Collaborative development of engineered products in a business-to-business (B2B) environment will require more than just the selection of components from an on-line catalogue. It will involve the electronic exchange of product, process, and production engineering information during both design and manufacturing. While the state-of-the-practice does include a variety of ways to exchange product data electronically, it does not extend to the exchange of manufacturing process data. The reason is simple; process data is usually tied to specific manufacturing resources. These resources are not known typically at product development time. This paper proposes an approach, called an Integrated Product and Process Data (IPPD), where manufacturing process data is considered during product development. This approach replaces traditional process plans, which are resource specific, with a resource-independent process representation. Such a representation will allow a much wider collaboration among business partners and provide the necessary base for collaborative product development.
In this paper a knowledge-level model of an individual designer as an agent is described, in which reflective reasoning about elements of situatedness, and reasoning from the point of view of other participants, are explicitly modelled. This model is based on existing models of single agent design. An individual designer in a specific distributed design process, namely website design, is used to illustrate the model.
This research presents an application of the relatively new approach of ant colony optimization (ACO) to address a production-sequencing problem when two objectives are present — simulating the artificial intelligence agents of virtual ants to obtain desirable solutions to a manufacturing logistics problem. The two objectives are minimization of setups and optimization of stability of material usage rates. This type of problem is NP-hard, and therefore, attainment of IP/LP solutions, or solutions via complete enumeration is not a practical option. Because of such challenges, an approach is used here to obtain desirable solutions to this problem with a minimal computational effort. The solutions obtained via the ACO approach are compared against solutions obtained via other search heuristics, such as simulated annealing, tabu search, genetic algorithms and neural network approaches. Experimental results show that the ACO approach is competitive with these other approaches in terms of performance and CPU requirements.
In this paper we describe a structured method for developing a conceptual data model by starting from a functional model expressed in a natural language. We have used the Conceptual Dependency theory for mapping natural language descriptions to conceptual dependency diagrams. We have developed algorithms to convert these conceptual dependency diagrams into unit conceptual dependency tables, which are then merged to represent the whole context of the application. We also show how transactional requirements can be incorporated into the unit conceptual dependency table, and subsequently convert the unit conceptual dependency table into a corresponding conceptual model. We have developed an augmented transition network (ATN) parser to develop conceptual dependency diagrams from natural language descriptions. A prototype system has been implemented using Oracle8i and developer platforms.
In this paper, we share our experience in modeling and representing design knowledge relevant for engineering design decisions. We define an object model where classes are used to capture design standards and requirements relevant to designed objects. The traditional object model is customized to the representation of design knowledge in two major ways: (1) Classes representing design objects are augmented with design validation information. (2) Associations between classes are made explicit and used to reduce the redundancy and maintain the consistency of the knowledge. We define the semantics of the resulting object model and formulate the axioms that define its consistency. The object model is defined in the context of stamping design knowledge.
The use of event–condition–action (ECA) rules has transformed database systems from passive query-based data repositories to active sources of information delivery. In a similar fashion, ECA rules can be used to benefit workflow systems. In this paper, a software framework known as STEP workflow management facility is proposed in order to manage collaborative and distributed workflows and to provide interfaces to object management group-compliant product data management systems. Issues related to implementation using open standards such as CORBA are discussed. A key point underlying the framework is the flexibility it affords to users to re-configure the system according to evolving needs in collaborative product development.
This paper presents an object model for maintenance management of roofing systems as a case study to demonstrate the applicability of a proposed generic framework for integrating the maintenance management of built-assets. The framework consists of five sequential management processes: (1) Identify Asset, (2) Identify Performance Requirements, (3) Assess Performance, (4) Plan Maintenance, (5) Manage Maintenance Operations. The model builds upon the Industry Foundation Classes (IFCs) (Releases 2.0 and 2X) to define object requirements and relationships for the exchange and sharing of maintenance information between applications. Maintenance management is one of the defined projects within the facilities management (FM) domain committee of the International Alliance for Interoperability (IAI). The paper proposes several extensions to the IFC's including the representation of functional requirements, assessed condition of objects, inspection and maintenance tasks, and libraries of non-specific information. Usage scenarios are provided to illustrate the use of the model to carry out selected processes.
The development and maintenance of product configuration systems is faced with increasing challenges caused by the growing complexity of the underlying knowledge bases. Effective knowledge acquisition is needed since the product and the corresponding configuration system have to be developed in parallel. In this paper, we show how to employ a standard design language (Unified Modeling Language, UML) for modeling configuration knowledge bases. The two constituent parts of the configuration model are the component model and a set of corresponding functional architectures defining which requirements can be imposed on the product. The conceptual configuration model is automatically translated into an executable logic representation. Using this representation we show how to employ model-based diagnosis techniques for debugging faulty configuration knowledge bases, detecting infeasible requirements, and for reconfiguring old configurations.
In this paper, we shall be dealing with the problem of space layout planning. We present an approach based on an intermediate topological level with a dynamic space ordering (dso) heuristic. Our software ARCHiPLAN proceeds through a number of steps. First all the topologically different solutions, without presuming any precise dimension, are enumerated. Next, we may evolve in this topological solution space, and than refine some of them to form consistent geometrical solutions. For each topological solution chosen, the optimising geometrical solution is determined from a cost, useful surface or wall length. By using a dynamic space ordering heuristic in the topological level the enumeration time has been reduced.
With the growing popularity of component-based design it is important to have a look at, or perhaps revisit, some of the issues in component-assembly modelling, some of which are not dealt with at all or else not resolved. This paper presents a number of conceptual issues and their possible solutions.
In this paper, a new time-series predication method is proposed based on pattern analysis. In this method, basic patterns and their probabilities are extracted from a time series. A probabilistic relaxation method is employed to classify the probability vectors of the basic patterns. In order to verify the effectiveness of the proposed method, several experiments are carried out on a simulation signal and real data. The results show that the proposed method has advantages over existing methods in some applications.
Concern for environmental issues has increased in recent years. Waste production influences humanity's future. The alga, an ubiquitous single-celled plant, can thrive on industrial waste, to the detriment of water clarity and human activities. To avoid this, biologists need to isolate the chemical parameters of these rapid population fluctuations. This paper proposes a Fuzzy–Rough Estimator of Algae Populations (FuREAP), a hybrid system involving Fuzzy Set and Rough Set theories that estimates the size of algae populations given certain water characteristics. Through dimensionality reduction, FuREAP significantly reduces computer time and space requirements. Also, it decreases the cost of obtaining measurements and increases runtime efficiency, making the system more viable economically. By retaining only information required for the estimation task, FuREAP offers higher accuracy than conventional rule induction systems. Finally, FuREAP does not alter the domain semantics, making the distilled knowledge human-readable. The paper addresses the problem domain, architecture and modus operandi of FuREAP, and provides and discusses detailed experimental results.
Ueda et al. (2008) proposed the value creation model based on emergent synthesis. He also argues that whole value of an artifactual system should be co-created through the interaction among various agents in a society. This paper discusses how we could understand whole value of societal systems could be emerged through the interaction among various stakeholders who pursue different aspect of values. Moreover the paper addresses the relationships between different aspects of values including price, function, or satisfaction with an example of service system in which includes customers, employees and business entities.
Traffic along a freeway varies not only with time but also with space. It is thus essential to model dynamic traffic patterns on the freeway in order to derive appropriate metering control strategies. Existing methods cannot fulfill this task effectively. Due to the learning capability, artificial neural network models are developed to simulate typical time series traffic data and then expanded to capture the inherent time–space interrelations. The augmented-type network is proposed that includes several basic modules intelligently affiliated according to traffic characteristics on the freeway. Inputs to neural network models are traffic states in each time period on the freeway segments while outputs correspond to the desired metering rate at each entrance ramp. The simulation outcomes indicate very encouraging achievements when the proposed neural network model is employed to govern the freeway traffic operations. Also discussed are feasible directions for further improvements.
New approaches for the development of shop floor control systems are needed to introduce better response to unanticipated disturbance situations and a better handling of ‘reconfiguration’ in production environments. In this paper, a development approach for agent-based shop floor control systems is presented, that uses co-ordination concepts as observable in insect colonies. In this scheme, agents operate within an information distribution environment, where information is made available in the form of ‘artificial pheromones’. Pheromone concepts from insect colonies and their mapping into a control system architecture are presented, a test bed implementation is discussed.
During conceptual design, a designer may wish to describe a shape vaguely, either because it is desired that the shape remains flexible or the shape has not yet been defined precisely. Maintaining the vagueness of an early idea until it is sufficiently developed is often of vital importance. However, most existing systems, mainly due to limitations in their modelling capabilities, attempt to interpret and thereby remove the vagueness at the earliest opportunity. This may lead to the loss of considerable information in original concepts or design fixation too early in the design stage. A new approach is needed to represent and maintain vague geometric information and such an approach is presented in this paper.
We illustrate here how software engineers developing engineering design systems can introduce patterns into the conceptual modeling techniques that were developed in the database community and integrate them with techniques that are emerging in the object-oriented analysis and engineering design community. The goal is to raise the level of abstraction used to communicate software specifications and to build applications. This will speed the development and improve the quality of engineering design tools. We show by an example how this can be accomplished through an example software pattern from the software engineering discipline (the observer pattern) [12] . We show how patterns can be automatically supported using the general techniques that were developed in the Semantic Objects, Relationships, and Constraints (SORAC) project [20] for the development of tools, for the specification of databases and for building design systems.
Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance-based reinforcement-learning scheme, is used for the sensory-motor control of an eight-legged mobile robot and for the synthesis of the robot gaits. The robot does not have a priori knowledge of the environment and its own internal model. It is only assumed that the robot can acquire stable gaits by learning how to reach a goal area. During the learning process the control system is self-organized by reinforcement signals. Reaching the goal area defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. As learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns. Feasibility of the proposed self-organized system is tested under simulation and experiment. A minimal simulation model that does not require sophisticated computational schemes is constructed and used in simulations. The simulation data, evolved on the minimal model of the robot, is downloaded to the control system of the real robot. Overall, of 10 simulation data seven are successful in running the real robot.
This research paper discusses the core object model for architectural design, developed in the context of the IDEA+project. This project aims at developing an Integrated Design Environment for Architect designers, in which design tools and computational tests are gathered around and make use of a core object model. The object-oriented analysis method MERODE is used to develop this model. Due to the method's model-driven development, conceptual modelling is subdivided in an enterprise-modelling phase and a functionality-modelling phase. This structured approach has proven to be a firm base to the development of the envisaged model and enhances the model's integration in the design environment.
The capabilities of the two computational intelligence technologies including neural network and fuzzy logic can be synergized through the formation of an integrated and unified model which capitalizes on the benefits and concurrently offsets the flaws of the involved technologies. In this paper, a neural-fuzzy model, which is characterized by its ability to suggest the appropriate change of process parameters in a relatively complex parameter-based control situation involving multiple parameters, is presented. This model is particularly useful in multiple input and multiple output situations where complex mathematical calculations are required if conventional control approach is adopted. In particular, it serves to acquire knowledge from the information base for extracting rules, which are then fuzzified based on fuzzy principle. To validate the feasibility of this approach, a test has been conducted based on the neural-fuzzy model with the objective to achieve heat transfer enhancement in rectangular ducts using transverse ribs. This paper describes the roadmap for the deployment of this hybrid model to enhance machine intelligence of a complex system with the description of a case study to exemplify its underlying principles.
