Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

Decision-based process planning for wire and arc additively manufactured and machined parts

The conventional manufacturing of aircraft components is based on the machining from bulk material and the buy-to-fly ratio is high. This, in combination with the often low machinability of the materials in use, leads to high manufacturing costs. To reduce the production costs for these components, a process chain was developed, which consists of an additive manufacturing process and a machining process. To fully utilize the process chain’s capabilities, an integrated process planning approach is necessary. As a result, the work sequence can be optimized to achieve the economically most suitable sequence. In this paper, a method for a joint manufacturing cost calculation and subsequent decision-based cost minimization is proposed for the wire and arc additive manufacturing (WAAM) & milling process chain. Furthermore, the parameters’ influence on the results and the magnitude of their influence are determined. These results make it possible to design an economically optimal work sequence and to automate the process planning for this process chain.     

Scalable framework for blockchain-based shared manufacturing

Shared Manufacturing is a new mode of social manufacturing based on the principles of a sharing economy. This paper presents a scalable framework for blockchain-based Shared Manufacturing that preserves the transparency and immutability characteristics of transaction records, which is critical to building trust between entities in blockchain-based systems. We define a blockchain-based protocol for the service execution according to the design principles of the sharing economy. We present a scalable integration of blockchain technology into the concept of Shared Manufacturing by employing cross-chain solutions. We discuss existing cross chain technologies regarding the requirements of Shared Manufacturing and propose hybrid approach. We compare implementations of the proposed framework on two different blockchain networks: Ethereum public network and Xdai sidechain network. We conduct user-oriented test to explore the performance (cost and time) of the implementations in realistic situations in order to justify the use of the sidechain technology. Results indicate that the implementation on the sidechains provides greater scalability than the implementation on the public blockchain network.     

A Polynomial Algorithm for Computing Elementary Siphons in a Class of Petri Nets

A set of elementary siphons plays a key role in the development of deadlock prevention policies for automated manufacturing systems. This paper addresses the computation problem for elementary siphons in a subclass of Petri nets which are basic systems of simple sequential processes with resources (BS³PR) and can model many automated manufacturing systems. An algorithm for enumerating elementary siphons is established by the one‐to‐one relationship between maximal perfect resource‐transition circuits (MPCs) and strict minimal siphons. A set of MPCs is first computed, followed by a set of elementary siphons in a BS³PR. The presented algorithm is proved to have polynomial‐time complexity. An example is used to illustrate the algorithm.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

火车车轮成形新工艺的开发研究

在比较分析技术先进国家的火车车轮成形工艺流程后，采用试验研究和数值模拟相结合方法，研究开发出优化的新工艺。钢坯先在一台水压机上预成形制坯，再在另一台压机上模锻，经轧制扩径后冲孔压弯。这样的生产线效益最好     

电机智能制造发展现状及推进策略

随着电机制造要求逐步提高、自动化逐渐普及,智能制造将成为未来主流的生产制造模式。基于对传统电机制造现状和发展趋势的分析,厘清智能制造优势及智能制造推进误区,概括智能制造推进内容及策略。以江苏大中电机股份有限公司相关项目为例,介绍智能制造的侧重内容、实施步骤、原则及实施成果。结果表明：总体生产效率提高了35.75%,生产运营成本降低了26.21%,产品不良率降低了75.84%。在此基础上概括电机行业智能制造推进参考战略,为中小型电机行业发展提供支持。     

模具敏捷制造系统研究

针对模具设计制造过程复杂、条件苛刻和单件或小批量生产的特点 ,论述了模具产业发展敏捷制造技术的必要性。提出了模具敏捷制造系统框架和基于STEP与特征技术实现信息集成与过程集成的运行机制。阐述了基于Internet/Intranet的开放式可视化协同工作平台的模具敏捷制造实施方案。     

基于多代理的制造执行系统的研究

制造执行系统作为连接计划管理系统（ERP/MRPⅡ）和生产控制系统的桥梁与纽带，对车间层生产调度的优化，生产过程的改进等方面起了重要的作用，文章对制造执行系统的制造模式进行研究的基础上，提出了基于多Agent的制造执行系统的应用框架结构，并对基于多Agent的制造执行系统的调度系统进行了研究，最后给出了制造执行系统的调度算法结构。