Analysis of assembly properties of a product is needed during the initial design stage in order to identify potential assembly problems, which affect product performance in the later stages of life cycle. Assemblability analysis and evaluation play a key role in assembly design, assembly operation analysis and assembly planning. This paper develops a novel approach to assemblability and assembly sequence analysis and evaluation using the concept of the fuzzy set theory and neuro-fuzzy integration. Assemblability is described by assembly-operation difficulty, which can be represented by a fuzzy number between 0 and 1. Assemblability evaluation is therefore fuzzy evaluation of assembly difficulty. The evaluation structure covers not only the assembly parts' geometric and physical characteristics, but also takes into account the assembly operation data necessary to assemble the parts. The weight of each assemblability factor is subject to change to match the real assembly environments based on expert advice. The approach has the flexibility to be used in various assembly methods and different environments. It can be used in a knowledge-based design for assembly expert system with learning ability. Through integration with the CAD system, the developed system can effectively incorporate the concurrent engineering knowledge into the preliminary design process so as to provide users with suggestions for improving a design and also helping to obtain better design ideas. The applications in assembly design and planning show that the proposed approach and system are feasible.

Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.

This issue of AIEDAM focuses on AI in equipment service. Recently there has been a strong and renewed emphasis on AI technologies that can be used to monitor products and processes, detect incipient failures, identify possible faults (in various stages of development), determine preventive or corrective action, and generate a cost-efficient repair plan and monitor its execution. This renewed emphasis stems from a focus of manufacturing companies on the service market where they hope to grow their market share by offering their customers novel and aggressive service contracts. This service market includes power generation equipment, aircraft engines, medical imaging systems, and locomotives, just to name a few. In some of these new service offerings, the old parts-and-labor billing model is replaced by guaranteed uptime. This in turn places the motivation to maintain equipment in working order on the servicing company. Monitoring can be more efficiently accomplished, in part, by employing remotely monitored systems. Big strides have been taken for in-use monitoring of stationary equipment, such as manufacturing plants or high-end appliances, and also mobile systems such as transportation systems (vehicles, aircraft, locomotives, etc.). While advances in hardware development make it possible to perform these tasks efficiently, there are new avenues for progress in accompanying AI software techniques. Some of these approaches have their roots in efforts of years past while others arise from new challenges. Characteristics of typical challenges for AI in monitoring and diagnosis (M&D) service can be categorized into input, model, and output. In particular, input questions try

Topology deals with geometric properties which are dependent only upon the relative positions of the components of figures and not upon such concepts as length, size, and magnitude. Topology supports design and representation of mechanical devices, communication and transportation networks, topographic maps, and planning and controlling of complex activities. In addition, aspects of topology are closely related to symbolic logic, which forms the foundation of artificial intelligence. By approaching engineering design from this abstract point of view, it is possible to use topological methods to study collections of geometric objects or collections of entities that are of concern in design analysis or synthesis. The importance of topological representation and reasoning in analysis, design, and manufacturing is heightened by the contemporary view that stresses the need for conceptual design.

Topology deals with geometric properties that are dependent only on the relative positions of the components of figures and not on such concepts as length, size, and magnitude. Topology supports design and representation of mechanical devices, communication and transportation networks, topographic maps, and planning and controlling of complex activities. In addition, aspects of topology are closely related to symbolic logic, which forms the foundation of artificial intelligence. By approaching engineering design from this abstract point of view, it is possible to use topological methods to study collections of geometric objects or collections of entities that are of concern in design analysis or synthesis. The importance of topological representation and reasoning in analysis, design, and manufacturing is heightened by the contemporary view that stresses the need for conceptual design.

The efficient use of critical machines or equipment in a manufacturing system requires reliable information about their current operating conditions. This information is often used as a basis for machine condition monitoring and fault diagnosis—which essentially is an endeavor of knowledge extraction. Rough set theory provides a novel way to knowledge acquisition, especially when dealing with vagueness and uncertainty. It focuses on the discovery of patterns in incomplete and/or inconsistent data. However, rough set theory requires the data analyzed to be in discrete manner. This paper proposes a novel approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis using rough set theory. Based on the proposed approach, a prototype system called RClass-Plus has been developed. RClass-Plus is validated using a case study on mechanical fault diagnosis. Details of the validation are described.

We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

This article addresses computational synthesis systems that attempt to find a structural description that matches a set of initial functional requirements and design constraints with a finite sequence of production rules. It has been previously shown by the author that it is computationally difficult to identify a sequence of production rules that can lead to a satisficing design solution. As a result, computational synthesis, particularly with large volumes of selection information, requires effective design search procedures. Many computational synthesis systems utilize transformational search strategies. However, such search strategies are inefficient due to the combinatorial nature of the problem. In this article, the problem is approached using a completely different paradigm. The new approach encodes a design search problem as a Boolean (propositional) satisfiability problem, such that from every satisfying Boolean-valued truth assignment to the corresponding Boolean expression we efficiently can derive a solution to the original synthesis problem (along with its finite sequence of production rules). A major advantage of the proposed approach is the possibility of utilizing recently developed powerful randomized search algorithms for solving Boolean satisfiability problems, which considerably outperform the most widely used satisfiability algorithms. The new design-as-satisfiability technique provides a flexible framework for stating a variety of design constraints, and also represents properly the theory behind modern constraint-based design systems.

For typical optimization problems, the design space of interest is well defined: It is a subset of Rn, where n is the number of (continuous) variables. Constraints are often introduced to eliminate infeasible regions of this space from consideration. Many engineering design problems can be formulated as search in such a design space. For configuration design problems, however, the design space is much more difficult to define precisely, particularly when constraints are present. Configuration design spaces are discrete and combinatorial in nature, but not necessarily purely combinatorial, as certain combinations represent infeasible designs. One of our primary design objectives is to drastically reduce the effort to explore large combinatorial design spaces. We believe it is imperative to develop methods for mathematically defining design spaces for configuration design. The purpose of this paper is to outline our approach to defining configuration design spaces for engineering design, with an emphasis on the mathematics of the spaces and their combinations into larger spaces that more completely capture design requirements. Specifically, we introduce design spaces that model physical connectivity, functionality, and assemblability considerations for a representative product family, a class of coffeemakers. Then, we show how these spaces can be combined into a “common” product variety design space. We demonstrate how constraints can be defined and applied to these spaces so that feasible design regions can be directly modeled. Additionally, we explore the topological and combinatorial properties of these spaces. The application of this design space modeling methodology is illustrated using the coffeemaker product family.

Collaborative engineering design involves various stakeholders with different perspectives. The design process is relatively complex and difficult to handle. Various conflicts always happen among the design tasks and affect the design team performance. Therefore, to represent the collaborative design process and capture the evolution of design perspectives in a structured way, it is critical to manage the design conflicts and improve the collaborative design productivity. This article provides a generic collaborative design process model based on a sociotechnical design framework. This model has a topological format and adopts process analysis techniques from Petri Nets. By addressing both the technical and social aspects of collaborative design activities, it provides a mechanism to identify the interdependencies among design tasks and perspectives of different stakeholders. Based on this design process model, a methodology of detecting and handling the design conflicts is developed to support collaborative design coordination.

The current paper describes the Multidisciplinary Combinatorial Approach (MCA), the idea of which is to develop discrete mathematical representations, called “Combinatorial Representations” (CR) and to represent with them various engineering systems. During the research, the properties and methods embedded in each representation and the connections between them were investigated thoroughly, after which they were associated with various engineering systems to solve related engineering problems. The CR developed up until now are based on graph theory, matroid theory, and discrete linear programming, whereas the current paper employs only the first two. The approach opens up new ways of working with representations, reasoning and design, some of which are reported in the paper, as follows: 1) Integrated multidisciplinary representation—systems which contain interrelating elements from different disciplines are represented by the same CR. Consequently, a uniform analysis process is performed on the representation, and thus on the whole system, irrespective of the specific disciplines, to which the elements belong. 2) Deriving known methods and theorems—new proofs to known methods and theorems are derived in a new way, this time on the basis of the combinatorial theorems embedded in the CR. This enables development of a meta-representation for engineering as a whole, through which the engineering reasoning becomes convenient. In the current paper, this issue is illustrated on structural analysis. 3) Deriving novel connections between remote fields—new connections are derived on the basis of the relations between the different combinatorial representations. An innovative connection between mechanisms and trusses, shown in the paper, has been derived on the basis of the mutual dualism between their corresponding CR. This new connection alone has opened several new avenues of research, since knowledge and algorithms from machine theory are now available for use in structural analysis and vice versa. Furthermore, it has opened opportunities for developing new design methods, in which, for instance, structures with special properties are developed on the basis of known mechanisms with special properties, as demonstrated in this paper. Conversely, one can use these techniques to develop special mechanisms from known trusses.

Product design and diagnosis are, today, worlds apart. Despite strong areas of overlap at the ontological level, traditional design process theory and practice does not recognize diagnosis as a part of the modeling process chain; neither do diagnosis knowledge engineering processes reference design modeling tasks as a source of knowledge acquisition. This paper presents the DAEDALUS knowledge engineering framework as a methodology for integrating design and diagnosis tasks, models, and modeling environments around a common Domain Ontology and Product Models Library. The approach organizes domain knowledge around the execution of a set of tasks in an enterprise product engineering task workflow. Each task employs a Task Application which uses a customized subset of the Domain Ontology—the Task Ontology—to construct a graphical Product Model. The Ontology is used to populate the models with relevant concepts (variables) and relations (relationships), thus serving as a concept dictionary-style mechanism for knowledge sharing and reuse across the different Task Applications. For inferencing, each task employs a local Problem-solving Method (PSM), and a Model-PSM Mapping, which operate on the local Product Model to produce reasoning outcomes. The use of a common Domain Ontology across tasks and models facilitates semantic consistency of variables and relations in constructing Bayesian networks for design and diagnosis.

The approach is motivated by inefficiencies encountered in cleanly exchanging and integrating design FMEA and diagnosis models. Demonstration software under development is intended to illustrate how the DAEDALUS framework can be applied to knowledge sharing and exchange between Bayesian network-based design FMEA and diagnosis modeling tasks. Anticipated limitations of the DAEDALUS methodology are discussed, as is its relationship to Tomiyama's Knowledge Intensive Engineering Framework (KIEF). DAEDALUS is grounded in formal knowledge engineering principles and methodologies established during the past decade. Finally, the framework is presented as one possible approach for improved integration of generalized design and diagnostic modeling and knowledge exchange.

When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

This paper presents a method for the automatic deduction of priority lists of input information as well as for the extraction of task relations from the available design knowledge. The method is based on multiple extensive searches of the design space and produces a final priority list for the instantiation of the primary design entities. If followed, this list ensures the generation of the most decisive design information at the very beginning of the design process. Additionally, the method can produce a priority list of design tasks, which represents the order of completion of these tasks. Finally, it offers a representation platform for tracking the evolution of the design process. First, a brief overview of the current literature is presented, after which the method is presented in detail. Entities, descriptors, and tasks are used for the representation of the design knowledge. They are linked in order to form multiple design relations, formally represented by digraphs. Simple set relations and graph theories are used as mathematical background to the method. Design experience is also taken into account through a weighting process of the primary design entities. Finally, an example of a belt conveyor design is presented, followed by a discussion of the results and some general conclusions. The method may be considered as a design-assisting tool that dynamically processes pieces of design knowledge and suggests corresponding design paths. Additionally, it relates the design tasks in a ordered form. Its extension—currently under elaboration—is expected to treat systematically the problem of identifying and handling the design knowledge inconsistencies.

When the idea of software reuse appeared in 1968, new horizons for software design were open. But some major problems appeared and most of the expectations were not met. One of the problems encountered is the selection of the right software component. This is related not only to the similarity between the desired functionality and the function delivered by the retrieved software component, but also to the effort needed to modify the chosen component to accommodate the desired functionality. Most of the research done in the case-based reasoning area has been in developing accurate and efficient retrieval algorithms. We think that case-based reasoning retrieval concepts and ideas can be successfully applied to software reuse. In this article we propose a metric to assess similarity between software cases supported on functional and behavioral knowledge. One important aspect of this metric is that reusability is taken into account to estimate the amount of effort needed to reuse retrieved software cases. We also present experimental work that shows that similarity at the functional level is the most important aspect of the similarity metric proposed.

A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.

Product configuration is the process of generating a product variant from a previously defined product family model and additional product specifications for this variant. The process of finding and sequencing the relevant operations for manufacturing this product is called process planning. This article combines the two principles in a new concept of process configuration that solves the process planning task using product configuration methods. The second section develops characteristics for two process configuration concepts, the interactive process configuration and the automation-based process configuration. Following an overview of the implementation of a process configuration system, the results of a case study in the aluminum rolling industry are presented. The main benefits of the process configuration concept are observed in a reduced knowledge-maintenance effort and in increased problem-solving speed.

The problem of finding optimal values in complex parameter optimization problems has often been solved with success by evolutionary algorithms (EAs). In many cases, these algorithms are employed as black-box methods over imprecisely known domains. Such problems arise frequently in engineering design. The principal barrier to the general use of EAs for those problems is the huge number of function evaluations that is often required. This makes EAs an impractical approach when the function evaluation depends on numerically heavy design analysis tools, for example, finite elements methods. This paper presents the use of kriging interpolation as a function approximation method for the construction of an internal model of the fitness landscape. This model is intended to guide the search process with a reduced number of fitness function evaluations.

This paper presents a novel knowledge-based Petri net approach to mechanical systems and assemblies modeling within a design with objects environment. A new unified class of object-oriented knowledge Petri nets, which can incorporate a knowledge-based system with ordinary Petri nets, is defined and used for the unified representations of assembly design and modeling. The object knowledge Petri nets, as a graphical language and a new knowledge-based description scheme, can be used to express the qualitative and quantitative aspects of the assembly design and modeling process in an interactive and integrated way. The four-level hierarchy model is proposed and constructed in terms of function-behaviors, structures, geometries, and features. The function-behavior-structure description is built on more abstract concepts so that it can match well top-down design. The static and dynamic characteristics in the design of assembly can also be captured. With the help of fuzzy logic, the incomplete, imprecise knowledge and uncertainty in the design process can also be dealt with. Therefore, the hybrid design object model can incorporate product data model, top-down design process, and assembly process model using an object-oriented, knowledge-based, feature-based, parametric, and constraint-based modeling approach, and can provide a more accurate and more flexible representation. To verify and demonstrate the effective use of the proposed hybrid design object model, a prototype system has been developed. This research provides a knowledge-intensive framework for intelligent assembly design and modeling.

For every problem mentioned by crew members in an aircraft log book, an associated repair action note is entered in the same log book by a maintenance technician after the problem has been handled. These hand-written repair notes, subsequently transcribed into a database, give an account of the actions undertaken by the technicians to fix the problems. Written in a free-text format with peculiar linguistic characteristics, including many arbitrary abbreviations and missing auxiliaries, they contain valuable information that can be used for decision support methods such as case-based reasoning. We use natural language techniques in our information extraction system to analyze the structure and contents of these notes in order to determine the pieces of equipment involved in a repair and what was done to them. Lexical information and domain knowledge are extracted from an electronic version of the illustrated parts catalog for the particular airplane, and are used at different stages of the process, from the morpholexical analysis to the evaluation of the semantic expression generated by the syntactical analyzer.