The intelligent product in manufacturing control and management

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.

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

IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1999

The paper describes a systematic approach to design and development of software for intelligent manufacturing systems. The approach is based on a multilevel, general object-oriented model of intelligent systems. Current methods and software design and development tools for intelligent manufacturing systems either stress particular components of intelligence (e.g., high-level domain expertise, or learning capabilities, or fuzziness of decisions), or their domain dependence (e.g., monitoring and control systems, or CAPP systems). It is usually difficult to make extensions of such methods and tools, nor is it easy to reuse their components in developing intelligent manufacturing systems. Considerable efforts are being dedicated to the development of interoperable software components, distributed object environments, and flexible and scalable applications to overcome some of these problems. The approach described in this paper starts with a well-founded software engineering principle, making clear distinction between generic, low-level intelligent software components, and domain-dependent, high-level components of an intelligent manufacturing system. It is extensible and adjustable. It also suggests some steps towards design of future software development tools for intelligent manufacturing systems. Several intelligent systems have been developed using the approach. One of these systems, in the cement-manufacturing domain, is briefly overviewed in the paper, illustrating how the approach is used in practice. Finally, some informal discussion on the performance and complexity of the approach is presented.

Department of Electronics & communication Engineering PSIT

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial intelligence (AI) techniques to be considered and then shows how these Artificial Intelligence (AI) techniques are used for the components of intelligent manufacturing systems

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.

Advanced Science Letters, 2013

The current trend of the final product quality increasing is affected by time analysis of the entire manufacturing process. The primary requirement of manufacturing is to produce as many products as soon as possible, at the lowest possible cost, but of course with the highest quality. Such requirements may be satisfied only if all the elements entering and affecting the production cycle are in a fully functional condition. These elements consist of sensory equipment and intelligent control elements that are essential for building intelligent manufacturing systems. Intelligent manufacturing system itself should be a system that can flexibly respond to changes in entering and exiting the process in interaction with the surroundings.

Computers in industry, 1997

International Journal of Recent advances in Mechanical Engineering, 2014

This paper shows a framework of the different techniques for the design of the planning and governing components, implementation, Evolution and execution of an intelligent manufacturing system. Architecture of a modern manufacturing industry is presented, which makes possible to create specific manufactrons system for the specific tasks, depending on the self-operated analysis of its essential characteristics. The manufactronic industry concept helps in the integration of intelligence and to increase flexibility at the maximum level of the manufacturing system as well as at the minimum level of the particular machine. This concept is implemented & demonstrated in the automobile and aeronautical industries, but can be simply applied to nearly all manufacturing industries. Implementation of manufactronic techniques in the industries helps to forecast and to fulfill the rapidly varying customer requirements, to produce high quality products in sufficient quantities with reduction in costs. The Modern Intelligence Technologies are also presented in this paper.

Control Engineering Practice, 2007

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-business-to-manufacturing industry-led issues. Achievement of the integration-in-manufacturing paradigm based on centralized/distributed hardware/software automation architectures is evolving using the intelligence-in-manufacturing paradigm addressed by IMS industry-led R&D initiatives. The remaining goal is to define and experiment with the next generation of manufacturing systems, which should be able to cope with the high degree of complexity required to implement agility, flexibility and reactivity in customized manufacturing. This introductory paper summarizes some key problems, trends and accomplishments in manufacturing plant control before emphasizing for practical purposes some rationales and forecasts in deploying automation over networks, holonic manufacturing execution systems and their related agent-based technology, and applying formal methods to ensure dependable control of these manufacturing systems.

2012

This research is motivated by a practical fastener company which is a supplier for most major automobile factories in China. Each month it receives more than 150 new orders, including 4000+ kinds of fasteners, and the production volume usually keeps in billion-level. As the managers suffer from the bottleneck of collecting the real-time shop-floor information, it is difficult to evaluate the available machine capacity, estimate the latest start time, and release appropriate amount of production orders. This paper presents a reallife case study to describe how to implement Auto-ID technology on a manufacturing shopfloor and use the collected real-time information to support decision-making processes. Meanwhile, the lessons like how to institutionalize a project team, how to defuse the resistance from the personnel will be discussed. We hope the experiences and insights learned from this project can be shared with other fastener manufacturers which are contemplating to adopt Auto-ID technology in their shop-floor management.