Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

Integration strategy for distributed intelligent systems

In this article, we describe a new framework for designing real-time intelligent control systems. An integrated intelligent system is a large knowledge integration environment that consists of both symbolic reasoning systems (expert systems) and numerical computation packages. These modular software programs are controlled by a meta-system which manages the selection, operation, and communication of these programs. This new architecture can serve as a universal configuration to develop high-performance intelligent systems for many complicated application domains in the real-time manufacturing process. As an example, an intelligent optimal control is utilized to illustrate the integrated intelligent control system.     

An object-oriented knowledge based Petri net approach to intelligent integration of design and assembly planning

Artificial intelligence can play an important role in the reduction of manufacturing costs and the enhancement of production efficiency and product quality. In order to assist designers in the early stages of a product development this paper develops an intelligent methodology for integration of design and assembly planning processes, including product design, assembly evaluation and redesign, assembly process planning, design of assembly system and assembly simulation, subjected to both econo-technical and ergonomic evaluations. A new unified class of object-oriented knowledge based Petri nets called OOKPNs, incorporating knowledge based expert systems and fuzzy logic into ordinary place–transition Petri nets, is defined and used for the representation and modeling of the distributed design processes. A prototype intelligent integrated design and assembly planning system (IIDAP) is implemented through distributed blackboard structure with concurrent integration of multiple cooperative knowledge sources and software. It consists essentially of the networked agents and the meta-system, each of which is a knowledge Petri net system with the capabilities of problem solving, learning and conflict resolution, and can be obtained through the inheritance, polymorphism and dynamic binding of instances of OOKPNs. In IIDAP system, both C/C++ language and COOL (CLIPS object-oriented language) are used to incorporate a Petri net tool, a geometric modeling and design tool, a planner and simulator and an evaluation tool. By use of this system, product design and assembly planning can be carried out simultaneously and intelligently in an entirely computer-aided concurrent design and assembly planning system. The design of manufacturable, cost-effective, usable products can therefore be achieved rapidly and flexibly. The developed methodology and system have been successfully applied to assembly design and planning of a micro switch.     

GMVN oriented S-BOX knowledge expression and reasoning framework

Global Manufacturing Virtual Network (GMVN) is a manufacturing architecture, which integrates all kinds of enterprises and production centers to construct and reconstruct agile supply network, and provides manufacturing services on unpredictable and fragmented market demand. However due to the geographical and cultural differences, and lack of a unified knowledge expressing and intelligent reasoning framework for GMVN, the system elements can not be integrated well. To achieve intelligent integration of the geographically dispersed worldwide manufacturing resource, capacity and service, the intelligent integration framework of Semantic Bill of X (S-BOX) is proposed by combining with the technologies of ontology, Semantic Web Rule Language (SWRL), Bill of X (BOX). Using this framework, we analyze the characteristics, structure of GMVN system, and construct GMVN oriented S-BOX models which include the ontology structure models and the reasoning rules sets. With the ontology structure models, we can express the elements and the relationships of GMVN. And with the reasoning rules sets, the business characteristics of the GMVN systems will be transformed into the SWRL rules. Therefore, the integrated expression and intelligent reasoning of the manufacturing system elements can be realized in the semantic knowledge layer. Furthermore, an applied case of GMVN project is given to verify the validity of this method.     

Agent-based intelligent system development for decision support in chemical process industry

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.     

Modular manufacturing

This paper discusses requirements to be satisfied by future manufacturing systems and proposes a new concept of modular manufacturing to integrate intelligent and complex machines. In large-scale systems such as manufacturing systems, modularization is indispensable for clarifying logical structure and assuring a high degree of ease of construction. The parts, products and manufacturing equipments as well as the design and operating activities themselves are all described in units called modules. A manufacturing system is constructed and operated by combining these in building-block style. The creation of this manufacturing system relies on construction and operating systems that enable design and simulation in the virtual world, and production and control in the real world, in a unified approach. Hardware modules and software modules are compiled flexibly and hierarchically to fulfil specified tasks. A system in which modular manufacturing as a concept of system integration is applied to manufacturing robots is called a modular robot system. The robots are embedded in manufacturing systems as the highest application of model-based robotics.     

A general framework of a Reference Model for Intelligent Integrated Manufacturing Systems (REMIMS)

Today's highly competitive business environment forces the managers to continuously make the best decisions in the shortest possible time. The ability to provide concurrency among manufacturing functions is a critical need for modern organizations as, especially, distributed environment requires synchronization of manufacturing functions. Moreover, manufacturing companies need to have strong capability of adaptation (agility) mainly because of the dynamic relationships that must be established between manufacturing units. To achieve these, there is a need for an integrated manufacturing system that will handle all interactions and interrelationships which will then be affected by the changes and create maximum gain under limited resources. In order to create and effectively manage such an integrated manufacturing system there is a need for a reference model. In this paper, such a reference model called REference Model for intelligent Integrated Manufacturing System (REMIMS) is introduced. REMIMS has hierarchical architecture with several agents responsible for different manufacturing functions. To facilitate REMIMS and allow interaction among the agents to share their knowledge, a specific knowledge exchange protocol in a knowledge network is being developed.     

An architectural model for intelligent network management

Traditional network management approach involves the management of each vendor‘s equipment and networkd segment in isolation through its own proprietary element management system.It is necessary to set up a new network management architecture that calls for operation consolidation across vendor and technology boundaries.In this paper,an architerctural model for Intelligent Network Management(INM)is presented.The INM system includes a manager system,which controls all subsystems and coordinates different management tasks;an expert system,which is responsible for handling particularly difficult problems,and intelligent agents,which bring the management closer to applications and user requirements by spreading intellignet agents through network segments or domain.In the expert system model proposed,especially an intellignet fault management system is given.The architectural model is to build the INM system to meet the need of managing modern network systems.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

INTELLIGENT SENSOR FUSION AND LEARNING FOR AUTONOMOUS ROBOT NAVIGATION

ABSTRACT

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.     

Applications of artificial intelligence in intelligent manufacturing: a review

Based on research into the applications of artificial intelligence (AI) technology in the manufacturing industry in recent years, we analyze the rapid development of core technologies in the new era of ‘Internet plus AI’, which is triggering a great change in the models, means, and ecosystems of the manufacturing industry, as well as in the development of AI. We then propose new models, means, and forms of intelligent manufacturing, intelligent manufacturing system architecture, and intelligent manufacturing technology system, based on the integration of AI technology with information communications, manufacturing, and related product technology. Moreover, from the perspectives of intelligent manufacturing application technology, industry, and application demonstration, the current development in intelligent manufacturing is discussed. Finally, suggestions for the application of AI in intelligent manufacturing in China are presented.     

Manufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenario

ABSTRACT

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.     

INTELLIGENT COMPUTER INTEGRATED MANUFACTURING (I-CIM): Research Perspectives

Abstract

Manufacturing automation has progressed through various stages from simple data transfer to the intelligence-intensive systems. The future of CIM relies heavily on intelligence-intensive systems because manufacturing is no longer confined to one local site and manufacturing systems have become complex because of their global nature. In this article, the authors study the future manufacturing environment as a collaborative effort. The essential characteristics-the requirements for integration from a process and communication perspective-are identified as are steps in the process requiring further study. Finally, Intelligent-Computer Integrated Manufacturing (I-CIM) scenarios are presented for specific problems.     

Agent-based approach for manufacturing integration: The ciimplex experience

The production management system used by most manufacturers today consists of disconnected planning and execution processes and lacks the support for interoperability and collaboration needed for enterprise-wide integration. This situation often prevents the manufacturer from fully exploring market opportunities in a timely fashion. To address this problem, we are exploring an agent-based approach to intelligent enterprise integration. In this approach, a set of agents with specialized expertise can be quickly assembled to help with the gathering of relevant information and knowledge, to cooperate with each other and with other parts of the production management system and humans to arrive at timely decisions in dealing with various enterprise scenarios. The proposed multiagent system, including its architecture and implementation, is presented and demonstrated through an example integration scenario involving real planning and execution software systems.     

SQA-Mashup: A mashup framework for continuous integration

ContextContinuous Integration (CI) has become an established best practice of modern software development. Its philosophy of regularly integrating the changes of individual developers with the master code base saves the entire development team from descending into Integration Hell, a term coined in the field of extreme programming. In practice, CI is supported by automated tools to cope with this repeated integration of source code through automated builds and testing. One of the main problems, however, is that relevant information about the quality and health of a software system is both scattered across those tools and across multiple views.ObjectiveThis paper introduces a quality awareness framework for CI-data and its conceptional model used for the data integration and visualization. The framework called SQA-Mashup makes use of the service-based mashup paradigm and integrates information from the entire CI-toolchain into a single service.MethodThe research approach followed in our work consists out of (i) a conceptional model for data integration and visualization, (ii) a prototypical framework implementation based on tool requirements derived from literature, and (iii) a controlled user study to evaluate its usefulness.ResultsThe results of the controlled user study showed that SQA-Mashup’s single point of access allows users to answer questions regarding the state of a system more quickly (57%) and accurately (21.6%) than with standalone CI-tools.ConclusionsThe SQA-Mashup framework can serve as one-stop shop for software quality data monitoring in a software development project. It enables easy access to CI-data which otherwise is not integrated but scattered across multiple CI-tools. Our dynamic visualization approach allows for a tailoring of integrated CI-data according to information needs of different stakeholders such as developers or testers.     

Extending IDEF1X to model fuzzy data

IDEF1X has provided a formal framework for consistent modeling of the data necessary for the integration of various functional areas in computer integrated manufacturing (CIM). The basic idea has been extensively applied in current manufacturing industry. Imprecise and uncertain information, however, is generally involved in many engineering activities. It is especially true for constructing intelligent manufacturing systems. This paper provides extensions to the IDEF1X, which makes it possible to represent fuzzy information.     

Intelligent systems in the automotive industry: applications and trends

There is a common misconception that the automobile industry is slow to adapt new technologies, such as artificial intelligence (AI) and soft computing. The reality is that many new technologies are deployed and brought to the public through the vehicles that they drive. This paper provides an overview and a sampling of many of the ways that the automotive industry has utilized AI, soft computing and other intelligent system technologies in such diverse domains like manufacturing, diagnostics, on-board systems, warranty analysis and design. Oleg Gusikhin received the Ph.D. degree from St. Petersburg Institute of Informatics and Automation of the Russian Academy of Sciences and the M.B.A. degree from the University of Michigan, Ann Arbor, MI. Since 1993, he has been with the Ford Motor Company, where he is a Technical Leader at the Ford Manufacturing and Vehicle Design Research Laboratory, and is engaged in different functional areas including information technology, advanced electronics manufacturing, and research and advanced engineering. He has also been involved in the design and implementation of intelligent control applications for manufacturing and vehicle systems. He is the recipient of the 2004 Henry Ford Technology Award. He holds two U.S. patents and has published over 30 articles in refereed journals and conference proceedings. He is an Associate Editor of the International Journal of Flexible Manufacturing Systems. He is also a Certified Fellow of the American Production and Inventory Control Society and a member of IEEE and SME. Nestor Rychtyckyj received the Ph.D. degree in computer science from Wayne State University, Detroit, MI. He is a technical expert in Artificial Intelligence at Ford Motor Company, Dearborn, MI, in Advanced and Manufacturing Engineering Systems. His current research interests include the application of knowledge-based systems for vehicle assembly process planning and scheduling. Currently, his responsibilities include the development of automotive ontologies, intelligent manufacturing systems, controlled languages, machine translation and corporate terminology management. He has published more than 30 papers in referred journals and conference proceedings. He is a member of AAAI, ACM and the IEEE Computer Society. Dimitar P. Filev received the Ph.D. degree in electrical engineering from the Czech Technical University, Prague, in 1979. He is a Senior Technical Leader, Intelligent Control and Information Systems with Ford Research and Advanced Engineering specializing in industrial intelligent systems and technologies for control, diagnostics and decision making. He is conducting research in systems theory and applications, modeling of complex systems, intelligent modeling and control, and has published 3 books and over 160 articles in refereed journals and conference proceedings. He holds 14 granted U.S. patents and numerous foreign patents in the area of industrial intelligent systems He is the recipient of the 1995 Award for Excellence of MCB University Press. He was awarded the Henry Ford Technology Award four times for development and implementation of advanced intelligent control technologies. He is an Associate Editor of International Journal of General Systems and International Journal of Approximate Reasoning. He is a member of the Board of Governors of the IEEE Systems, Man and Cybernetics Society and President of the North American Fuzzy Information Processing Society (NAFIPS).     

Sliding mode control for trajectory tracking of mobile robot in the RFID sensor space

This paper presents a sliding mode control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. A robust control law which is called sliding mode control is proposed for asymptotically stabilizing the mobile robot to a desired trajectory. The posture of the mobile robot (including the position and heading direction) is presented and the kinematics equations are established in the two-dimensional coordinates. According to the kinematics equations, the controller is designed to find an acceptable control law so that the tracking error will approximate 0 as the time approaches infinity with an initial error. The RFID sensor space is used to estimate the real posture of the mobile robot. Simulation and experiment demonstrate the efficacy of the proposed system for robust tracking of mobile robots. Recommended by Sooyong Lee under the direction of Editor Jae-Bok Song. This work was supported by the Korea Science and Engineering (KOSEF) grant funded by the Korea government (MOST) (No. R01-2007-000-10171-0). Jun Ho Lee received the M.S degree in Mechanical Engineering from Pusan National University. His research interests include factory automation and sliding mode control. Cong Lin received the B.S. degree in Electrical Engineering from Jilin University and the M.S degree in Electrical Engineering from Pusan National University. His research interests include neural network and sliding mode control. Hoon Lim is currently a M.S student in Electrical Engineering of Pusan National University. His research interests include mobile manipulator and sliding mode control. Jang Myung Lee received the B.S. and M.S degrees in Electronics Engineering from Seoul National University, Korea. He received the Ph.D. degree in Computer from the University of Southern California, Los Angeles. Now, he is a Professor in Pusan National University. His research interests include integrated manufacturing systems and intelligent control.     

水利数据多库智能整合机制研究与应用

面向多种水利要素的整合应用需求,结合水利数据分库独立管理的特点,提出水利数据多库智能整合机制,利用统一数据资源索引模型对异构的水利业务数据进行一体化描述与整合,支持异构数据库的接入与动态扩展,并提出水利数据整合资源的服务工作流,为水利数据资源的有效整合提供可行的思路。该机制在长江水文资源整合和应用服务系统中得以验证,可实现多个水利业务数据库的整合,极大提升水利数据的管理和服务效率。