturing facilities can be placed in regions in which production costs are lower. However, this physical and logical separation between designers and manufacturers (or between upstream manufacturers and downstream manufacturers) raises various integration issues. The present paper addresses two of these issues: the framework for representing the data necessary to communicate requirements and objectives of the designer, and the methodology for utilizing such data to optimize the business objectives related to production cost and quality. The proposed representation, collaboration framework, and methodology will enable design houses and manufacturing facilities to realize the benefits of global production and to accommodate the management of loosely integrated supply chains.     

Manufacturing strategies for the ecosystem-based manufacturing system in the context of 3D printing

This paper aims to investigate the manufacturing strategies for the manufacturing systems in the context of 3D printing, referring to ecosystem-based manufacturing systems, rather than firm-based and network-based ones. A case study approach was adopted for this research, as the data was mainly collected via semi-structured interviews with staff members of companies in China. Besides the elements of strategic choices and manufacturing capabilities identified in the extant literature, this research verified three additional strategic choice elements (functional role, platform and solution) and identified two factors (platform openness and solution diversity) to classify an ecosystem-based manufacturing system. Meanwhile, four manufacturing capabilities of the ecosystem-based manufacturing system have been identified: collaborative manufacturing flexibility, rapid thriftiness ability, self-customisation and co-evolved design capability. The research results contribute to the area of manufacturing strategy via expanding its view from the firm and network levels to the ecosystem level. Meanwhile, the research results present operations managers with an understanding of the strategic choices and manufacturing capabilities of an ecosystem-based manufacturing system in the context of 3D printing.     

Dynamic restructuring process for self-reconfiguration in the fractal manufacturing system

To meet the rapidly changing customer needs in the manufacturing environment, future manufacturing systems must be dynamically and flexibly reconfigured. The fractal manufacturing system (FrMS) is one of the new manufacturing paradigms that can meet such requirements. The basic component of the FrMS is referred to as a fractal. Consisting of self-similar agents, each fractal autonomously cooperates and negotiates with others to coordinate its tasks. Dynamic restructuring process (DRP) supports reorganization of the system configurations so that the FrMS can be adapted to dynamically changing environments. Although traditional approaches have endeavoured to demonstrate reconfigurability of a manufacturing system, they are not accurate enough to meet the requirements of circumstances such as high-level autonomy in reconfiguring the system architecture. In this paper, therefore, the DRP, which embodies self-reconfigurability of a system, is proposed focusing on the FrMS. To check the effectiveness of the DRP, we have developed the FrMS test bed and conducted the experimentation on the DRP. A simulation study has been conducted to show the effectiveness of the DRP under an illustrative situation.     

Artificial immune system for optimal design of composite hydrogen storage vessel

Based on the clonal selection principle in the biological immunology, an artificial immune system (AIS) is proposed to optimize the weight of Al-carbon fiber/epoxy composite hydrogen storage vessels under burst pressure constraint. The AIS simulates the generation of the antigens, the combination of the antigens and antibodies, and the selection, cloning and hypermutation of B cells, the maintenance of diverse antibody cells, the generation of the memory cells with high affinities and the death of the antibody cells with low affinities. Effects of the antibody size and the iterative number on the optimization results are explored. By comparison, the AIS shows higher search velocity and precision than the genetic algorithm and simulated annealing.     

Goal-balancing process for goal formation in the fractal manufacturing system

The coordination architecture and mechanisms are proposed for the goal-balancing process (GBP), which is one of the components of goal-formation process (GFP) in the fractal manufacturing system (FrMS). Since each agent in the FrMS generates, achieves and modifies its own goal autonomously during the coordination process with other agents, it is necessary to develop a systematic methodology for the goal-formations in a manufacturing system: GFP takes charge of this methodology, which enables the formation of and the changes in goal through coordination between agents in real-time in the distributed and dynamic systems. The GFP is composed of three sub-processes: the goal-generating process (GGP), the goal-harmonizing process (GHP), and the GBP. The GGP makes and propagates goals for all fractals. The GHP then eliminates or reduces possible conflicts and interferences between goals generated in GGP. The GBP is the post-process of GHP; it refines the fractal's goal, which has been ridded of conflict during the GHP to enhance the global performance of the system, rather than maximizing the performance of each fractal. For the development of the GBP we proposed a mechanism for unit goal-balancing process (UGBP), which is the basic process of GBP that adopts the, fuzzy decision-making, and distributed problem solving approaches.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

A prototype knowledge-based system (KBS) for selection of inventory control policies

In a previous paper Parlar presents an expert system for advising on the selection of production/inventory models. A deficiency of this system is that the user must review the reference literature to locate the corresponding inventory control policy. This paper presents a prototype expert system where the knowledge base contains the control policy equations for reorder level (L), reorder quantity (Q) and total system cost (TC). Moreover, after identifying the inventory control policy equations, the expert system then shells to an external computer program to numerically solve the model that the rules select. It is believed that the output of more precise information to the user will lead to more accurate usage of inventory models.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

A CAD/CAM system for process planning and optimization in LOM (Laminated Object Manufacturing)

Rapid Prototyping (RP) technologies have emerged as a powerful set of manufacturing technologies in recent years. While these technologies invariably provide tremendous time savings over traditional methods for the manufacture of design prototypes, most are still quite inefficient. This paper proposes two ideas: (i), that these processes can be significantly optimized by using better process planning; and (ii), that several of these technologies use similar core planning technologies for optimization. The first hypothesis is verified in this paper by presenting an improved process planning system for one RP technology, Laminated Object Manufacturing (LOM). The second observation led us to develop an open architecture planning system for a host of RP technologies. A testbed software system using these ideas has been developed and is presented in this paper. While the methodologies developed can work with the current industry standard STL format for storing object CAD data, the software is planned purely to work using exact solid models and direct slicing methods.     

A knowledge-based system for materials selection in mechanical engineering design

This paper studies various work on the development of computerized material selection system. The importance of knowledge-based system (KBS) in the context of concurrent engineering is explained. The study of KBS in material selection in an engineering design process is described. The development in materials databases, which sometimes serve as material selection packages, is also discussed. The use of KBS in material selection and the application in the domain of polymeric-based composite are chosen as typical examples.     

快速重组制造系统的构建原理及其应用

快速重组制造系统是一种适应21世纪全球化市场竞争的新型制造系统。提出了基于组态式柔性制造单元组成的陈列式布局的快速重组制造系统的创新结构体系,建立了以生产效益为该系统方案设计和运行决策目标的随机模型,应用排队网络模型和静态马尔科夫工件运送方式得出系统重组时缓冲区容量的优化配置和最优工件运送策略的算法,研制了作为构建快速重组制造系统核心的新颖的组态式柔性制造单元的原型,并给出生产工程中应用所创建的阵列式可重组制造系统的成功案例。