Manufacturing plant control: Challenges and issues

Proceedings of the 16th IFAC World Congress, 2005, 2005

Enterprise-control system integration between business systems, manufacturing execution systems and shop-floor process-control systems remains a key issue for facilitating the deployment of plant-wide information-control systems for practical e-Businessto-Manufacturing industry-led issues. This achievement of the Integration-in-Manufacturing paradigm based on centralized/distributed hardware/software automation architectures is shifting by the Intelligence-in-Manufacturing paradigm addressed by the IMS industry-led R&D initiative in order to define and to experiment the next generation of manufacturing systems capable to cope with the high degree of complexity of meeting agility over flexibility and reactivity in customized manufacturing. This survey paper of the TC 5.1 summarizes these key problems, trends and accomplishments for manufacturing plant control before to emphasize for practical purposes some rationales and forecasts in deploying automation over networks, HMS and its related agent-based technology, as well as in applying formal methods to ensure dependable manufacturing.

Service Orientation in Holonic and Multi-Agent Manufacturing, 2018

To satisfy the complex needs of a production process control, many architectures have been proposed which regularly can be classified into hierarchical (e.g. ISA 95 standard), heterarchical (e.g. multi-agent systems) and holonic or semiheterarchical, based on the holarchy notion (self-regulating holons organization with "smart" properties). A manufacturing system under the holonic approach (HMS) is considered part of the Intelligent Manufacturing Systems (IMS) implying that the HMS are defined as highly distributed organizations. The goal of these organizations is to decentralize manufacturing into individual entities called holons, which are autonomous, cooperative and intelligent. A. Koestler defines the concept of Holon as a self-similar or fractal, stable and coherent structure consisting of several holon-shaped sub-structures. Thus, no elements in the holarchy can self-develop without their subordinated parts or components. This characteristic ensures that HMS are stable structures that can resist disturbances. Applying this concept results in control processes that are generalized in terms of the next-generation manufacturing systems. The state of the art reveals that traditional manufacturing systems and HMS approaches are characterized by the Production Unit (PU). Examples of HMS proposed during the last decades with their strengths and weaknesses are summarized hereafter. Finally, the goal of this research is to analyze, design, and validate a holonic PU (HPU) model of intelligent control based on HMS. Primary results show an implementation of a decentralized industrial automation. There are given HPU information models in order to establish a control architecture for manufacturing systems to support real-time plant operation.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2002

Existing modeling frameworks for manufacturing system control can be classified into hierarchical, heterarchical, and hybrid control frameworks. The main drawbacks of existing frameworks are discussed in this paper. A new hybrid modeling framework is also described. It is a hybrid of the two: hierarchical and heterarchical frameworks. In this proposed framework, entities (e.g., parts) and resources (e.g., material handling devices, machines, cells, departments) are modeled as holonic structures that use intelligent agents to function in a cooperative manner so as to accomplish individual, as well as cell-wide and system-wide objectives. To overcome the structural rigidity and lack of flexibility, negotiation mechanisms for real-time task allocation are used. Lower-level holons may autonomously make their negotiations within the boundary conditions that the higher-level holons set. Horizontal, as well as vertical decisions, are made between various levels of controllers, and these are explicitly captured in the model.

2006 World Automation Congress, 2006

Agent-based technology provides a natural way to design and implement efficient manufacturing environments. In this paper we emphasize features of state-of the art manufacturing systems. Furthermore we present fundamentals of agent based systems and an agent-based framework for the coordination and integration of manufacturing systems. It models each stage or process as an autonomous agent. Each agent is a software entity that has a set of protocols which govern the operations of the manufacturing entity, a knowledge base, an inference mechanism and an explicit model of the problem to solve. The protocols specify what action an agent will take based on its local state and the messages received from other agents.

Proceedings of the Practical Application of …, 1998

Manufacturing enterprises are now moving towards open architectures for integrating their activities with those of their suppliers, customers and partners within wide supply chain networks. Agent-based technology provides a natural way to design and implement such environments. This paper presents an agent-based manufacturing enterprise infrastructure. After a brief review of recent advancements in this domain, we describe the main features of the proposed infrastructure and the functions of its components. A machine-centered dynamic scheduling/rescheduling mechanism is then detailed and a prototype implementation is presented.

2019

Manufacturing is constantly changing due to new disruptive technologies that enable implementation of new innovative concepts. Current enablers for innovative changes are new Information Technologies (IT) that consist of Internet of Things (IoT), big data, Artificial Intelligence (AI) and are all backed up by the Industry 4.0 concept [1]. This technology connects and improves Cyber-Physical Systems (CPS is a mechanism that is controlled or monitored by computer-based algorithms) to communicate, control processes and have decision-making/problems solving capabilities. Together they enable the development of smart manufacturing. The backbone for implementation of these technologies is information connectivity that has evolved over the years from using machine operators for controlling machines and gather data (first and second phase / industrial revolution) to manufacturing equipment connected via networked computers (third and fourth industrial revolution/phase Fig. 1). The communica...

2003

Some concepts of manufacturing on their own playa decisive role in manufacturing like Integration, Intelligence and Remote Monitoring. They have been tried and tested with great success in various applications in manufacturing. However, very little has been written on the synergy that is created when all three is deployed in one system. It is the aim of this work to survey the attributes of each of these key concepts, to compare them on the grounds of applicability and to study the effects when combined into one system. Final conclusions are made after the hypotheses have been validated with the aid of an experimental model. The first objective of this work is to show how many techniques such as expert systems, fuzzy logic, neural networks and genetic algorithms are used to enable systems to perform intelligently. It is accepted that the competitiveness, growth and profitability of a company in future may depend on the level of its system intelligence. This is so because an intelligent system is able to act appropriately under rapidly changing conditions of customer customisation and demands on quicker throughputs. A further objective of this work is to show how integration adds the element of synergy to a system. This is done by showing several ways of achieving integration by non-technological means like departmental consolidation, plant consolidation, product rationalisation, more flexible working practices, etc. There are as many options for integration by technical means as well, ranging from group technology to process or transfer lines, and from flexible automation such as robots through to hard automation using special-purpose machinery and transfer lines. The third objective is to show how remote monitoring enhances the capabilities of manufacturing systems by synergising with the other two key concepts. With the technology of intelligent manufacturing and integration, larger and more complex manufacturing systems are becoming a reality. However, the danger exists that the shop floor machine tools remain isolated islands of automation.

Manufacturing System, 2012

Numerous and significant challenges are currently being faced by manufacturing companies. Product customization demands are constantly growing, customers are expecting shorter delivery times, lower prices, smaller production batches and higher quality. These factors result in significant increase in complexity of production processes and the necessity for continuous optimization. In order to fulfil market demands, managing the production processes require effective support from computer systems and continuous monitoring of manufacturing resources, e.g. machines and employees. In order to provide reliable and accurate data for factory management personnel the computer systems should be integrated with production resources located on the factory floor. Currently, most production systems are characterized by centralized solutions in organizational and software fields. These systems are no longer appropriate, as they are adapted to high volume, low variety and low flexibility production processes. In order to fulfil current demands, enterprises should reduce batch sizes, delivery times, and product life-cycles and increase product variety. In traditional manufacturing systems this would create an unacceptable decrease in efficiency due to high replacements costs, for example. Modern computer systems devoted to manufacturing must be scalable, reconfigurable, expandable and open in the structure. The systems should enable an on-line monitoring, control and maximization of the total use of manufacturing resources as well as support human interactions with the system, especially on the factory floor. Due to vast amounts of data collected by the systems, they should automatically process data about the manufacturing processes, human operators, equipment and material requirements as well as discover valuable knowledge for the factory's management personnel. The new generation of manufacturing systems which utilizes artificial intelligence techniques for data analyses is referred to as Intelligent Manufacturing Systems (IMS). IMS industrial implementation requires computer and factory automation systems characterized by a distributed structure, direct communication with manufacturing resources and the application of sophisticated embedded devices on a factory floor. Many concepts in the field of organizational structures for manufacturing have been proposed in recent years to make IMS a reality. It seems that the most promising concepts www.intechopen.com

Nowadays, a new generation of responsive factories is needed to face continuous changes in product demand and variety, and to manage complex and variant production processes. To such an aim, self-adaptive automation solutions are required, capable to adapt their control strategy in real-time to cope with planned as well as unforeseen product and process variations. In such a context, the present paper describes an automation solution based on a modular distributed approach for agile factory integration and reconfiguration, integrating a knowledge based cooperation policy providing self-adaptation to endogenous as well as exogenous events. The proposed approach is discussed through its application to a plant for customized shoe manufacturing.

Journal of Intelligent Manufacturing, 2003

The area of intelligent systems has generated a considerable amount of interest—occasionally verging on controversy—within both the research community and the industrial sector. This paper aims to present a unified framework for integrating the methods and techniques related to intelligent systems in the context of design and control of modern manufacturing systems. Particular emphasis is placed on the methodologies relevant to distributed processing over the Internet. Following presentation of a spectrum of intelligent techniques, a framework for integrated analysis of these techniques at different levels in the context of intelligent manufacturing systems is discussed. Integration of methods of artificial intelligence is investigated primarily along two dimensions: the manufacturing product life-cycle dimension, and the organizational complexity dimension. It is shown that at different stages of the product life-cycle, different intelligent and knowledge-oriented techniques are used, mainly because of the varied levels of complexity associated with those stages. Distribution of the system architecture or system control is the most important factor in terms of demanding the use of the most up-to-date distributed intelligence technologies. A tool set for web-enabled design of distributed intelligent systems is presented. Finally, the issue of intelligence control is addressed. It is argued that the dominant criterion according to which the level of intelligence is selected in technological tasks is the required precision of the resulting operation, related to the degree of generalization required by the particular task. The control of knowledge in higher-level tasks has to be executed with a strong involvement of the human component in the feedback loop. In order to facilitate the human intervention, there is a need for readily available, user-transparent computing and telecommunications infrastructure. In its final part, the paper discusses currently emerging ubiquitous systems, which combine this type of infrastructure with new intelligent control systems based on a multi-sensory perception of the state of the controlled process and its environment to give us tools to manage information in a way that would be most natural and easy for the human operator.