In this paper we lay the foundations for exchanging, adapting, and interoperating engineering analysis models (EAMs). Our primary foundation is based upon the concept that engineering analysis models are knowledge-based abstractions of physical systems, and therefore knowledge sharing is the key to exchanging, adapting, and interoperating EAMs within or across organizations. To enable robust knowledge sharing, we propose a formal set of ontologies for classifying analysis modeling knowledge. To this end, the fundamental concepts that form the basis of all engineering analysis models are identified, described, and typed for implementation into a computational environment. This generic engineering analysis modeling ontology is extended to include distinct analysis subclasses. We discuss extension of the generic engineering analysis modeling class for two common analysis subclasses: continuum-based finite element models and lumped parameter or discrete analysis models. To illustrate how formal ontologies of engineering analysis modeling knowledge might facilitate knowledge exchange and improve reuse, adaptability, and interoperability of analysis models, we have developed a prototype engineering analysis modeling knowledge base, called ON-TEAM, based on our proposed ontologies. An industrial application is used to instantiate the ON-TEAM knowledge base and illustrate how such a system might improve the ability of organizations to efficiently exchange, adapt, and interoperate analysis models within a computer-based engineering environment. We have chosen Java as our implementation language for ON-TEAM so that we can fully exploit object-oriented technology, such as object inspection and the use of metaclasses and metaobjects, to operate on the knowledge base to perform a variety of tasks, such as knowledge inspection, editing, maintenance, model diagnosis, customized report generation of analysis models, model selection, automated customization of the knowledge interface based on the user expertise level, and interoperability assessment of distinct analysis models.

The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.

This study presents an application to optimize the use of an L-cut guillotine machine. The application has two distinct parts to it; first, a number of rectangular shapes are placed on as few metal sheets as possible by using genetic algorithms. Second, the sequence for cutting these pieces has to be generated. The guillotine's numeric control then uses this sequence to make the cuts.

Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.

Neuroevolution, or evolving neural networks with evolution algorithms such as genetic algorithms, is becoming one of the hottest areas in hybrid systems research. One of the areas that become under research using neuroevolutions is the controllers. In this paper, we shall present two engineering controllers based on neuroevolutions techniques. One of the controllers is used to monitor the temperature and humidity in an industry. This controller is having a linear behavior. The second controller is concerned with scheduling parts in queues in an industry. The scheduling controller is having a nonlinear behavior. The results obtained by the proposed controllers based on neuroevolution are compared with results obtained by traditional methods such as neural networks with backpropagation and ordinary simulation for the controller. The results show that the neuroevolution approaches outperform the results obtained by other methods.