Establishing information requirements for supervisory controllers in a flexible manufacturing system using GTA

In this article we consider the technological change that has occurred in complex manufacturing systems within the past two decades and the implications it has had on the role of human operators in manufacturing systems control. Our examination ranges from the traditional production line manned by skilled machinists to flexible manufacturing systems (FMS) under supervisory control. On the basis of this study, we raise the question as to whether new advanced manufacturing technology interfaces are supportive of human operators in their responsibilities to manufacturing systems. We address this problem by analyzing supervisory controller information requirements for intervening in complex process control tasks as part of FMS operation. This analysis was conducted using a cognitive engineering research methodology, which has not previously been applied, in the domain of manufacturing. The method of GTA was applied to supervisory control of an FMS and produced detailed information requirements, which facilitated the formulation of general design guidelines for FMS interface design. The guidelines are aimed at supporting human operator process strategy development and decision making. © 2000 John Wiley & Sons, Inc.     

Information theoretic approach to man-machine interface complexityevaluation

We analyze the interactions between human operators and control room equipment and find that several factors affect the information-gathering and decision-making processes of operators: contents of the provided information, the way information is provided, and knowledge of the operators. These factors contribute to the perceived cognitive complexity by human operators in plant operation. Based on the information theory concept, we propose an integrated framework for evaluating this complexity. The proposed framework is designed to be applied to various types of control room equipment which have different types of man-machine interface and contain different types of information. An experimental verification for the proposed framework is performed and its result shows that the framework successfully integrates various aspects of man-machine interface systems and estimates the mental workload of human operators     

航空制造过程管理电子标签技术应用研究

针对航空制造企业属于典型的离散型复杂制造、生产过程复杂、质量控制要求高等特点,分析了企业资源计划管理与车间现场控制之间信息和管理的分离而导致的生产管理问题,在企业内部生产过程中通过引入电子标签技术跟踪在制品,并兼容条码标签,实现了生产物料信息的实时采集;设计了软件平台实现了制造过程管理与企业资源计划管理的数据和业务集成。某航空制造企业应用的结果表明,生产过程控制能力、生产效率和生产质量得到明显改善。     

Local interactions over global broadcasts for improved task allocation in self-organized multi-robot systems

We present a study of self-organized multi-robot task-allocation, examining performance under local and centralized communication strategies. The results extend our current understanding of the effects of communication by providing evidence that local strategies can improve system performance over centralized strategies, in terms of total task throughput as well as reduced communication overheads. The framework employed is the attractive field model, a generic model of self-organized division of labour derived from observations of ant, human and robot social systems. The framework provides sufficient abstraction to accommodate both communication strategies. Each of the studies used 16 e-puck robots in a simplified manufacturing environment where sensing and communication was realized using camera-based overhead tracking and centralized communication. In terms of task throughput, communication overhead and energy efficiency, the experimental results show that systems with restricted access to information perform better than systems with free flow of information. This suggests a potential paradigm shift where, for self-organizing systems, diminishing access to information renders a system more efficient.     

Formal model of human material-handling tasks for control of manufacturing systems

To achieve an effective integration framework for manufacturing systems, a formal model of a system is highly desired. In spite of significant work on automated manufacturing systems, human operators still play a critical role in virtually every system, especially for material-handling processes. To build a model for control and analysis of a system where a human operator is integrated, a formal functional specification of a human material handler (MH) is presented in a hierarchical framework. Two types of human operational errors associated with material-handling tasks are also classified and discussed. A shop floor control example is provided to illustrate the proposed modeling framework.     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

Dynamic production system diagnosis and prognosis using model-based data-driven method

Advanced manufacturing systems are becoming increasingly complex, subjecting to constant changes driven by fluctuating market demands, new technology insertion, as well as random disruption events. While information about production processes has been becoming increasingly transparent, detailed, and real-time, the utilization of this information for real-time manufacturing analysis and decision-making has been lagging behind largely due to the limitation of the traditional methodologies for production system analysis, and a lack of real-time manufacturing processes modeling approach and real-time performance identification method. In this paper, a novel data-driven stochastic manufacturing system model is proposed to describe production dynamics and a systematic method is developed to identify the causes of permanent production loss in both deterministic and stochastic scenarios. The proposed methods integrate available sensor data with the knowledge of production system physical properties. Such methods can be transferred to a computer for system self-diagnosis/prognosis to provide users with deeper understanding of the underlying relationships between system status and performance, and to facilitate real-time production control and decision making. This effort is a step forward to smart manufacturing for system real-time performance identification in achieving improved system responsiveness and efficiency.     

Study on semantic-entity relevance of industrial icons and generation of metaphor design

The intelligent transformation of the manufacturing industry is accompanied by the visualization of industrial information, and the semantic expression of industrial icons plays a crucial role in improving operators' cognitive performance. Taking the production process of a battery production line as the research object, we studied the semantics-entity relevance of industrial icons based on the concepts of “signifier” and “signified” in metaphorical cognition as well as “topic” and “vehicle.” On this basis, we designed the icons of eight process stations (precleaning, diffusion, coating, screen printing, power testing, etc.) of the battery production line using the metaphor design method. The experimental results show that the icons complying with the law of semantic cognition have high search efficiency. It is concluded that the industrial icons should be designed according to the relevance between functional semantics and entity specific to each production process and that applying such carefully designed industrial icons to industrial information visualization will effectively improve the cognitive performance of operators.     

基于新型制造资源组织模型的生产调度系统研究

为了适应航天制造企业对生产调度系统的要求,提出了新型的制造资源组织模型—–基于虚拟制造单元的制造资源组织模型,探讨了此模式的理论思想以及基于此模型构建的生产调度系统.首先构建了基于虚拟制造单元的资源组织模型结构,并给出虚拟制造单元构建的详细过程;然后,建立了基于此制造资源组织模型的生产调度系统模型;最后,通过应用实例表明了该系统能够有效地简化生产调度过程,提高生产效率.     

An integrated modelling framework to support manufacturing system diagnosis for continuous improvement

This paper proposes an integrated modelling framework for the analysis of manufacturing systems that can increase the capacity of modelling tools for rapidly creating a structured database with multiple detail levels and thus obtain key performance indicators (KPIs) that highlight possible areas for improvement. The method combines five important concepts: hierarchical structure, quantitative/qualitative analysis, data modelling, manufacturing database and performance indicators. It enables methods to build a full information model of the manufacturing system, from the shopfloor functional structure to the basic production activities (operations, transport, inspection, etc.). The proposed method is based on a modified IDEF model that stores all kind of quantitative and qualitative information. A computer-based support tool has been developed to connect with the IDEF model, creating automatically a relational database through a set of algorithms. This manufacturing datawarehouse is oriented towards obtaining a rapid global vision of the system through multiple indicators. The developed tool has been provided with different scorecard panels to make use of KPIs to decide the best actions for continuous improvement. To demonstrate and validate both the proposed method and the developed tools, a case study has been carried out for a complex manufacturing system.     

Development of an agent-based system for manufacturing control and coordination with ontology and RFID technology

Integrating physical objects with the corresponding enterprise applications any time any where is the essential issue for a real-time enterprise. This study proposes a multi-agent system framework called agent-based manufacturing control and coordination (AMCC) system, a agent-based framework using ontology and RFID technology to monitor and control dynamic production flows and also to improve the traceability and visibility of mass customization manufacturing processes. The capabilities offered by multi-agent systems to respond to RFID events in real-time and a broad class of agent design and coordination issue regarding just in time (JIT) and just in sequence (JIS) manufacturing processes are also exploited in this study. To validate the proposed framework, case study of a bicycle manufacturing company is used to demonstrate how the proposed framework can benefit its JIT production. Finally, an example prototype system is implemented to demonstrate the concept of the proposed framework.     

An implementation framework for seru production

Seru production, which merges the flexibility of job shops, efficiency of mass production, and environment friendly characteristics of sustainable manufacturing, largely for electronics assembly, is the latest manufacturing mode developed in Japan. It is receiving attention from Japanese practitioners and researchers. However, some attempts to implement seru production are unsuccessful, especially outside Japan. This is because of lack of knowledge of the specific means to implement seru production. The purpose of this paper is to provide a general framework and some basic principles that should be followed while implementing seru production for practitioners from a practical perspective. This work is based on a systematic analysis of many implementation experiences of seru production in Japanese manufacturing factories as well as a broad investigation of the popular literature information. The proposed framework can serve as a concise guide to help implement seru production in the manufacturing industry. In addition, constructive information is provided for researchers who would like to know and study the advanced manufacturing mode of seru production but do not understand the Japanese language.     

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.     

Optimizing the energy efficiency of chiller systems in the semiconductor industry through big data analytics and an empirical study

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed and automaticity in manufacturing processes. The semiconductor industry is a fundamental driver of this transformation; moreover, due to the highly complex and energy-consuming nature of the semiconductor manufacturing process, semiconductor fabrication facilities (fabs) can also benefit greatly from incorporating big data analytics to improve production and energy efficiency. This paper developed a big data analytics framework, along with an empirical study conducted in collaboration with a semiconductor manufacturer in Taiwan, to optimize the energy efficiency of chiller systems in semiconductor fabs. Chiller systems are one of the most energy-consuming systems within a typical modern fab. The developed big data analytics framework allows production managers to ensure that chiller systems operate at an optimized level of energy efficiency under dynamically changing conditions, while fulfilling the chilling demands. Compared to the commonly-used heuristics previously employed at the fab to tune chiller system parameters, by the utilization of big data analytics, it is shown that fabs can achieve substantial energy savings, greater than 12%. The developed framework and the lessons learned from the empirical study are not only generalizable but also useful for practitioners who are interested in applying big data analytics to optimize the performance of other equipment systems in fabs.     

Software framework concept with visual programming and digital twin for intuitive process creation with multiple robotic systems

With the progressive digitalization in industrial manufacturing, the usage of complex robotic systems in both intralogistics and production is expected to increase. This proposes a challenge for planners and shop floor workers, as programming and interacting with these various systems leads to a high cognitive load. Especially the broad range of different manufacturer specific software leads to a number of problems, e.g. the program-synchronization between different systems and the often necessary workshops for workers. These problems can lead to inefficient programming and planning operations, bad worker satisfaction and human errors. In this paper, we present a modular, system agnostic and human centered software framework that unifies the programming of different systems, to enable centralized and intuitive system programming for non-expert operators. Our software framework utilizes visual programming concepts together with an integrated digital twin of the factory and a novel graph-based programming interface. We explain our concept in detail and describe our validation through integration into a realistic industrial setup with three different systems. In addition, we provide an evaluation of our concept's usability with an experimental user study and discuss the results of the study and the software implementation. Our study results show that even non-technical users are able to use our software after a brief introduction to create complex processes that involve multiple machines working in parallel. All users reported high usability and expert users reported that the visual process editor has enough features to create processes for industrial applications. Finally, we conclude this paper by providing an outlook on future work and use-cases of our software.     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

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。     

用户认知驱动的VR自然交互认知负荷研究

针对目前虚拟现实自然交互系统中用户认知负荷难以量化问题,以量化认知负荷为目的,提出一种基于概率神经网络的多通道信息融合模型。首先根据认知理论建立“认知—行为—环境”框架,并在此基础上研究用户认知的数学表征（方法,过程,机理）,构建多通道认知模型;然后将多通道信息融合方法引入虚拟现实自然交互系统,建立一种面向虚拟现实自然交互系统的认知负荷模型;通过VR隧道应急救援系统实验对系统中用户认知进行验证。验证结果表明,具体提出的认知负荷模型具有一定的可行性。     

I spy with my AI: The effects of AI-based visual cueing on human operators’ performance and cognitive load in CCTV control rooms

The increased number of security cameras in modern cities has elevated the video-feed monitoring demands of closed-circuit television (CCTV) operators. As a result, new AI-driven support systems that leverage the power of computer vision algorithms have been deployed to facilitate the operators' work. However, to effectively design intuitive, AI-driven interfaces and validate their impact on the operators' performance, extensive user testing is required. To address this, we previously developed and tested a virtual reality (VR) control room that can be used to iteratively evaluate intelligent computer assistants and interfaces while operators are subjected to different cognitive load. In the present study, we use this VR environment and physiological markers (e.g., eye tracking measures) to investigate how AI-based visual cueing (i.e., pushing forward video streams on which detections are highlighted by rectangles drawn around targets) affects operator performance and cognitive load. Results suggest that support systems using such technology in a control room improve operators’ performance and decrease their cognitive load, as reflected by changes in pupil dilation and subjective reports irrespective of induced cognitive load.     

A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.