Guidelines for implementing robust supervisors in flexible manufacturing systems

Over the past several years, researchers have developed numerous control policies that assure deadlock-free operation for flexible manufacturing systems. Using this research base as a foundation, we have developed several supervisory policies that assure robust operation in the face of resource failure. Along with deadlock-free operation, these policies guarantee that failure of unreliable resources does not block production of part types not requiring failed resources. In our previous work, we developed two types of robust policies, those that ‘absorb’ all parts requiring failed resources into the buffer space of failure-dependent resources (resources that support only parts requiring failed resources), and those that ‘distribute’ parts requiring failed resources among the buffer space of shared resources (resources shared by parts requiring and parts not requiring failed resources). These two types of robust controllers assure different levels of robust system operation and impose very different operating dynamics on the system, thus affecting system performance in different ways. In this research, we use extensive simulation and experimentation on a highly complex and configurable system to develop guidelines for choosing the best robust supervisor based on manufacturing system characteristics and performance objectives. We validate these guidelines using seven randomly generated complex systems and find a better than 88% agreement.     

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.     

Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine–part–worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.     

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 2: number of Kanban and workstation starvation

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. In the first part of this two-part paper, a basic, single-tugger MR system is described, and stability conditions as well as the probability of exceeding either the physical capacity of the tugger or the prescribed cycle time are derived. Given the stability conditions, and the distribution of the number of containers requested per MR, in the second part of the paper, the number of Kanban required in the MR system is examined, and analytical approximations are derived both for the number of Kanban required and for predicting workstation starvation. The latter is a key concern when designing a MR system that will support workstations in a manufacturing plant. The performance of the analytic approximation is evaluated by simulating various MR systems. Our results suggest that, in a stable MR system, the number of Kanban and the physical capacity of the tugger have a bigger impact on workstation starvation than the prescribed cycle time.     

A method for comparing flexibility performance for the lifecycle of manufacturing systems under capacity planning constraints

The objective of this work is to describe a method for comparing the flexibility performance of manufacturing systems, in an uncertain environment, under lifecycle considerations and capacity planning constraints. The manufacturing systems costs are estimated over a time horizon and for a large variety of possible market scenarios. In order for the lifecycle cost values to be comparable among different systems, their values are calculated with the use of a special purpose algorithm. Statistical analysis of the estimated cost values is then employed for assessing the flexibility of each manufacturing system. The method is applied in an industrial case for checking, also from a flexibility point of view, the investment on a production system, using real life industrial data.     

论制造系统模式的新进展

分析了制造系统模式变迁的原因；论述了现代制造系统模式的新特征，即客户化动态系统、可变性、自组织、自治与协调、全生命周期高的成本效率以及集成进化；提出了支持现代制造系统模式的若干关键技术，包括时变制造过程建模、自适应生产管理以及现代集成技术等；介绍了可重构制造系统、多智能代理制造系统、子整制造系统以及虚拟生产系统等现代制造系统；并从工业工程的角度，讨论了发展中国现代制造系统模式的若干问题，即改变金字塔式的集中管理组织结构，合理地发挥人的作用以及重视信息技术的应用。     

Introducing new parts into existing cellular manufacturing systems based on a novel similarity coefficient

Over the last three decades, designing cellular manufacturing systems (CMS) still centres on assigning machines to machine cells and parts to part families. This task ends after assigning these part families to the appropriate machine cells. In the past, testing CMS was evaluated according to the efficiency of clustering, but actual testing of CMS after installation is still unexplored. Introducing one or more new parts (products) into CMS without any changes in the installation of the cells during processing of the current parts is a new concept to be considered and evaluated. Transferring these systems from traditional ideologues to advanced ideologues (agile systems) is highly desired. This concept can be considered as part (product) flexibility in CMS. To address this concept, a new similarity coefficient between the new part and the existing manufacturing cell will be created. New productivity and flexibility measurements in CMS will also be suggested. A new strategy for accepting a new part into CMS will be proposed based on machine utilization and flexibility in the cells, cell utilization and flexibility in the system, product flexibility (system flexibility), and similarity of this part with existing manufacturing cells. A complete analytical example will be presented.     

Scalable reconfigurable equipment design principles

Scalability is one of six key characteristics found in reconfigurable manufacturing systems. Scalable systems satisfy changing capacity requirements efficiently through system reconfiguration, and in the flexible manufacturing literature this capability is called expansion flexibility. The development of modular scalable machine tools is a necessary precursor to achieving scalable systems. Unfortunately, there is little work describing the design of scalable machines. This paper establishes the need for scalable machines and a basis for evaluating and describing them. Applicable metrics are defined, and an architecture for scalable machines is presented. Two examples illustrate the scalable architecture. Finally, a design parameter based on a mathematical approach is presented to determine the optimal number of modules to be included on a modular scalable machine. This as a design parameter is important because it limits machine size and the number of module interfaces included in the base machine structure.     

A computational control implementation environment for automated manufacturing systems

This paper presents a computational environment as a tool for supporting the implementation of control coding of an automated manufacturing system. The proposed environment considers a cyclic three-stage control development – modelling, synthesis and implementation – until the real system accomplishes the required specification, resulting in the automated and integrated manufacturing system. The research details the three stages and describes the steps executed for each one. The mathematical formalism used in this work is also presented, as a basis for control implementation. The implementation environment is proposed in order to validate the control structure of this formalism and to allow a progressive integration of control hardware and software. To submit to a test and validate the proposal environment, two experiments are performed, in two different manufacturing systems. Thus, it is demanded that the control system can be reconfigurable in a fast and reliable way.     

The Future of Manufacturing: A New Perspective

Many articles have been published on intelligent manufacturing, most of which focus on hardware, software, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a different perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalability. The first major challenge is to (re-)define what the manufacturing of the future will be, if we wish to: ① raise public awareness of new manufacturing’s economic and societal impacts, and ② garner the unequivocal support of policy-makers. The second major challenge is to recognize that manufacturing in the future will consist of systems of hybrid systems of human and robotic operators; additive and subtractive processes; metal and composite materials; and cyber and physical systems. Therefore, studying the interfaces between constituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can be evaluated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of “scalability” of non-information technologies. The last, but not least, challenge is to examine successful modalities of industry–academia–government collaborations through public–private partnerships. This article discusses these challenges in detail.     

Cyber–Physical Production Systems for Data-Driven,Decentralized, and Secure Manufacturing—A Perspective

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.     

面向船体曲面分段建造的虚拟流水线生产调度

基于船舶船体曲面分段建造的特点,利用成组技术和流水线生产的原理,提出了面向曲面分段建造的虚拟流水线生产模式,研究了包括生产组织改造和生产调度的关键技术。根据虚拟流水线生产模式的原理,建立作业班组分解模型,并应用遗传算法实现基于虚拟流水线的曲面分段制造生产调度方法。利用上海某造船厂实际数据进行验证,结果表明虚拟流水线生产模式及方法可有效提高船舶生产效率。     

Mission reliability evaluation based on operational quality data for multistate manufacturing systems

Modern and intelligent manufacturing systems have a prominent multistate feature. However, previous studies of reliability analysis of multistate manufacturing systems mostly focused on the basic reliability of manufacturing systems but disregarded their operating characteristics, which has hindered the development of Prognostics and Health Management technique for intelligent manufacturing systems. Therefore, an evaluation approach of mission reliability for multistate manufacturing systems based on operational quality data is proposed in this paper. First, from the systematic viewpoint of the composition and operational principle of the manufacturing system, the relationship among production task execution state, production equipment degradation state, and produced product quality state is expounded, and the connotation of the mission reliability of multistate manufacturing systems is defined. Second, an extended state task network (ESTN) is presented to organise operational quality data by considering the quality state of work in process (WIP). Third, a fusion model of operational quality data for manufacturing systems is established with the aid of the ESTN, and an operational quality data-oriented evaluation method of mission reliability is been put forward. Finally, a case study of a manufacturing system for a cylinder head is conducted to verify the proposed approach.     

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 1: system stability and distribution of container starts

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. MR systems generally result in short lead times, low variability, and low line-side inventory levels. In the first part of this two-part paper, a basic, single-tugger MR system is described. Stability conditions are derived with respect to the ‘physical capacity’ of the tugger, and the ‘time utilisation’ of the driver/material handler, both of which are key criteria since the number of containers that can be delivered during each MR is limited, and each MR targets a prescribed cycle time to ensure timely and consistent deliveries. We also derive the distribution of the number of containers requested per MR, which allows the model to estimate the probability of exceeding the physical capacity of the tugger or the prescribed cycle time, under the assumption that sufficient kanban are available for each part type delivered by the MR system. The results of the study facilitate the design and analysis of MR systems used in industry.     

A genetic scheduling methodology for virtual cellular manufacturing systems: an industrial application

Effective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially.

The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs.     

Complex metallic alloys as new materials for additive manufacturing

Additive manufacturing processes allow freeform fabrication of the physical representation of a three-dimensional computer-aided design (CAD) data model. This area has been expanding rapidly over the last 20 years. It includes several techniques such as selective laser sintering and stereolithography. The range of materials used today is quite restricted while there is a real demand for manufacturing lighter functional parts or parts with improved functional properties. In this article, we summarize recent work performed in this field, introducing new composite materials containing complex metallic alloys. These are mainly Al-based quasicrystalline alloys whose properties differ from those of conventional alloys. The use of these materials allows us to produce light-weight parts consisting of either metal–matrix composites or of polymer–matrix composites with improved properties. Functional parts using these alloys are now commercialized.     

Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing

An intelligent manufacturing system is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. This kind of intelligent system is called a human–cyber–physical system (HCPS). In terms of technology, HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing. It can be concluded that the essence of intelligent manufacturing is to design, construct, and apply HCPSs in various cases and at different levels. With advances in information technology, intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing, and is evolving toward new-generation intelligent manufacturing (NGIM). NGIM is characterized by the in-depth integration of new-generation artificial intelligence (AI) technology (i.e., enabling technology) with advanced manufacturing technology (i.e., root technology); it is the core driving force of the new industrial revolution. In this study, the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs, and the implications, characteristics, technical frame, and key technologies of HCPSs for NGIM are then discussed in depth. Finally, an outlook of the major challenges of HCPSs for NGIM is proposed.     

Value creation through design for scalability of reconfigurable manufacturing systems

Rapid and cost-effective scalability of the throughput of manufacturing systems is an invaluable feature for the management of manufacturing enterprises. System design for scalability allows the enterprise to build a manufacturing system to supply the current demand, and upgrade its throughput in the future, in a cost-effective manner, to meet possible higher market demand in a timely manner. To possess this capability, the manufacturing system must be designed at the outset for future expansions in its throughput to enable growths in supply exactly when needed by the market. A mathematical method that maximises the system throughput after reconfiguration is proposed, and an industrial case is presented to validate the method. The paper offers a set of principles for system design for scalability to guide designers of modern manufacturing systems.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts

Manufacturing of aircraft structural parts has the characteristics of multiple varieties, complex structures and small batches, which make the manufacturing resource allocation highly difficult. This paper proposes a manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation, considering multiple manufacturing resources including process planners, machine tools and cutting tools, as well as manufacturing process schemes of aircraft structural parts. Knowledge in terms of experts’ experience and historical data is used for fuzzy comprehensive evaluation. A manufacturing resource allocation model is proposed based on the analysis of manufacturing processes of aircraft structural parts. The capability of planners, the complexity of structural parts, the reliability of machine tools, the reliability of cutting tools and the correlations between manufacturing resources and structural parts are evaluated using the fuzzy comprehensive evaluation method. Multiple manufacturing resources are allocated based on the fuzzy comprehensive evaluation results. A prototype system has been implemented and a case study is used to validate the proposed approach.