Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed-model ASP and ALB using Multi-objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed-model ASP and ALB is modeled using task-based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed-model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed-model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed-model assembly problem.

The shape grammar formalism has been discussed theoretically extensively. Recently there has been increased activity in implementing shape grammar interpreters, yet there is a lack of implementations that support parametric rules and emergence. Here the structure of a general parametric shape grammar interpreter is discussed in detail. The interpreter is based on graph grammars. It supports emergence, parametric rules, and numerous types of geometric objects. The shape grammar engine, an agent-based rule selection system and several implementations based on the engine are discussed.

The evolution of computer aided design (CAD) systems and related technologies has promoted the development of software for the automatic configuration of mechanical systems. This occurred with the introduction of knowledge aided engineering (KAE) systems that enable computers to support the designer during the decision-making process. This paper presents a knowledge-based application that allows the designer to automatically compute and evaluate mass properties of racing cars. The system is constituted by two main components: the computing core, which determines the car model, and the graphic user interface, because of which the system may be used also by nonprogrammers. The computing core creates the model of the car based on a tree structure, which contains all car subsystems (e.g., suspension and chassis). Different part–subpart relationships define the tree model and link an object (e.g., suspension) to its components (e.g., wishbones and wheel). The definition of independent parameters (including design variables) and relationships definition allows the model to configure itself by evaluating all properties related to dimension, position, mass, etc. The graphic user interface allows the end user to interact with the car model by editing independent design parameters. It visualizes the main outputs of the model, which consist in numeric data (mass, center of mass of both the car and its subsystems) and graphic elements (car and subsystems 3D representation).

This paper presents a reference framework for the configuration process. The reference framework is established through an extensive review of existing literature, and as such consolidates an extensive theoretical base. The review of literature shows a broadening of the understanding of the configuration task. The definition of the configuration task is somewhat ambiguous because different research groups define configuration tasks differently. This paper proposes a reference framework for configuration that permits a more precise understanding of a configuration task, a definition of the basic concepts in product configuration, and a total configuration system view that describes how operators come together to perform the configuration task in the configuration process. We will define the product, the product model, the configuration task, and the configuration system, and put the whole thing into perspective with the theory of technical systems, where we describe the configuration process and the different abstraction level of configurations. We will also use our resulting framework to describe sales configuration, technical configuration, and reconfiguration. We do this to synthesize previous work, to clarify and make coherent definitions of relevant terms, to extent the definition of product configuration to include “softer” products like information and service, and finally, to give a comparative framework to analyze work done in the field of product configuration. The total configuration system, together with the definition of key concepts, comprises a strong reference framework when working with, developing, and analyzing configuration systems.

Case-based reasoning (CBR) is a promising methodology for solving many complex engineering design problems. CBR employs past problem-solving experiences when solving new problems. This paper presents a case study of how to apply CBR to a specific engineering problem: mechanical bearing design. A system is developed that retrieves previous design cases from a case repository and uses adaptation techniques to modify them to satisfy the current problem requirements. The approach combines both parametric and constraint satisfaction adaptations. Parametric adaptation considers not only parameter substitution but also the interrelationships between the problem definition and its solution. Constraint satisfaction provides a method to globally check the design requirements to assess case adaptability. Currently, our system has been implemented and tested in the domain of rolling bearings. This work serves as a template for application of CBR techniques to realistic engineering problems.

Detailed design is often a time-consuming and experience-dependent engineering process, where various detailed design knowledge can be reused. This paper proposes a formal approach for modeling detailed design knowledge for effective reuse. An extended structure–behavior–function model is developed for representing the structural, behavioral, and functional information in various life cycle periods of a detailed design. Based on the extended structure–behavior–function model, an issue- and solution-based approach is then developed to model the detailed knowledge of a mechanical design. The proposed approach is implemented in a detailed design knowledge modeling system, with a fixture design knowledge modeling as a brief example.

Design involves reasoning about descriptions of design artifacts, reasoning about design requirements, and reasoning about design process objectives (such as keeping to deadlines and available budget). Reasoning about these three aspects occurs during exploration, generation, and evaluation of partial design descriptions. Design space exploration involves exploration in all three related spaces: the space of partial descriptions of design artifacts, the space of design requirements, and the space of design process objectives. These spaces are vast. Explicit representation of the relations between elements in these three spaces provides the additional information needed to understand and reuse descriptions of partial design process traces, and to guide design exploration. In their Keynote Article, Woodbury and Burrow describe one of these spaces, namely, the space of design object descriptions, as a network of partial and intentional descriptions of design artifacts. The links between partial descriptions represent paths in design processes. Making the information compiled in these paths of exploration explicit, as proposed in this paper, extends the approach described by Woodbury and Burrow, increasing options for accessibility.

Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

Integrating multiple engineering perspectives is critical to designing ever more complex products, but this introduces great potential for miscommunication leading to design conflicts. The SHADE (SHAred Dependency Engineering) project is defining agent infrastructure technology that supports dynamic, knowledge-based communication among heterogeneous engineering tools, collaboration systems, and conflict management systems. Building on technologies for defining a shared formal vocabulary and protocols for exchanging information, SHADE is developing facilitators that assist in locating and disseminating information. The result is a flexible infrastructure that helps existing engineering tools work together more effectively, and that supports a variety of new conflict management approaches. This article outlines the facilitation and application agents created by SHADE, and provides an in-depth example of their application to an engineering task.

Computational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogical inspiration in a user-driven manner. To achieve this, we perform hierarchical Nonnegative Matrix Factorization to generate a clustered structure of patent data and employ D3.js to visualize the patent structure in a node-link network, in which user interaction capabilities are enabled for data exploration. In this study, we also analyze the effect of data size (ranging from 100 to 3000 patents) on two performance aspects of VISION – the clustering quality of topic modeling results and the frame rate of interactive data visualization. The findings show that the tool exhibits more randomized and inconsistent topic modeling results when the database size is too small. But, increasing the database size lowers the frame rate to the point that it could diminish designers’ ability to retrieve and recall information. The scope of the work here is to present the creation of the DbA visualization tool called VISION and to evaluate its data scale limitations in order to provide a basis for developing a visual interaction tool for the analogical retrieval process during DbA.

The competitive market forces companies to offer tailored products to meet specific customer needs. To avoid wasting time, design efforts generally address the configuration of existing solutions, without producing substantial design modifications. Configuration tools are used to achieve customized products starting from a common platform. Many approaches have been successfully proposed in literature to configure products. However, in the mechanical field they need further investigation in order to be efficiently linked to computer-aided design technologies. Research is focused on tools and methods to automatically produce geometrical models and improve the flexibility of the continuous product updating process. In this context, this paper aims to combine product configuration approaches with design automation techniques in order to support design activities of products to fulfill specific requirements. The approach is based on entities called configurable virtual prototypes. Three different domains are managed and connected via configurable virtual prototypes: product specifications, geometrical data, and product knowledge. In particular, geometry recognition rules are used to identify the parameterization of parts and the assembly mating constraints. The approach is exemplified through an industrial case study where a tool has been developed on the basis of the described method. Advantages of the system are shown in terms of achieved product configuration efficiency.

Most past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

In intelligent computer-aided design the concept of intelligence is related to that of integration. Using feature-based computer-aided design models is thought to make a complete integration. This paper presents a feature recognition approach based on the use of a feature grammar. Given the complexity of feature recognition in interactions, the basic idea of the approach is to find the latent and logical structure of features in interaction. The approach includes five main phases. The first phase, called regioning, identifies the potential zones for the birth of features. The second phase, called virtual extension, builds links and virtual faces. The third phase, called structuring, transforms the region into a structure compatible with the structure of the features represented by the feature grammar. The fourth phase, called Identification, identifies the features in these zones. The fifth phase, called modeling, represents the model by features. The feature modeling system software is developed based on this approach.

The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

This paper introduces an architecture for aggregation of output from different diagnostic tools. The diagnostic fusion tool deals with conflict resolution, where diagnostic tools disagree; temporal information discord, where the estimate of different tools is separated in time; differences in information updates, where the classifiers are updated at different rates; fault coverage discrepancies; and integration of a priori performance specifications. To this end, a hierarchical weight manipulation approach is introduced which creates and successively refines a fused output. The performance of the fusion tool is evaluated throughout its design. This allows impact assessment of adding heuristics and enables early tuning of parameters. Results obtained from diagnosing on-board faults from aircraft engines are shown which demonstrate the fusion tool's operation.

The method known as the analysis of interconnected decision areas (AIDA) has been in use for nearly 40 years, but has made little headway into engineering design. This paper describes an implementation of AIDA that is useful to engineering designers wishing to combine the solution principles of various subfunctions within a product in new ways. Traditionally, the method is used to understand how one decision affects the options available to other decisions in a large-scale project. The method is to be used interactively with designers participating in a brainstorming session so that ideas are added to AIDA and immediately combined with other compatible ideas. The existing implementation has been tested in a classroom setting in which upper level undergraduates have successfully used the AIDA method along with numerous of design methods to solve conceptual design problems.

Adaptability can have many different definitions: reliability, robustness, survivability, and changeability (adaptability to requirements change). In this research, we focused entirely on the last type. We discuss two alternative approaches to requirements change adaptability. One is the valuation approach that is based on utility and cost of design changes in response to modified requirements. The valuation approach is theoretically sound because it is based on utility and decision theory, but it may be difficult to use in the real world. The second approach is based on examining product architecture characteristics that facilitate changes that include modularity, hierarchy, interfaces, performance sensitivity, and design margins. This approach is heuristic in nature but more practical to use. If calibrated, it could serve as a surrogate for real adaptability. These measures were incorporated in a software tool for exploring alternative configurations of fractionated space satellite systems.

Adaptation of design cases is usually the most challenging part in building any case-based reasoning design system. The success of the adaptation process in finding a solution for a new design problem determines the success of the entire case-based reasoning (CBR) system. The techniques used for generating design solutions have many common aspects among the various engineering design classes that make them amenable to be captured in a generic framework for an acceptable level of abstraction. This paper proposes a design-plan-oriented methodology for adapting design cases to produce a solution to a new design problem in the domain of engineering design. The proposed methodology uses multicase adaptation and case built-in adaptation knowledge to produce a design plan for a new design problem. We first define the model of case representation to work with the proposed methodology. We then define the overall structure of the procedural framework of this methodology and its subprocesses. The system is then demonstrated through an application from the structural engineering domain.

This paper presents a novel formulation of the configuration-design problem that achieves the benefits of the concurrent engineering (CE) design paradigm. In CE, all design concerns (manufacturability, testability, etc.) are applied to an evolving design throughout the design cycle. CE identifies conflicts early on, avoids costly redesign, and leads to better products. Our formulation is based on a distributed dynamic interval constraint-satisfaction problem (DDICSP) model. Persistent catalog agents map onto DDICSP variables and constraint agents map onto DDICSP constraints. These agents use a set of operations and heuristics to navigate the design space to eliminate sets of designs until a solution is found. Experimental results show that an architecture where each catalog agent resides on a separate computer has performance advantages over nondistributed approaches.

The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.