Intelligent Manufacturing Systems

An intelligent manufacturing system is intended to produce one or more subjects that it is designed for in an optimal way. This means that it has to find a proper production process to produce the subject in an optimal way. The manufacturing system can be called "intelligent" when it is able to find applicable optimisation criteria upon its past experiences thus improving its performance in future. Therefore, an intelligent manufacturing system needs capabilities to store data and make decisions upon them. Such a "brain" can be established by a proper design of a technological database and its database management system (DBMS). Examining all constitutive parameters of a work operation a model of a production process organization can be made, which can serve as a basis for a suitable database design. In addition, an application programme that will check the existence and availability of work operations in the database has to be added to the DBMS. What remains are some optimisation criteria upon which we will choose an operation among suitable and available work operations. This task is fulfilled by a genetic algorithm optimisation technique that would consider work operations' data as parameters of optimisation and on this basis search the optimal one out of the set of available operation.

We present a study of production planning and management in the Control Room of a manufacturing plant producing mass-customised diesel engines. The study illustrates how these activities are subject to various 'worldly contingencies' and how the production process emerges from the situated and resourceful activities of various kinds of professionals in the plant. These observations have serious implications for the question of how the various kinds of "intelligent manufacturing systems" that have been devised can be employed. While the plant studied uses various kinds of automation systems, it critically depends on situated and resourceful action. We contend that for any kind of production management technology to be successful, it has to build on the everyday working life experiences of those working in production environments. This implies reconceptualising IT systems design and development to support evolutionary processes through 'design-in-use'.

Issues related to the design of large systems are explored based on the axiomatic approach to design. A large system has the characteristic of having to satisfy varying sets of functional requirements (FRs) over its lifetime, some of which are not known a priori. Such a system must therefore be designed to accommodate these varying and sometimes unknown sets of functional requirements~ The design must also allow the system to have an expandable database of design parameters (DPs) to satisfy yet-unknown FRs and endow the system with "intelligence" to make correct decisions. The independence axiom and the information axiom are the basis for several theorems that are derived for large systems. These theorems characterize or establish the bounds on the large systems. The implications of these theorems are examined using organizational design and the design of intelligent manufacturing systems as examples. Finally, the human cognitive process, including human creativity, is discussed as an example of a large system.

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.

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP.

New researches in Artificial intelligence have given wide powers for many areas, which based on the fact that the performance of machine is better than humans. Widely, one of the most significant areas in Artificial Intelligence is education and learning. Occasionally the traditional teaching methods do not achieve the required goals from learning. Some teaching progra comparing to traditional class room. appropriate learning procedures are children with learning disabilities. Parents always capability to understand new subjects and to develop expertise. Early interference is very important to help children in their early stages for enhance their learning capacity. on black board system to assists the children sufferer from learning disabilities during the early levels of school. The proposed system targets the child with typical intellectual quotient (IQ). During the implementation of the system the child will go through two types of tests and experiments, which will determine the level of (IQ) and evaluate the diagnosis purposes (developmental disabilities). The rules of the inference engine are generated based on the knowledge engineering resulted from the test. Education are one of most requirements needed for children, Which mean keep the student interest and motivated for learn. Diagnosing various learning disabilities is costly process and can be highly delayed disabilities meanwhile they need to accomplish teach mission using variety of education materials. Diagnosing various LD depend exclusively on our understanding of scientific r communication with the Infected People. in various learning systems. Numerous researches demonstrate that kids are more concerned in computer games. Thus, serious games are

Intelligent Manufacturing Systems require fault-tolerant components capable of preventing and detecting failures, and optimizing their own performance. Although there are a number of process monitoring and control systems (PMCS) available for these tasks, most of them require special equipment that increases cost and setup time. This work proposes a standard software architecture and methodology for implementing a sensorless PMCS which separates data representation from its algorithmic processing. Two different case studies are introduced showing the feasibility and applicability of the proposed design, one for cycle time optimization and another one for online tool condition detection. A good performance was obtained in both case studies based on the proposed PMCS architecture, which can be implemented using different distributed software technologies.

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.

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.

A Holonic Manufacturing System HMS is a manufacturing system where key elements, such as machines, cells, factories, parts, products, operators, teams, etc., are modeled as holons" having autonomous and cooperative properties. The decentralized information structure, the distributed decision-making authority, the integration of physical and informational aspects, and the cooperative relationship among holons, make the HMS a new paradigm, with great potential for meeting today's agile manufacturing challenges. Critical issues to be investigated include how t o de ne holons for a given problem context, what should be the appropriate system architecture, and how to design e ective cooperation mechanisms for good system performance. In this paper, holonic scheduling is developed for a factory consisting of multiple cells. Relevant holons are identi ed, and their relationships are delineated through a n o vel modeling of the interactions among parts, machines, and cells. The cooperation mechanisms among holons are established based on the pricing concept of market economy following Lagrangian relaxation" of mathematical optimization, and cooperation across cells is performed without accessing individual cells' local information nor intruding on their decision authority. The system also possesses structural recursivity and extendibility. Numerical testing shows that the method can generate near-optimal schedules with quanti able quality in a timely fashion, and has comparable computational requirements and performance as compared to the centralized method following single-level Lagrangian relaxation. The method thus provides a theoretical foundation for guiding the cooperation among holons, leading to globally near-optimal performance.

Manufacturing systems today operate in a changing environment rife with uncertainty and disturbances. Difficulties arise from unexpected tasks / events, nonlinearities, and a multitude of interactions, possible failures while attempting to control various activities in dynamic shop floors. The fundamental aim of the paper is to outline reactive and proactive approaches on the one hand, and distributed control architectures, on the other hand, for change and disturbance management in manufacturing.

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.

Design and integration of multi-agent systems are becoming more and more important since there is evidence of a shift of multi-agent application deployment from academic or lab testing areas to real world applications such as manufacturing, aerospace, logistics, and networking. Therefore, utilization of an environment that guides the development process starting from the design of templates that can be reused multiple times up to the code generation and debugging is essential. In this manuscript we present advancements in the Agent Development Environment that can be used not only for the development of agents designed for high-level decision-making, but also for distributed low-level control that is automatically integrated.

There is an increasing demand for manufacturing processes to improve product quality and production rates while minimising the costs. The quality of the products is influenced by several sources of errors introduced during the series of manufacturing operations. These errors accumulate over these multiple stages of manufacturing. Therefore, monitoring systems for product health utilising data and information from different sources and manufacturing stages is a key factor to meet these growing demands. This paper addresses the process of combining new measurement data or information with machine learning-based prediction information obtained as each product goes through a series of manufacturing steps to update the conditional probability distribution of the end product quality during manufacturing. A Bayesian approach is adopted in obtaining an updated posterior distribution of the end product quality given new information from subsequent measurements, and, in particular, On-Machine Probing (OMP). Following the steps of heat treatment, machining, and OMP, the posterior distribution of the previous step can be considered as the new prior distribution to obtain an updated posterior distribution of the product condition as new metrological information becomes available. It is demonstrated that the resulting posterior estimates can lead to more efficient product condition monitoring in multistage manufacturing.

We present a study of production planning and management in the Control Room of a manufacturing plant producing mass-customised diesel engines. The study illustrates how these activities are subject to various 'worldly contingencies' and how the production process emerges from the situated and resourceful activities of various kinds of professionals in the plant. These observations have serious implications for the question of how the various kinds of "intelligent manufacturing systems" that have been devised can be employed. While the plant studied uses various kinds of automation systems, it critically depends on situated and resourceful action. We contend that for any kind of production management technology to be successful, it has to build on the everyday working life experiences of those working in production environments. This implies reconceptualising IT systems design and development to support evolutionary processes through 'design-in-use'.

We present a study of production planning and management in the Control Room of a manufacturing plant producing mass-customised diesel engines. The study illustrates how these activities are subject to various 'worldly contingencies' and how the production process emerges from the situated and resourceful activities of various kinds of professionals in the plant. These observations have serious implications for the question of how the various kinds of "intelligent manufacturing systems" that have been devised can be employed. While the plant studied uses various kinds of automation systems, it critically depends on situated and resourceful action. We contend that for any kind of production management technology to be successful, it has to build on the everyday working life experiences of those working in production environments. This implies reconceptualising IT systems design and development to support evolutionary processes through 'design-in-use'.

In distributed manufacturing systems, the level of interoperability of hardware and software components depends on the quality and flexibility of their information models. Syntactic descriptions of input and output parameters, e.g., using interface description languages (IDL), are not suffcient when it comes to evaluating whether a manufacturing resource provides the capabilities that are required for performing a particular process step on a product. The semantics of capabilities needs to be explicitly modelled and must be provided together with manufacturing resources. In this paper, we introduce concepts developed by the German BaSys 4.0 initiative dealing with semantically describing manufacturing skills, orchestrating higher-level skills from basic skills, and using them in a cognitive manufacturing framework.

— Credit scoring is an important tool in financial institutions, which can be used in credit granting decision. Credit applications are marked by credit scoring models and those with high marks will be treated as " good " , while those with low marks will be regarded as " bad ". As data mining technique develops, automatic credit scoring systems are warmly welcomed for their high efficiency and objective judgments. Many machine learning algorithms have been applied in training credit scoring models, and ANN is one of them with good performance. This paper presents a higher accuracy credit scoring model based on MLP neural networks trained with back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: optimize data distribution in datasets using a new method called Average Random Choosing; compare effects of training-validation-test instances numbers; and find the most suitable number of hidden units. Another contribution of this paper is summarizing the tendency of scoring accuracy of models when the number of hidden units increases. The experiment results show that our methods can achieve high credit scoring accuracy with imbalanced datasets. Thus, credit granting decision can be made by data mining methods using MLP neural networks.

We present a study of production planning and management in the Control Room of a manufacturing plant producing mass-customised diesel engines. The study illustrates how these activities are subject to various 'worldly contingencies' and how the production process emerges from the situated and resourceful activities of various kinds of professionals in the plant. These observations have serious implications for the question of how the various kinds of "intelligent manufacturing systems" that have been devised can be employed. While the plant studied uses various kinds of automation systems, it critically depends on situated and resourceful action. We contend that for any kind of production management technology to be successful, it has to build on the everyday working life experiences of those working in production environments. This implies reconceptualising IT systems design and development to support evolutionary processes through 'design-in-use'.

Agility is an essential requirement for manufacturers to increase market share and profitability in a highly competitive industry. Having reconfigurable production lines is considered an essential aspect of modern manufacturing operations. This article investigates the possibility of using a non-cooperative game theoretic technique for making reconfiguration decisions at the resource controller level in such an environment. Under the proposed approach, a resource controller evaluates a configuration game in order to determine appropriate actions when faced with a reconfiguration decision. The prescribed action is defined by pure or mixed strategy equilibriums inside the configuration game. By evaluating each game, the resource controller can account for the potential actions and reactions of other resource controllers in the system. The investigation shows the applicability of such an approach to optimally resolving resource-sharing issues in an example semiconductor manufacturing environment.

The purpose of this work is to study the use of controlled magnetic oscillation of the electric arc according to complex geometric patterns applied to the GMAW process for root pass welding. These patterns were represented here by what is known as Figures of Lissajous. These one are parametric curves mathematically produced that can offer versatility for the creation of wetting movements similar to those practiced by experienced welders. These curves are basically produced by applying and controlling sine equations for the orthogonal axes "X" and "Y". An electrical voltage generator/controller system was developed for this work and its software combines and controls the variables, frequency (Hz), amplitude (V), phase (rad) and offset (%) of the sine equation of each axis. This system was built to feed two pairs of electromagnets magnetic field generators, each pair corresponding to its respective axis. The electromagnets, in turn, receive from the system the appropriate electrical voltage to produce magnetic fields with specific magnetic density (mT) values. Once the electromagnets are positioned on their respective orthogonal axes with reference to the vertical center line of the electric arc, it becomes possible to make weave movements through the magnetic flux that was generated and directed to the arc. With this equipment, the welding was performed in order to deflect the electric arc following the forms of Figures of Lissajous.
With this resource/equipment in the process, in addition to promoting controlled deflection of the electric arc (difficult to be achieved manually or mechanically), it was possible to deposit addition metal in specific locations, modify the characteristics of the thermal, distribution located in the welding region, contribute to the formation of a fusion pool less concentrated in the weld region and produce beads with potential to overcome obstacles such as misalignments and uneven clearances typical of the stage known as root pass.
Prior to the groove joints welding using the pulsed GMAW process, the magnetic oscillator system that was built was put to the test in a sequence of initial trials performed with the autogenous GTAW and GMAW processes with short circuit transfer. Imposing weave movements to the arc following two different Lissajous Figure in the autogenous GTAW welds was clearly evidenced transverse and longitudinal movements of the molten pool as a visual and dimensional characteristics of the beads that were produced. In short-circuited GMAW welds such movement was not clearly visualized. However, through thermographic records obtained on the opposite side of weld beads made on plate, it was possible to record thermal differences with higher or lower heat concentration between beads produced without and with weaving patterns. This fact suggests that the magnetic oscillation imposed on the process interfered in the thermal distribution of the weld.
The welds in the GMAW process pulsed in groove joints parts were produced by applying weave following four different Lissajous Figures. The results were analyzed according to dimensional characteristics of the respective beads and electrical signals recorded in oscillograms. These results showed that the application of the controlled magnetic oscillation imposed to the arc was able to produce root pass welds in flat positioned groove joints parts and these with an average clearance of the 3.7 mm between their faces, and without the application of the oscillation the average root opening was 2.4 mm.
Additionally, this Thesis established a basic concept called "Rate of variation of electrical voltage" which proved to be a consequence of the magnetic field applied in the arc.

Manufacturing processes usually consist of multiple different stages, each of which is influenced by a multitude of factors. Therefore, variations in product quality at a certain stage are contributed to by the errors generated at the current, as well as preceding, stages. The high cost of each production stage in the manufacture of high-quality products has stimulated a drive towards decreasing the volume of non-added value processes such as inspection. This paper presents a new method for what the authors have referred to as ‘inspection by exception’ – the principle of actively detecting and then inspecting only the parts that cannot be categorized as healthy or unhealthy with a high degree of certainty. The key idea is that by inspecting only those parts that are in the corridor of uncertainty, the volume of inspections are considerably reduced. This possibility is explored using multistage manufacturing data and both unsupervised and supervised learning algorithms. A case study is presented whereby material conditions and time domain features for force, vibration and tempering temperature are used as input data. Fuzzy C-Means (FCM) clustering is implemented to achieve inspection by exception in an unsupervised manner based on the normalized Euclidean distances between the principal components and cluster centres. Also, deviation vectors for product health are obtained using a comparator system to train neural networks for supervised learning-based inspection by exception. It is shown that the volume of inspections can be reduced by as much as 82% and 93% using the unsupervised and supervised learning approaches, respectively.

Manufacturing systems are changing its structure and organisation. Supply chain structure is evolving to more dynamic organisations such extended enterprise and virtual enterprises, enhancing single entity autonomy, adaptability and dynamism properties. Such organisations are very different from the established, which imply organisational and technological shift through distribution, decentralisation, reactivity, flexibility and agility. New organisational and technological paradigms are needed in order to reply to these challenges. This paper present a brief description of three organisational paradigms, and relating to its properties, proposes a holonic manufacturing system architecture. Developed complementary services are introduced and its advantages in overall system new requirements are justified.

Understanding the variations in surface finish due to machining is a non-trivial task and cannot be very easily estimated even for a given set of machining parameters and operating conditions due to the complexity of interactions involved. In this work, an attempt has been made to propose an automated intelligent manufacturing system for the estimation and control of surface finish

Modern manufacturing is in need of flexible and adaptive concepts for process planning, production control, and scheduling. However, strong borderlines exist in industrial production caused by an extreme specialisation and seperated historical paths of system evolution. Thus, a gap exists between the involved systems, which implies loss of time and information and in consequence a prolonged time-to-market. The application of methods from "Distributed Artificial Intelligence" (especially of multiagent systems) is very promising to improve information logistics between process planning and production scheduling. This approach is used by an ongoing research project and will be discussed in this paper.

This white paper will outline a path to production-ready, decision-making AI by drawing on key concepts from the history of AI and combining them with automation technology in pursuit of useful AI. We assert that useful AI provides the next evolution in industrial control, automation, and optimization and that teaching is the primary mechanism for enabling useful AI.

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 fourth industrial revolution is rapidly changing the manufacturing landscape. Due to the growing research and fast evolution in this field, no clear definitions of these concepts yet exist. This work provides a clear description of technological trends and gaps. We introduce a novel method to create a map of Industry 4.0 technologies, using natural language processing to extract technology terms from 14,667 research articles and applying network analysis. We identified eight clusters of Industry 4.0 technologies, which served as the basis for our analysis. Our results show that Industrial Internet of Things (IIoT) technologies have become the center of the Industry 4.0 technology map. This is in line with the initial definitions of Industry 4.0, which centered on IIoT. Given the recent growth in the importance of artificial intelligence (AI), we suggest accounting for AI’s fundamental role in Industry 4.0 and understanding the fourth industrial revolution as an AI-powered natura...

High temperature deformation behavior of Al–5.9%Cu–0.5%Mg alloy and Al–5.9%Cu–0.5%Mg alloy containing 0.06 wt.% Sn was studied by hot compression tests conducted at different temperatures and strain rates. Trace content of Sn resulted in a significant increase of flow stress for various processing conditions. Artificial neural network (ANN) modeling was applied providing excellent prediction of flow stress at different combinations of strain, strain rate and deformation temperature. While validation, it was possible to predict 100 % and 89 % of the flow stress values of the respective alloys within an error less than ± 10 %. The flow stress data thus generated using the ANN architectures, was used to develop power dissipation efficiency maps, instability maps and subsequently the processing maps, which would delineate the process domains for safe metal working. Optimum hot processing window was suggested for the investigated alloys, providing intelligent processing and manufacturing system of these wrought microalloyed Al alloys, extensively used in aircraft and space applications. The power dissipation efficiency maps revealed a maximum efficiency of 60 % for the alloy without Sn content, while a comparatively lower value of 40 % for the microalloyed material. The instability maps generated for the alloy containing Sn, revealed only one instability regime. The safe regimes for hot working of the base alloy without Sn content were observed at (i) very low strain rate (< 0.003 s-1) with temperature < 450 ºC and (ii) high temperature (> 400 ºC) with strain rate > 0.02 s-1. The safe processing zone of the alloy with trace content of Sn, is at low strain rate (< 0.01 s-1) for the entire range of temperatures studied. Microstructural analysis confirmed that dynamic recovery (DRV) and dynamic recrystallization (DRX) characterized the safe processing regimes of both the alloys. Instability during hot deformation was observed to be driven mainly by shear band formation and/or intercrystalline cracking for the investigated Al alloys. keywords: Aluminum alloy; Microalloying; High temperature deformation behavior; Artificial neural network, Processing map.

& One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed.

New generation manufacturing systems are involved in a transformation process which aims for more reliable production processes and with a lower response time to the demand of the market. This work presents an application of arti®cial intelligence planning techniques for the automatic generation of the control program for a manufacturing system expressed as a safe and live Petri net. The advantage of the system presented here is straightforward: it allows for a fast generation of sound results free of human errors, reducing the cost and duration of the development phase of control programs.

Today's complex manufacturing systems operate in a changing environment rife with uncertainty. The performance of manufacturing companies ultimately hinges on their ability to rapidly adapt their production to current internal and external circumstances. On the base of a running national research project on digital enterprises and production networks, the paper illustrates how the concepts of intelligent manufacturing systems and digital

Modern manufacturing is in need of flexible and adaptive concepts for process planning, production control, and scheduling. However, strong borderlines exist in industrial production caused by an extreme specialisation and seperated historical paths of system evolution. Thus, a gap exists between the involved systems, which implies loss of time and information and in consequence a prolonged time-to-market. The application of

Abstract: In this paper a concept of designing and building intelligent decision support systems in production management is introduced. The new approach to the design of intelligent management systems is proposed based on integration of artificial intelligence technologies (fuzzy logic, artificial neural networks, expert systems and genetic algorithms) with exact methods and models of decisions search and simulation techniques. The proposed approach allows for creating intelligent decision support systems of complex, unstructured management problems in fuzzy conditions. The systems learn based on accumulated data and adapt to changes in operation conditions.

Agility is an essential requirement for manufacturers to increase market share and profitability in a highly competitive industry. Having reconfigurable production lines is considered an essential aspect of modern manufacturing operations. This article investigates the possibility of using a non-cooperative game theoretic technique for making reconfiguration decisions at the resource controller level in such an environment. Under the proposed approach, a resource controller evaluates a configuration game in order to determine appropriate actions when faced with a reconfiguration decision. The prescribed action is defined by pure or mixed strategy equilibriums inside the configuration game. By evaluating each game, the resource controller can account for the potential actions and reactions of other resource controllers in the system. The investigation shows the applicability of such an approach to optimally resolving resource-sharing issues in an example semiconductor manufacturing environment.

Intelligent Manufacturing System (IMS) shall be considered as a system integrated with different intelligent subsystems, which completes the distributed solution procedure on the basis of exchanging large quantities of materials, energy and information. Any intelligent manufacturing process selfregulates and self-controls itself in manufacturing the products within the design specifications. It incorporates introduction of human-like decision-making capabilities into the manufacturing system that brings the intelligence. The system however requires complete information of the requirements and processing of production system to generate appropriate decisions. This paper provides a comprehensive review of research on web-enabled e-Manufacturing technology which bridges certain aspects of manufacturing system like Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Supply Chain Management (SCM). Web-based systems have been implemented to provide remote service and manufacturing for rapid prototyping, enhance the availability of facilities and improve the capability of rapid product development for various enterprises. Web-based technique is utilized to derive the availability of information anytime, anywhere and by the persons who are authorized to deal with it. The e-Manufacturing production system can substantially reduce product cost and delivery cycle for manufacturing systems.

Manufacturing informatics aims to optimize productivity by extracting information from numerous data sources and making decisions based on that information about the process and the parts being produced. Manufacturing processes usually include a series of costly operations such as heat treatment, machining, and inspection to produce high-quality parts. However, performing costly operations when the product conformance to specifications cannot be achievable is not desirable. This paper develops a new machine learning-based informatics system capable of predicting the end product quality so that non-value-adding operations such as inspection can be minimized and the process can be stopped before completion when the part being manufactured fails to meet the design specifications.

A system devoted to the planning of motion and assembly operations n robotized assembly cells at abstract level is presented. The goal is to achieve an interactive planning system for assembly tasks. Automatic plan generation techniques are interplayed with external interactive help from the human expert. The interaction is realized through a graphical simulation system. For instance, a collision-free skeleton-trajectory can be specified by the human expert via the simulation system. Proposed architecture for a cell programming system has a multilevel structure in which a task can
be. described at multiple abstraction levels. The problem is analyzed
in a CIM context in which the integration of information and knowledge plays an important role. A detailed description of the planning activity is m,de and experimental results already achieved are described. Finally, current status of work and future plan are presented.